Indra Prakash - Academia.edu (original) (raw)

Papers by Indra Prakash

Research paper thumbnail of Predicting Load-Deflection of Composite Concrete Bridges Using Machine Learning Models

Journal of Science and Transport Technology, Dec 28, 2023

The main objective of this study is to predict accurately the loaddeflection of composite concret... more The main objective of this study is to predict accurately the loaddeflection of composite concrete bridges using two popular machine learning (ML) models namely Random Tree (RT) and Artificial Neural Network (ANN). Data from 83 track loading tests conducted on various bridges in Vietnam were collected and analyzed. Various input parameters namely bridge's crosssectional shape, length of concrete beam, number of years in use, height of the main girder, distance between the main girders were selected for the modelling. Validation indicators like R, RMSE, and MAE, and Taylor diagram were used for validation and comparison of the models. Results of this study showed that both RT and ANN are good for prediction of the load-deflection of composite concrete bridges, but RT outperforms ANN. Thus, the developed ML models can facilitate efficient bridge health monitoring and management by predicting the load-deflection of simple-span concrete bridges.

Research paper thumbnail of Prediction of white spot disease susceptibility in shrimps using decision trees based machine learning models

Applied Water Science, Dec 8, 2023

Recently, the spread of white spot disease in shrimps has a major impact on the aquaculture activ... more Recently, the spread of white spot disease in shrimps has a major impact on the aquaculture activity worldwide affecting the economy of the countries, especially SouthEast Asian countries like Vietnam. This deadly disease in shrimps is caused by the White Spot Syndrome Virus (WSSV). Researchers are trying to understand the spread and control of this disease by doing field and laboratory studies considering effect of environmental conditions on shrimps affected by WSSV. Generally, they have not considered spatial factors in their study. Therefore, in the present study, we have used spatial (distances to roads and factories) as well as physio-chemical factors of water: Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Salinity, NO 3 , P 3 O 4 and pH, for developing WSSV susceptibility maps of the area using Decision Tree (DT)-based Machine Learning (ML) models namely Random Tree (RT), Extra Tree (ET), and J48. Model's performance was evaluated using standard statistical measures including Area Under the Curve (AUC). The results indicated that ET model has the highest accuracy (AUC: 0.713) in predicting disease susceptibility in comparison to other two models (RT: 0.701 and J48: 0.641). The WSSV susceptibility maps developed by the ML technique, using DT (ET) method, will help decision makers in better planning and control of spatial spread of WSSV disease in shrimps.

Research paper thumbnail of Forecasting Construction Price Index using Artificial Intelligence Models: Support Vector Machines and Radial Basis Function Neural Network

Journal of Science and Transport Technology

Estimation of Construction Price Index (CPI) is important for a market economy and it is a measur... more Estimation of Construction Price Index (CPI) is important for a market economy and it is a measure to manage construction investment costs. This is a tool to help organizations and individuals to reduce the effort and management of expenses for construction projects by reducing time of procedures for calculating and adjusting the total investment for the estimation and evaluation of contract price. The CPI is an indicator that reflects the level of construction price fluctuations of the type of work over time. In this study, the CPI data of Son La province, Vietnam from January 2016 to March 2022 (75 dataset) has been used for the modelling. Two Artificial Intelligence (AI) models namely Support Vector Machine (SVM) and Radial Basis Function Neural Network (RBFN) were proposed to predict the CPI based on limited input data. Performance of the models in correctly predicting CPI was evaluated using standard statistical indicators such as Coefficient of Determination (R2), Root Mean ...

Research paper thumbnail of Development of effective XGB model to predict the Axial Load Capacity of circular CFST columns

Journal of Science and Transport Technology

The Axial Load Capacity (ALC) of Concrete-Filled Steel Tubular (CFST) structural members is regar... more The Axial Load Capacity (ALC) of Concrete-Filled Steel Tubular (CFST) structural members is regarded as one of the most crucial technical factors for the design of these composite structures. This work proposes the development and application of the Extreme Gradient Boosting (XGB) model to forecast the ALC of circular CFST structural components using the affecting input parameters, namely column diameter, steel tube thickness, column length, steel yield strength, and concrete compressive strength. A dataset of 2073 experimental results from the literature was used for the model development. The performance of the XGB model was evaluated using statistical criteria such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R2), and Mean Absolute Percentage Error (MAPE). The five-fold cross-validation technique and Monte Carlo simulation method were used to evaluate the model's performance. The results show good performance of the XGB model (R2...

Research paper thumbnail of Development and application of hybrid artificial intelligence models for groundwater potential mapping and assessment

Vietnam Journal of Earth Sciences

Groundwater potential assessment is essential for optimum utilization and recharge of groundwater... more Groundwater potential assessment is essential for optimum utilization and recharge of groundwater resources for the proper development and management of an area. The main aim of this study is to develop an accurate groundwater potential map of the Dak Nong Province (Vietnam) using hybrid artificial intelligence models, which are a combination of Random Forest (RF) and its Ensemble Framework (AdaBoost - ABRF, Bagging - BRF and LogitBoost - LBRF). In this study, twelve conditioning factors, namely topography (aspect, elevation, Topographic Wetness Index - TWI, slope, and curvature), hydrology (infiltration and river density, rainfall, Sediment Transport Index - STI, Stream Power Index - SPI), land use, and soil were used to develop the models. Well, yield data was also utilized to develop and validate potential groundwater zones. One Rule (R) feature selection method was utilized to prioritize the importance of groundwater potential affecting parameters. The results indicated that the...

Research paper thumbnail of Evaluation of Shannon Entropy and Weights of Evidence Models in Landslide Susceptibility Mapping for the Pithoragarh District of Uttarakhand State, India

Advances in Civil Engineering

Landslide susceptibility mapping is considered a useful tool for planning, disaster management, a... more Landslide susceptibility mapping is considered a useful tool for planning, disaster management, and natural hazard mitigation of a region. Although there are different methods for predicting landslide susceptibility, the bivariate statistical analysis method is considered to be simple and popular. In this study, the main aim is to evaluate the performance of Shannon entropy (SE) and weights of evidence (WOE) statistical models in landslide susceptibility mapping of Pithoragarh district of Uttarakhand state, India. For this purpose, ten landslide affecting factors, namely, slope degree, aspect, curvature, elevation, land cover, slope forming materials, geomorphology (landforms), distance to rivers, distance to roads, and overburden depth were used for the development of landslide susceptibility maps using the SE and WOE methods. Data extracted from the Google Earth images, Aster Digital Elevation Model, and Geological Survey of India report were used for the construction and evaluati...

Research paper thumbnail of Hybridization of Differential Evolution and Adaptive-NetworkBased Fuzzy Inference System in Estimation of Compression Coefficient of Plastic Clay Soil

Computer Modeling in Engineering & Sciences, 2022

One of the important geotechnical parameters required for designing of the civil engineering stru... more One of the important geotechnical parameters required for designing of the civil engineering structure is the compressibility of the soil. In this study, the main purpose is to develop a novel hybrid Machine Learning (ML) model (ANFIS-DE), which used Differential Evolution (DE) algorithm to optimize the predictive capability of Adaptive-Network-based Fuzzy Inference System (ANFIS), for estimating soil Compression coefficient (Cc) from other geotechnical parameters namely Water Content, Void Ratio, Specific Gravity, Liquid Limit, Plastic Limit, Clay content and Depth of Soil Samples. Validation of the predictive capability of the novel model was carried out using statistical indices: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R). In addition, two popular ML models namely Reduced Error Pruning Trees (REPTree) and Decision Stump (Dstump) were used for comparison. Results showed that the performance of the novel model ANFIS-DE is the best (R = 0.825, MAE = 0.064 and RMSE = 0.094) in comparison to other models such as REPTree (R = 0.7802, MAE = 0.068 and RMSE = 0.0988) and Dstump (R = 0.7325, MAE = 0.0785 and RMSE = 0.1036). Therefore, the ANFIS-DE model can be used as a promising tool for the correct and quick estimation of the soil Cc, which can be employed in the design and construction of civil engineering structures.

Research paper thumbnail of A Comparative Study of Soft Computing Models for Prediction of Permeability Coefficient of Soil

Mathematical Problems in Engineering, 2021

Determination of the permeability coefficient (K) of soil is considered as one of the essential s... more Determination of the permeability coefficient (K) of soil is considered as one of the essential steps to assess infiltration, runoff, groundwater, and drainage in the design process of the construction projects. In this study, three cost-effective algorithms, namely, artificial neural network (ANN), support vector machine (SVM), and random forest (RF), which are well-known as advanced machine learning techniques, were used to predict the permeability coefficient (K) of soil (10−9 cm/s), based on a set of simple six input parameters such as natural water content w (%), void ratio (e), specific density (g/cm3), liquid limit (LL) (%), plastic limit (PL) (%), and clay content (%). For this, a total of 84 soil samples data collected from the detailed design stage investigations of Da Nang-Quang Ngai national road project in Vietnam was used to generate training (70%) and testing (30%) datasets for building and validating the models. Statistical error indicators such as RMSE and MAE and c...

Research paper thumbnail of GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, India

Mathematical Problems in Engineering, 2021

The main objective of the study was to investigate performance of three soft computing models: Na... more The main objective of the study was to investigate performance of three soft computing models: Naïve Bayes (NB), Multilayer Perceptron (MLP) neural network classifier, and Alternating Decision Tree (ADT) in landslide susceptibility mapping of Pithoragarh District of Uttarakhand State, India. For this purpose, data of 91 past landslide locations and ten landslide influencing factors, namely, slope degree, curvature, aspect, land cover, slope forming materials (SFM), elevation, distance to rivers, geomorphology, overburden depth, and distance to roads were considered in the models study. Thematic maps of the Geological Survey of India (GSI), Google Earth images, and Aster Digital Elevation Model (DEM) were used for the development of landslide susceptibility maps in the Geographic Information System (GIS) environment. Landslide locations data was divided into a 70 : 30 ratio for the training (70%) and testing/validation (30%) of the three models. Standard statistical measures, namely,...

Research paper thumbnail of Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil

Mathematical Problems in Engineering, 2021

The main objective of this study is to evaluate and compare the performance of different machine ... more The main objective of this study is to evaluate and compare the performance of different machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme Learning Machine (ELM), and Boosting Trees (Boosted) algorithms, considering the influence of various training to testing ratios in predicting the soil shear strength, one of the most critical geotechnical engineering properties in civil engineering design and construction. For this aim, a database of 538 soil samples collected from the Long Phu 1 power plant project, Vietnam, was utilized to generate the datasets for the modeling process. Different ratios (i.e., 10/90, 20/80, 30/70, 40/60, 50/50, 60/40, 70/30, 80/20, and 90/10) were used to divide the datasets into the training and testing datasets for the performance assessment of models. Popular statistical indicators, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R), were employed to evaluate the predictive cap...

Research paper thumbnail of GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam

Sustainability, 2019

Landslides affect properties and the lives of a large number of people in many hilly parts of Vie... more Landslides affect properties and the lives of a large number of people in many hilly parts of Vietnam and in the world. Damages caused by landslides can be reduced by understanding distribution, nature, mechanisms and causes of landslides with the help of model studies for better planning and risk management of the area. Development of landslide susceptibility maps is one of the main steps in landslide management. In this study, the main objective is to develop GIS based hybrid computational intelligence models to generate landslide susceptibility maps of the Da Lat province, which is one of the landslide prone regions of Vietnam. Novel hybrid models of alternating decision trees (ADT) with various ensemble methods, namely bagging, dagging, MultiBoostAB, and RealAdaBoost, were developed namely B-ADT, D-ADT, MBAB-ADT, RAB-ADT, respectively. Data of 72 past landslide events was used in conjunction with 11 landslide conditioning factors (curvature, distance from geological boundaries, ...

Research paper thumbnail of Groundwater potential zoning using Logistics Model Trees based novel ensemble machine learning model

Vietnam Journal of Earth sciences/Vietnam Journal of Earth Sciences, Mar 11, 2024

Research paper thumbnail of Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction

Symmetry, Jun 17, 2020

Predicting and mapping fire susceptibility is a top research priority in fire-prone forests world... more Predicting and mapping fire susceptibility is a top research priority in fire-prone forests worldwide. This study evaluates the abilities of the Bayes Network (BN), Naïve Bayes (NB), Decision Tree (DT), and Multivariate Logistic Regression (MLP) machine learning methods for the prediction and mapping fire susceptibility across the Pu Mat National Park, Nghe An Province, Vietnam. The modeling methodology was formulated based on processing the information from the 57 historical fires and a set of nine spatially explicit explanatory variables, namely elevation, slope degree, aspect, average annual temperate, drought index, river density, land cover, and distance from roads and residential areas. Using the area under the receiver operating characteristic curve (AUC) and seven other performance metrics, the models were validated in terms of their abilities to elucidate the general fire behaviors in the Pu Mat National Park and to predict future fires. Despite a few differences between the AUC values, the BN model with an AUC value of 0.96 was dominant over the other models in predicting future fires. The second best was the DT model (AUC = 0.94), followed by the NB (AUC = 0.939), and MLR (AUC = 0.937) models. Our robust analysis demonstrated that these models are sufficiently robust in response to the training and validation datasets change. Further, the results revealed that moderate to high levels of fire susceptibilities are associated with~19% of the Pu Mat National Park where human activities are numerous. This study and the resultant susceptibility

Research paper thumbnail of Comparison of HEC-HMS and SWAT Hydrological Models in Simulating Runoff at Machhu River Catchment, Gujarat, India

Springer eBooks, Nov 17, 2021

Research paper thumbnail of Estimation of Runoff and Soil Erosion for Vishwamitri River Watershed, Western India Using RS and GIS

Water science and engineering, Oct 15, 2015

A watershed is an area covering all the land that contributes water after rainfall occurs to a co... more A watershed is an area covering all the land that contributes water after rainfall occurs to a common point. Watershed management programme is mainly for conservation and development of natural resources. Remote Sensing and Geographic Information System (GIS) are emerging very powerful tools for analyzing spatial distributed information. In these study satellite images of IRS-P6 LISS-III images have been used. Heavy runoff and soil erosion are two severe problems of watershed development. In the present study, SCS Curve Number (CN) is used to estimate the runoff and USLE equations are used to measure the soil loss from the study watershed. The present study is carried out on Vishwamitri river watershed, Panchmahal& Vadodara districts of Gujarat State, India having an area of 1185 Sq.km. The geographical location of the area lies between 22 o 00' and 22 o 45' of north latitude and 73 o 00' and 73 o 45' of east longitude. The daily rainfall data of 5 rain gauge stations (1990-2013) was collected and used to predict the daily runoff from the watershed using SCS-CN method and GIS. The analysis shows that for the study period 1990-2013, minimum and maximum values of (a) yearly computed average rainfall are 336.28 mm and 2170.2 mm and (b) yearly computed average runoff are 49.49 mm and 800.19 mm respectively. All five parameters of USLE equation for soil loss viz. R, K, LS, C, and P were estimated. Watershed based analysis for erosion shows that two sub watersheds coded as SW1 & SW2 are experiencing very severe soil erosion conditions whereas remaining two sub watersheds coded as SW3 & SW4 are subjected to moderate soil erosion conditions. The average computed annual soil loss from study watersheds is 60.65 ton /ha/year.

Research paper thumbnail of Application and Comparison of Decision Tree-Based Machine Learning Methods in Landside Susceptibility Assessment at Pauri Garhwal Area, Uttarakhand, India

Environmental Processes, Jun 23, 2017

Landslide susceptibility assessment has been conducted at the Pauri Garhwal area of Uttarakhand s... more Landslide susceptibility assessment has been conducted at the Pauri Garhwal area of Uttarakhand state, India, an area affected by numerous landslides causing significant losses of life, infrastructure and property every year. Decision tree-based machine learning methods, namely Random Forest (RF), Logistic Model Trees (LMT), Best First Decision Trees (BFDT) and Classification and Regression Trees (CART) have been used, and results are compared herein for proper spatial prediction of landslides. Analysis of the data has been done considering sixteen conditioning factors (i.e., slope angle, elevation, slope aspect, profile curvature, land cover, curvature, lithology, plan curvature, soil, distance to lineaments, lineament density, distance to roads, road density, distance to river, river density and rainfall), and 1295 historical landslide polygons. Models were validated and compared using Receiver Operating Characteristics (ROC) curve and statistical indices. The results show that the RF model has the highest predictive capability, followed by the LMT, BFDT and CART models, respectively, and indicate that although all four methods have shown good results, the performance of the RF method is the best for landslide spatial prediction.

Research paper thumbnail of Development of artificial intelligence models for the prediction of Compression Coefficient of soil: An application of Monte Carlo sensitivity analysis

Science of The Total Environment, Aug 1, 2019

Research paper thumbnail of Landslide Susceptibility Assessment at a Part of Uttarakhand Himalaya, India using GIS – based Statistical Approach of Frequency Ratio Method

International journal of engineering research and technology, Nov 21, 2015

Frequency Ratio has successfully applied as statistical approach for landslide susceptibility ass... more Frequency Ratio has successfully applied as statistical approach for landslide susceptibility assessment in many regions over the world. In the present study, a part of Uttarakhand Himalaya has been selected as a case study to apply the FR model for landslide susceptibility assessment. For this, landslide inventory map was firstly constructed with 430 landslide locations identified from various sources with the help of GIS technology. These landslide locations were then randomly split into two parts (i) for training process (70% landslide locations) and (ii) for validation process (30% landslide locations). Presently, the total of six landslide conditioning factors (slope, aspect, elevation, curvature, land use, and rainfall) has been selected for analyzing the spatial relationship with landslide occurrences. Using training dataset, the FR model was then built to assess landslide susceptibility in the study area. Finally, success rate curve and predictive rate curve have been employed to validate the performance of the FR model. The results show that the FR model indicates fairly well in the present study. Overall, the FR model is an effective method for the landslide susceptibility assessment of hilly areas. It can be applied in other areas of Himalayas for the assessment and management of landslide hazards

Research paper thumbnail of Bagging based Support Vector Machines for spatial prediction of landslides

Environmental Earth Sciences, Feb 1, 2018

A hybrid Bagging based Support Vector Machines (BSVM) method, which is a combination of Bagging E... more A hybrid Bagging based Support Vector Machines (BSVM) method, which is a combination of Bagging Ensemble and Support Vector Machine (SVM) classifier, was proposed for the spatial prediction of landslides at the district of Mu Cang Chai, Viet Nam. In the present study, 248 past landslides and fifteen geo-environmental factors (curvature, elevation, distance to rivers, slope, aspect, river density, plan curvature, distance to faults, profile curvature, fault density, lithology, distance to roads, rainfall, land use, and road density) were considered for the model construction. Different evaluation criteria were applied to validate the proposed hybrid model such as statistical index-based methods and area under the receiver operating characteristic curve (AUC). The single SVM and the Naïve Bayes Trees (NBT) models were selected for comparison. Based on the AUC values, the proposed hybrid model BSVM (0.812) outperformed the SVM (0.804) and NBT (0.8) models. Thus, the BSVM is a promising and better method for landslide prediction.

Research paper thumbnail of Landslide Susceptibility Assessment Using Bagging Ensemble Based Alternating Decision Trees, Logistic Regression and J48 Decision Trees Methods: A Comparative Study

Geotechnical and Geological Engineering, May 22, 2017

In this study, we have evaluated and compared prediction capability of Bagging Ensemble Based Alt... more In this study, we have evaluated and compared prediction capability of Bagging Ensemble Based Alternating Decision Trees (BADT), Logistic Regression (LR), and J48 Decision Trees (J48DT) for landslide susceptibility mapping at part of the Uttarakhand State (India). The BADT method has been proposed in the present study which is a novel hybrid machine learning ensemble approach of bagging ensemble and alternating decision trees. The J48DT is a relative new machine learning technique which has been applied only in few landslide studies, and the LR is known as a popular landslide susceptibility model. For the model studies, a spatial database of 930 historical landslide events and 15 landslide affecting factors have been collected and analyzed. This database has been used to build and validate the landslide models namely BADT, LR and J48DT Predictive capability of these models has been validated and compared using statistical analyzing methods and Receiver Operating Characteristic (ROC) curve. Results show that these three landslide models (BADT, LR and J48DT) performed well with the training dataset. However, using the validation dataset the BADT model has the highest prediction capability, followed by the LR model, and the J48DT model, respectively. This indicates that the BADT is a promising method which can be used for landslide susceptibility assessment also for other landslide prone areas.

Research paper thumbnail of Predicting Load-Deflection of Composite Concrete Bridges Using Machine Learning Models

Journal of Science and Transport Technology, Dec 28, 2023

The main objective of this study is to predict accurately the loaddeflection of composite concret... more The main objective of this study is to predict accurately the loaddeflection of composite concrete bridges using two popular machine learning (ML) models namely Random Tree (RT) and Artificial Neural Network (ANN). Data from 83 track loading tests conducted on various bridges in Vietnam were collected and analyzed. Various input parameters namely bridge's crosssectional shape, length of concrete beam, number of years in use, height of the main girder, distance between the main girders were selected for the modelling. Validation indicators like R, RMSE, and MAE, and Taylor diagram were used for validation and comparison of the models. Results of this study showed that both RT and ANN are good for prediction of the load-deflection of composite concrete bridges, but RT outperforms ANN. Thus, the developed ML models can facilitate efficient bridge health monitoring and management by predicting the load-deflection of simple-span concrete bridges.

Research paper thumbnail of Prediction of white spot disease susceptibility in shrimps using decision trees based machine learning models

Applied Water Science, Dec 8, 2023

Recently, the spread of white spot disease in shrimps has a major impact on the aquaculture activ... more Recently, the spread of white spot disease in shrimps has a major impact on the aquaculture activity worldwide affecting the economy of the countries, especially SouthEast Asian countries like Vietnam. This deadly disease in shrimps is caused by the White Spot Syndrome Virus (WSSV). Researchers are trying to understand the spread and control of this disease by doing field and laboratory studies considering effect of environmental conditions on shrimps affected by WSSV. Generally, they have not considered spatial factors in their study. Therefore, in the present study, we have used spatial (distances to roads and factories) as well as physio-chemical factors of water: Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Salinity, NO 3 , P 3 O 4 and pH, for developing WSSV susceptibility maps of the area using Decision Tree (DT)-based Machine Learning (ML) models namely Random Tree (RT), Extra Tree (ET), and J48. Model's performance was evaluated using standard statistical measures including Area Under the Curve (AUC). The results indicated that ET model has the highest accuracy (AUC: 0.713) in predicting disease susceptibility in comparison to other two models (RT: 0.701 and J48: 0.641). The WSSV susceptibility maps developed by the ML technique, using DT (ET) method, will help decision makers in better planning and control of spatial spread of WSSV disease in shrimps.

Research paper thumbnail of Forecasting Construction Price Index using Artificial Intelligence Models: Support Vector Machines and Radial Basis Function Neural Network

Journal of Science and Transport Technology

Estimation of Construction Price Index (CPI) is important for a market economy and it is a measur... more Estimation of Construction Price Index (CPI) is important for a market economy and it is a measure to manage construction investment costs. This is a tool to help organizations and individuals to reduce the effort and management of expenses for construction projects by reducing time of procedures for calculating and adjusting the total investment for the estimation and evaluation of contract price. The CPI is an indicator that reflects the level of construction price fluctuations of the type of work over time. In this study, the CPI data of Son La province, Vietnam from January 2016 to March 2022 (75 dataset) has been used for the modelling. Two Artificial Intelligence (AI) models namely Support Vector Machine (SVM) and Radial Basis Function Neural Network (RBFN) were proposed to predict the CPI based on limited input data. Performance of the models in correctly predicting CPI was evaluated using standard statistical indicators such as Coefficient of Determination (R2), Root Mean ...

Research paper thumbnail of Development of effective XGB model to predict the Axial Load Capacity of circular CFST columns

Journal of Science and Transport Technology

The Axial Load Capacity (ALC) of Concrete-Filled Steel Tubular (CFST) structural members is regar... more The Axial Load Capacity (ALC) of Concrete-Filled Steel Tubular (CFST) structural members is regarded as one of the most crucial technical factors for the design of these composite structures. This work proposes the development and application of the Extreme Gradient Boosting (XGB) model to forecast the ALC of circular CFST structural components using the affecting input parameters, namely column diameter, steel tube thickness, column length, steel yield strength, and concrete compressive strength. A dataset of 2073 experimental results from the literature was used for the model development. The performance of the XGB model was evaluated using statistical criteria such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R2), and Mean Absolute Percentage Error (MAPE). The five-fold cross-validation technique and Monte Carlo simulation method were used to evaluate the model's performance. The results show good performance of the XGB model (R2...

Research paper thumbnail of Development and application of hybrid artificial intelligence models for groundwater potential mapping and assessment

Vietnam Journal of Earth Sciences

Groundwater potential assessment is essential for optimum utilization and recharge of groundwater... more Groundwater potential assessment is essential for optimum utilization and recharge of groundwater resources for the proper development and management of an area. The main aim of this study is to develop an accurate groundwater potential map of the Dak Nong Province (Vietnam) using hybrid artificial intelligence models, which are a combination of Random Forest (RF) and its Ensemble Framework (AdaBoost - ABRF, Bagging - BRF and LogitBoost - LBRF). In this study, twelve conditioning factors, namely topography (aspect, elevation, Topographic Wetness Index - TWI, slope, and curvature), hydrology (infiltration and river density, rainfall, Sediment Transport Index - STI, Stream Power Index - SPI), land use, and soil were used to develop the models. Well, yield data was also utilized to develop and validate potential groundwater zones. One Rule (R) feature selection method was utilized to prioritize the importance of groundwater potential affecting parameters. The results indicated that the...

Research paper thumbnail of Evaluation of Shannon Entropy and Weights of Evidence Models in Landslide Susceptibility Mapping for the Pithoragarh District of Uttarakhand State, India

Advances in Civil Engineering

Landslide susceptibility mapping is considered a useful tool for planning, disaster management, a... more Landslide susceptibility mapping is considered a useful tool for planning, disaster management, and natural hazard mitigation of a region. Although there are different methods for predicting landslide susceptibility, the bivariate statistical analysis method is considered to be simple and popular. In this study, the main aim is to evaluate the performance of Shannon entropy (SE) and weights of evidence (WOE) statistical models in landslide susceptibility mapping of Pithoragarh district of Uttarakhand state, India. For this purpose, ten landslide affecting factors, namely, slope degree, aspect, curvature, elevation, land cover, slope forming materials, geomorphology (landforms), distance to rivers, distance to roads, and overburden depth were used for the development of landslide susceptibility maps using the SE and WOE methods. Data extracted from the Google Earth images, Aster Digital Elevation Model, and Geological Survey of India report were used for the construction and evaluati...

Research paper thumbnail of Hybridization of Differential Evolution and Adaptive-NetworkBased Fuzzy Inference System in Estimation of Compression Coefficient of Plastic Clay Soil

Computer Modeling in Engineering & Sciences, 2022

One of the important geotechnical parameters required for designing of the civil engineering stru... more One of the important geotechnical parameters required for designing of the civil engineering structure is the compressibility of the soil. In this study, the main purpose is to develop a novel hybrid Machine Learning (ML) model (ANFIS-DE), which used Differential Evolution (DE) algorithm to optimize the predictive capability of Adaptive-Network-based Fuzzy Inference System (ANFIS), for estimating soil Compression coefficient (Cc) from other geotechnical parameters namely Water Content, Void Ratio, Specific Gravity, Liquid Limit, Plastic Limit, Clay content and Depth of Soil Samples. Validation of the predictive capability of the novel model was carried out using statistical indices: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R). In addition, two popular ML models namely Reduced Error Pruning Trees (REPTree) and Decision Stump (Dstump) were used for comparison. Results showed that the performance of the novel model ANFIS-DE is the best (R = 0.825, MAE = 0.064 and RMSE = 0.094) in comparison to other models such as REPTree (R = 0.7802, MAE = 0.068 and RMSE = 0.0988) and Dstump (R = 0.7325, MAE = 0.0785 and RMSE = 0.1036). Therefore, the ANFIS-DE model can be used as a promising tool for the correct and quick estimation of the soil Cc, which can be employed in the design and construction of civil engineering structures.

Research paper thumbnail of A Comparative Study of Soft Computing Models for Prediction of Permeability Coefficient of Soil

Mathematical Problems in Engineering, 2021

Determination of the permeability coefficient (K) of soil is considered as one of the essential s... more Determination of the permeability coefficient (K) of soil is considered as one of the essential steps to assess infiltration, runoff, groundwater, and drainage in the design process of the construction projects. In this study, three cost-effective algorithms, namely, artificial neural network (ANN), support vector machine (SVM), and random forest (RF), which are well-known as advanced machine learning techniques, were used to predict the permeability coefficient (K) of soil (10−9 cm/s), based on a set of simple six input parameters such as natural water content w (%), void ratio (e), specific density (g/cm3), liquid limit (LL) (%), plastic limit (PL) (%), and clay content (%). For this, a total of 84 soil samples data collected from the detailed design stage investigations of Da Nang-Quang Ngai national road project in Vietnam was used to generate training (70%) and testing (30%) datasets for building and validating the models. Statistical error indicators such as RMSE and MAE and c...

Research paper thumbnail of GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, India

Mathematical Problems in Engineering, 2021

The main objective of the study was to investigate performance of three soft computing models: Na... more The main objective of the study was to investigate performance of three soft computing models: Naïve Bayes (NB), Multilayer Perceptron (MLP) neural network classifier, and Alternating Decision Tree (ADT) in landslide susceptibility mapping of Pithoragarh District of Uttarakhand State, India. For this purpose, data of 91 past landslide locations and ten landslide influencing factors, namely, slope degree, curvature, aspect, land cover, slope forming materials (SFM), elevation, distance to rivers, geomorphology, overburden depth, and distance to roads were considered in the models study. Thematic maps of the Geological Survey of India (GSI), Google Earth images, and Aster Digital Elevation Model (DEM) were used for the development of landslide susceptibility maps in the Geographic Information System (GIS) environment. Landslide locations data was divided into a 70 : 30 ratio for the training (70%) and testing/validation (30%) of the three models. Standard statistical measures, namely,...

Research paper thumbnail of Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil

Mathematical Problems in Engineering, 2021

The main objective of this study is to evaluate and compare the performance of different machine ... more The main objective of this study is to evaluate and compare the performance of different machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme Learning Machine (ELM), and Boosting Trees (Boosted) algorithms, considering the influence of various training to testing ratios in predicting the soil shear strength, one of the most critical geotechnical engineering properties in civil engineering design and construction. For this aim, a database of 538 soil samples collected from the Long Phu 1 power plant project, Vietnam, was utilized to generate the datasets for the modeling process. Different ratios (i.e., 10/90, 20/80, 30/70, 40/60, 50/50, 60/40, 70/30, 80/20, and 90/10) were used to divide the datasets into the training and testing datasets for the performance assessment of models. Popular statistical indicators, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R), were employed to evaluate the predictive cap...

Research paper thumbnail of GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam

Sustainability, 2019

Landslides affect properties and the lives of a large number of people in many hilly parts of Vie... more Landslides affect properties and the lives of a large number of people in many hilly parts of Vietnam and in the world. Damages caused by landslides can be reduced by understanding distribution, nature, mechanisms and causes of landslides with the help of model studies for better planning and risk management of the area. Development of landslide susceptibility maps is one of the main steps in landslide management. In this study, the main objective is to develop GIS based hybrid computational intelligence models to generate landslide susceptibility maps of the Da Lat province, which is one of the landslide prone regions of Vietnam. Novel hybrid models of alternating decision trees (ADT) with various ensemble methods, namely bagging, dagging, MultiBoostAB, and RealAdaBoost, were developed namely B-ADT, D-ADT, MBAB-ADT, RAB-ADT, respectively. Data of 72 past landslide events was used in conjunction with 11 landslide conditioning factors (curvature, distance from geological boundaries, ...

Research paper thumbnail of Groundwater potential zoning using Logistics Model Trees based novel ensemble machine learning model

Vietnam Journal of Earth sciences/Vietnam Journal of Earth Sciences, Mar 11, 2024

Research paper thumbnail of Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction

Symmetry, Jun 17, 2020

Predicting and mapping fire susceptibility is a top research priority in fire-prone forests world... more Predicting and mapping fire susceptibility is a top research priority in fire-prone forests worldwide. This study evaluates the abilities of the Bayes Network (BN), Naïve Bayes (NB), Decision Tree (DT), and Multivariate Logistic Regression (MLP) machine learning methods for the prediction and mapping fire susceptibility across the Pu Mat National Park, Nghe An Province, Vietnam. The modeling methodology was formulated based on processing the information from the 57 historical fires and a set of nine spatially explicit explanatory variables, namely elevation, slope degree, aspect, average annual temperate, drought index, river density, land cover, and distance from roads and residential areas. Using the area under the receiver operating characteristic curve (AUC) and seven other performance metrics, the models were validated in terms of their abilities to elucidate the general fire behaviors in the Pu Mat National Park and to predict future fires. Despite a few differences between the AUC values, the BN model with an AUC value of 0.96 was dominant over the other models in predicting future fires. The second best was the DT model (AUC = 0.94), followed by the NB (AUC = 0.939), and MLR (AUC = 0.937) models. Our robust analysis demonstrated that these models are sufficiently robust in response to the training and validation datasets change. Further, the results revealed that moderate to high levels of fire susceptibilities are associated with~19% of the Pu Mat National Park where human activities are numerous. This study and the resultant susceptibility

Research paper thumbnail of Comparison of HEC-HMS and SWAT Hydrological Models in Simulating Runoff at Machhu River Catchment, Gujarat, India

Springer eBooks, Nov 17, 2021

Research paper thumbnail of Estimation of Runoff and Soil Erosion for Vishwamitri River Watershed, Western India Using RS and GIS

Water science and engineering, Oct 15, 2015

A watershed is an area covering all the land that contributes water after rainfall occurs to a co... more A watershed is an area covering all the land that contributes water after rainfall occurs to a common point. Watershed management programme is mainly for conservation and development of natural resources. Remote Sensing and Geographic Information System (GIS) are emerging very powerful tools for analyzing spatial distributed information. In these study satellite images of IRS-P6 LISS-III images have been used. Heavy runoff and soil erosion are two severe problems of watershed development. In the present study, SCS Curve Number (CN) is used to estimate the runoff and USLE equations are used to measure the soil loss from the study watershed. The present study is carried out on Vishwamitri river watershed, Panchmahal& Vadodara districts of Gujarat State, India having an area of 1185 Sq.km. The geographical location of the area lies between 22 o 00' and 22 o 45' of north latitude and 73 o 00' and 73 o 45' of east longitude. The daily rainfall data of 5 rain gauge stations (1990-2013) was collected and used to predict the daily runoff from the watershed using SCS-CN method and GIS. The analysis shows that for the study period 1990-2013, minimum and maximum values of (a) yearly computed average rainfall are 336.28 mm and 2170.2 mm and (b) yearly computed average runoff are 49.49 mm and 800.19 mm respectively. All five parameters of USLE equation for soil loss viz. R, K, LS, C, and P were estimated. Watershed based analysis for erosion shows that two sub watersheds coded as SW1 & SW2 are experiencing very severe soil erosion conditions whereas remaining two sub watersheds coded as SW3 & SW4 are subjected to moderate soil erosion conditions. The average computed annual soil loss from study watersheds is 60.65 ton /ha/year.

Research paper thumbnail of Application and Comparison of Decision Tree-Based Machine Learning Methods in Landside Susceptibility Assessment at Pauri Garhwal Area, Uttarakhand, India

Environmental Processes, Jun 23, 2017

Landslide susceptibility assessment has been conducted at the Pauri Garhwal area of Uttarakhand s... more Landslide susceptibility assessment has been conducted at the Pauri Garhwal area of Uttarakhand state, India, an area affected by numerous landslides causing significant losses of life, infrastructure and property every year. Decision tree-based machine learning methods, namely Random Forest (RF), Logistic Model Trees (LMT), Best First Decision Trees (BFDT) and Classification and Regression Trees (CART) have been used, and results are compared herein for proper spatial prediction of landslides. Analysis of the data has been done considering sixteen conditioning factors (i.e., slope angle, elevation, slope aspect, profile curvature, land cover, curvature, lithology, plan curvature, soil, distance to lineaments, lineament density, distance to roads, road density, distance to river, river density and rainfall), and 1295 historical landslide polygons. Models were validated and compared using Receiver Operating Characteristics (ROC) curve and statistical indices. The results show that the RF model has the highest predictive capability, followed by the LMT, BFDT and CART models, respectively, and indicate that although all four methods have shown good results, the performance of the RF method is the best for landslide spatial prediction.

Research paper thumbnail of Development of artificial intelligence models for the prediction of Compression Coefficient of soil: An application of Monte Carlo sensitivity analysis

Science of The Total Environment, Aug 1, 2019

Research paper thumbnail of Landslide Susceptibility Assessment at a Part of Uttarakhand Himalaya, India using GIS – based Statistical Approach of Frequency Ratio Method

International journal of engineering research and technology, Nov 21, 2015

Frequency Ratio has successfully applied as statistical approach for landslide susceptibility ass... more Frequency Ratio has successfully applied as statistical approach for landslide susceptibility assessment in many regions over the world. In the present study, a part of Uttarakhand Himalaya has been selected as a case study to apply the FR model for landslide susceptibility assessment. For this, landslide inventory map was firstly constructed with 430 landslide locations identified from various sources with the help of GIS technology. These landslide locations were then randomly split into two parts (i) for training process (70% landslide locations) and (ii) for validation process (30% landslide locations). Presently, the total of six landslide conditioning factors (slope, aspect, elevation, curvature, land use, and rainfall) has been selected for analyzing the spatial relationship with landslide occurrences. Using training dataset, the FR model was then built to assess landslide susceptibility in the study area. Finally, success rate curve and predictive rate curve have been employed to validate the performance of the FR model. The results show that the FR model indicates fairly well in the present study. Overall, the FR model is an effective method for the landslide susceptibility assessment of hilly areas. It can be applied in other areas of Himalayas for the assessment and management of landslide hazards

Research paper thumbnail of Bagging based Support Vector Machines for spatial prediction of landslides

Environmental Earth Sciences, Feb 1, 2018

A hybrid Bagging based Support Vector Machines (BSVM) method, which is a combination of Bagging E... more A hybrid Bagging based Support Vector Machines (BSVM) method, which is a combination of Bagging Ensemble and Support Vector Machine (SVM) classifier, was proposed for the spatial prediction of landslides at the district of Mu Cang Chai, Viet Nam. In the present study, 248 past landslides and fifteen geo-environmental factors (curvature, elevation, distance to rivers, slope, aspect, river density, plan curvature, distance to faults, profile curvature, fault density, lithology, distance to roads, rainfall, land use, and road density) were considered for the model construction. Different evaluation criteria were applied to validate the proposed hybrid model such as statistical index-based methods and area under the receiver operating characteristic curve (AUC). The single SVM and the Naïve Bayes Trees (NBT) models were selected for comparison. Based on the AUC values, the proposed hybrid model BSVM (0.812) outperformed the SVM (0.804) and NBT (0.8) models. Thus, the BSVM is a promising and better method for landslide prediction.

Research paper thumbnail of Landslide Susceptibility Assessment Using Bagging Ensemble Based Alternating Decision Trees, Logistic Regression and J48 Decision Trees Methods: A Comparative Study

Geotechnical and Geological Engineering, May 22, 2017

In this study, we have evaluated and compared prediction capability of Bagging Ensemble Based Alt... more In this study, we have evaluated and compared prediction capability of Bagging Ensemble Based Alternating Decision Trees (BADT), Logistic Regression (LR), and J48 Decision Trees (J48DT) for landslide susceptibility mapping at part of the Uttarakhand State (India). The BADT method has been proposed in the present study which is a novel hybrid machine learning ensemble approach of bagging ensemble and alternating decision trees. The J48DT is a relative new machine learning technique which has been applied only in few landslide studies, and the LR is known as a popular landslide susceptibility model. For the model studies, a spatial database of 930 historical landslide events and 15 landslide affecting factors have been collected and analyzed. This database has been used to build and validate the landslide models namely BADT, LR and J48DT Predictive capability of these models has been validated and compared using statistical analyzing methods and Receiver Operating Characteristic (ROC) curve. Results show that these three landslide models (BADT, LR and J48DT) performed well with the training dataset. However, using the validation dataset the BADT model has the highest prediction capability, followed by the LR model, and the J48DT model, respectively. This indicates that the BADT is a promising method which can be used for landslide susceptibility assessment also for other landslide prone areas.