Flood Risk Pattern Recognition Using Chemometric Technique: A Case Study in Muda River Basin (original) (raw)
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Flood Risk Pattern Recognition Using Chemometric Technique: A Case Study In Kuantan River Basin
Integrated Chemometric and Artificial Neural Network were being applied in this study to identify the main contributor for flood, predicting hydrological modelling and risk of flood occurrence at the Kuantan river basin. Based on the Correlation Test analysis, the relationship for Suspended Solid and Stream Flow with Water Level were very high with Pearson correlation of coefficient value more than 0.5. Factor Analysis had been carried out and based on the result, variables such as Stream Flow, Suspended Solid and Water Level turned out to be the major factors and had a strong factor pattern with the results of factor score with >0.7 respectively. Time series analysis was being employed and the limitation had been set up where the Upper Control Limit for Stream Flow, Suspended Solid and Water Level where at this level, it was predicted by using Artificial Neural Network (ANN) to be High Risk Class. The accuracy of prediction from this method stood at 97.8%.
FLOOD RISK PATTERN RECOGNITION BY USING ENVIRONMETRIC TECHNIQUE: A CASE STUDY IN LANGAT RIVER BASIN
This study looks into the downscaling of statistical model to produce and predict hydrological modelling in the study area based on secondary data derived from the Department of Drainage and Irrigation (DID) since 1982-2012. The combination of chemometric method and time series analysis in this study showed that the monsoon season and rainfall did not affect the water level, but the suspended solid, stream flow and water level that revealed high correlation in correlation test with p-value < 0.0001, which affected the water level. The Factor analysis for the variables of the stream flow, suspended solid and water level showed strong factor pattern with coefficient more than 0.7, and 0.987, 1.000 and 1.000, respectively. Based on the Statistical Process Control (SPC), the Upper Control Limit for water level, suspended solid and stream flow were 21.110 m3/s, 4624.553 tonnes/day, and 8.224 m/s, while the Lower Control Limit were 20.711 m, 2538.92 tonnes/day and 2.040 m/s. This shows that human development in the area gives high impact towards climate change and flood risk, and not the monsoon season. Prediction was carried out using the Artificial Neural Network (ANN) to classify risks into their own classes, and the rate of accuracy for the prediction was 97.1%. This meant that the points in the time series analysis which were located beyond Upper Control Limit were considered as High Risk class, and the probability for flood occurrence is very high. The other classes classified in this prediction are Caution Zone, Low Risk and No risk. This is important to set a trigger for warning system in the case of emergency response plan during flood.
Flood is a major problem in Johor river basin, which normally happened during monsoon season. However in this study, it shows that rainfall did not have a strong relationship for the changes of water level compared to suspended solid and stream flow, where both variables have p-values of <0.0001 and these variables also became the main factors in contributing to the flood occurrence based on Factor Analysis result. Time Series Analysis was being carried out and based on Statistical Process Control, the limitation has been set up for mitigation in controlling flood. All data beyond the Upper Control Limit was predicted to have High Risk to face flood and Emergency Response Plan should be implemented to prevent complication and destruction because of flood. The prediction for the risk level was carried out using the application of Artificial Neural Network (ANN), where the accuracy of prediction was very high, with the result of 96% for the level of accuracy in the prediction of risk class.
Flood Risk Pattern Recognition Analysis in Klang River Basin
International Journal of Engineering & Technology
This study was implemented to identify the specific factors that lead to major contribution of floods in Klang River Basin. A thirty-year (1987-2017) database obtained from Department of Irrigation and Drainage (DID), the selected data was analyzed by using integrated Chemometric techniques. The finding from Correlation Analysis revealed strong correlation between stream flow and water level is more than 0.5 (= 0.799). The finding from Principal Component Analysis proved that the selected parameters were significant with the result of R2 > 0.7was applied as a main tool for further analysis. Based on the result, it revealed that stream flow and water level were the most significant hydrological factor that influenced flood risk pattern in Klang River basin. Based on the result from Statistical Process control (SPC), the finding showed that the Upper Control Limit (UCL) for water level was 30.290m. The plotted data which is more than 30.290 m can cause flood to occur in Klang River...
India is one of the most flood-affected countries in the world. The Koshi River, in north Bihar plains of India, gives a challenge in term of long and repetitive flood hazards. Supaul district often gets severely affected due to flood over Koshi River basin. Districts of Koshi division, Saharsa, Purnia and Madhepura, also get affected by flood. For monitoring flood events over this region, Standardized Precipitation Index (SPI) has been extensively used in the present study. This index is useful, as it can indicate the hydrological and climatological conditions of the river basin to a certain extent. SPI values over the flood-prone districts were calculated and analyzed at different time scales, using monthly rainfall data over a period of 50 years . Through the analysis, it was found that SPI indicated flood events of Bihar in the years 1954, 1968, 1974, 1984 and 1987 in a precise manner. Modified Rainfall Anomaly Index (MRAI) is also used at different time scales over the study region. Results of these two indices were compared at different time scales, to show that MRAI can be used as an alternative of SPI. Our study revealed that MRAI values at different timescales are highly correlated with SPI values at corresponding timescales. Linear Regression analysis of MRAI with SPI yielded strong positive correlation between these indices. This paper shows, SPI and MRAI are useful for flood risk assessment & monitoring over Koshi basin and can be utilized for other river basins also.
International Journal of Engineering & Technology, 2018
Flood is a major issue during monsoon season in Northern region of Malaysia especially in Muda River Basin. This study focused on the specific hydrology parameters that lead to the flood events in Muda River Basin, Kedah. There were 4 hydrologic parameters for thirty years of collected data from selected hydrology monitoring stations provided by Department of Irrigations and Drainage, Malaysia. The study applied Principal Component Analysis (PCA) and result shown that stream flow and suspended solid stand with highest correlation of coefficient variables with the changes of water level in the study area. Statistical Process Control (SPC) applied in this study was to determine the control limit for every selected parameter obtained from PCA. The Upper Control Limit value for water level reported from SPC analysis in the study area was 7.568m and starting from this level and above, the risk of flood is high to occur in the study area. This research proved that the flood risk model created in this study was accurate and flexible for flood early warning system at Muda River Basin.
Flood Risk Index Assessment: Case Study in Lenggor River Basin, Johor, Malaysia
International Journal of Engineering & Technology
The objective of this research is to determine the correlation of selected hydrological variables, to analyzed the significance factors influenced the occurrences of flood, to propose the flood control limit system and establish new flood risk index model in Lenggor River Basin based on secondary data derived from Department of Drainage and Irrigation (DID). Application of Chemometric technique such as Spearman’s Correlation Test, Principle Component Analysis, Statistical Process Control and Flood Risk Index created the most efficient results. Result shows water level has strong factor loading of 0.78 and significant for flood warning alert system application. The Upper Control Limit (UCL) for the water level in study area is 33.23m while the risk index for the water level set by the constructed formula of flood risk index consisting 0-100. The results show 20.6% classified as High Risk Class with index range from 70 and above. Thus, these findings are able to facilitate state gover...
FLOOD RISK INDEX ASSESSMENT IN JOHOR RIVER BASIN
This study is focusing on constructing the flood risk index in the Johor river basin. The application of statistical methods such as factor analysis (FA), statistical process control (SPC) and artificial neural network (ANN) had revealed the most efficient flood risk index. The result in FA was water level has correlation coefficient of 0.738 and the most practicable variable to be used for the warning alert system. The upper control limits (UCL) for the water level in the river basin Johor is 4.423m and the risk index for the water level has been set by this method consisting of 0-100.The accuracy of prediction has been evaluated by using ANN and the accuracy of the test result was R2 = 0.96408 with RMSE= 2.5736. The future prediction for UCL in Johor river basin has been predicted and the value was 3.75m. This model can shows the current and future prediction for flood risk index in the Johor river basin and can help local authorities for flood control and prevention of the state of Johor.
Flood Frequency Analysis for Kosi River Basin, Bihar, India Using Statistical Methods
Civil Engineering and Architecture, 2024
Flood peak estimation provides assistance in water resources management by offering sufficient information regarding possible flood risk. In the present analysis, flood peaks are estimated for various return periods using the probabilistic model. Six statistical methods namely Normal, Gumbel, log Normal, General Extreme Value (GEV), Pearson III and log Pearson III are used to forecast the flood discharge of Kosi river which is responsible for inundating a large area of North Bihar plain despite various flood management activities. The annual flow data for a period of 33 years (1981 to 2013) at Birpur gauge station are used in the study. The flood peak magnitudes are computed for the return period of 5, 10, 20, 50, 100, 200, 500 and 1000 years. The Generalised extreme value method provided the higher values of predicted flood magnitude. The Goodness of fit test for six distributions is assessed using Kolmogorov Smirnov (KS), Chi-Squared (CS) and Anderson Darling (AD) tests. The tests of Goodness of fit show that Normal distribution followed by Generalised extreme value distribution provides the best results for Kosi river basin. The predicted flood peak for different return periods is of greater importance and may be utilised in designing important hydraulic structures along the river, constructing bridges, developing flood inundation zones and flood management activities.
International Journal of Recent Technology and Engineering (IJRTE), 2019
Due to climate change, there is an increased/decreased frequency of peak flood discharge in river and streams. The most important concern of planning is to safe passing of the extreme flood discharge influenced by extreme climatic changes. It is a concern for planning for the storage capacity to safely store extreme discharge/inflow of the river. In this paper, probability theories and statistics for flood frequency factor (K) are applied and based on the results; it is found that the Extreme value model results in to a best model for frequency factor K, as it is yielding the minimum relative error. Using the frequency factor, the flood frequency analysis for peak flood is carried out for climate change scenario/Advance scenario. The peak discharge in advance scenario is more as compare to the base line scenario at most of the stations except three stations located on the south-east of the Sabarmati river basin.