Artificial Neural Network and Near Infrared Light in Water pH and Total Ammonia Nitrogen Prediction (original) (raw)

Water Quality Index Estimation Model for Aquaculture System Using Artificial Neural Network

2019

Water Quality plays an important role in attaining a sustainable aquaculture system, its cumulative effect can make or mar the entire system. The amount of dissolved oxygen (DO) alongside other parameters such as temperature, pH, alkalinity and conductivity are often used to estimate the water quality index (WQI) in aquaculture. There exist different approaches for the estimation of the quality index of the water in the aquatic environment. One of such approaches is the use of the Artificial Neural Network (ANN), however, its efficacy lies in the ability to select and use optimal parameters for the network. In this work, different WQI estimation models have been developed using the ANN. These models have been developed by varying the activation function in the hidden layer of the ANN. The performance of the ANN-based estimation models was compared with that of the multilinear regression (MLR) based model. The performance comparison depicts the ANN model case 3 with a tangent activat...

Use of ultraviolet-visible spectrophotometry associated with artificial neural networks as an alternative for determining the water quality index

Environmental monitoring and assessment, 2018

The water quality index (WQI) is an important tool for water resource management and planning. However, it has major disadvantages: the generation of chemical waste, is costly, and time-consuming. In order to overcome these drawbacks, we propose to simplify this index determination by replacing traditional analytical methods with ultraviolet-visible (UV-Vis) spectrophotometry associated with artificial neural network (ANN). A total of 100 water samples were collected from two rivers located in Assis, SP, Brazil and calculated the WQI by the conventional method. UV-Vis spectral analyses between 190 and 800 nm were also performed for each sample followed by principal component analysis (PCA) aiming to reduce the number of variables. The scores of the principal components were used as input to calibrate a three-layer feed-forward neural network. Output layer was defined by the WQI values. The modeling efforts showed that the optimal ANN architecture was 19-16-1, trainlm as training fun...

Review of Nitrogen Compounds Prediction in Water Bodies Using Artificial Neural Networks and Other Models

Sustainability

The prediction of nitrogen not only assists in monitoring the nitrogen concentration in streams but also helps in optimizing the usage of fertilizers in agricultural fields. A precise prediction model guarantees the delivering of better-quality water for human use, as the operations of various water treatment plants depend on the concentration of nitrogen in streams. Considering the stochastic nature and the various hydrological variables upon which nitrogen concentration depends, a predictive model should be efficient enough to account for all the complexities of nature in the prediction of nitrogen concentration. For two decades, artificial neural networks (ANNs) and other models (such as autoregressive integrated moving average (ARIMA) model, hybrid model, etc.), used for predicting different complex hydrological parameters, have proved efficient and accurate up to a certain extent. In this review paper, such prediction models, created for predicting nitrogen concentration, are c...

Assessment of Water Quality in Lake Qaroun Using Ground-Based Remote Sensing Data and Artificial Neural Networks

Water

Monitoring and managing water quality parameters (WQPs) in water bodies (e.g., lakes) on a large scale using sampling-point techniques is tedious, laborious, and not highly representative. Hyperspectral and data-driven technology have provided a potentially valuable tool for the precise measurement of WQPs. Therefore, the objective of this work was to integrate WQPs, derived spectral reflectance indices (published spectral reflectance indices (PSRIs)), newly two-band spectral reflectance indices (NSRIs-2b) and newly three-band spectral indices (NSRIs-3b), and artificial neural networks (ANNs) for estimating WQPs in Lake Qaroun. Shipboard cruises were conducted to collect surface water samples at 16 different sites throughout Lake Qaroun throughout a two-year study (2018 and 2019). Different WQPs, such as total nitrogen (TN), ammonium (NH4+), orthophosphate (PO43−), and chemical oxygen demand (COD), were evaluated for aquatic use. The results showed that the highest determination coe...

Machine learning for manually-measured water quality prediction in fish farming

PLOS ONE

Monitoring variables such as dissolved oxygen, pH, and pond temperature is a key aspect of high-quality fish farming. Machine learning (ML) techniques have been proposed to model the dynamics of such variables to improve the fish farmer’s decision-making. Most of the research on ML in aquaculture has focused on scenarios where devices for real-time data acquisition, storage, and remote monitoring are available, making it easy to develop accurate ML techniques. However, fish farmers do not necessarily have access to such devices. Many of them prefer to use equipment to manually measure these variables limiting the amount of available data to process. In this work, we study the use of random forests, multivariate linear regression, and artificial neural networks in scenarios with limited amount of measurements to analyze data from water-quality variables that are commonly measured in fish farming. We propose a methodology to build models in two scenarios: i) estimation of unobserved v...

Nonlinear neural network-based mixture model for estimating the concentration of nitrogen salts in turbid inland waters using hyperspectral imagery

2004

The development of hyperspectral imaging instruments designed for water quality assessment, such as the DLR Reflective Optics System Imaging Spectrometer (ROSIS), has created a need for methods which are able to infer water quality parameters of turbid inland waters, and to use those parameters as indicators for water quality. It has been reported that the irradiance reflectance and, subsequently, the radiance collected by the sensor in such scenario is usually the result of an intimate mixture of sub-pixel components. As a result, the commonly used linear mixing model may not be appropriate to describe materials composition. In this work, we develop a nonlinear neural network-based algorithm for estimating water constituent concentrations, with special emphasis on the detection of chemical substances provided by agricultural and industrial sources. The proposed neural network architecture consists of a modified multi-layer perceptron (MLP) whose entries are determined by a linear function activation provided by a Hopfield neural network algorithm (HNN). The combined HNN/MLP supervised model has been used to estimate the concentration of water constituents by training the MLP with ground spectra of nitrogen salts, which are commonly used in extensive agricultural farms. Such spectra were collected using a Minolta spectro-photometer. The model was calibrated in our laboratory by using mixtures of water and nitrogen salt in different proportions. Hyperspectral images collected by the ROSIS imaging spectrometer over the Guadiloba reservoir in Cáceres, SW Spain, are also used in this study to estimate the concentration of nitrogen salts in turbid inland waters.

PREDICTION OF DISSOLVED OXYGEN IN TIGRIS RIVER BY WATER TEMPERATURE AND BIOLOGICAL OXYGEN DEMAND USING ARTIFICIAL NEURAL NETWORKS (ANNs)

The Journal of The University of Duhok

The purpose of this study is to develop a feed-forward neural network (FFNN) model with back-propagation learning algorithm to predict the dissolved oxygen from water temperature and 5 days-biological oxygen demand in the Tigris River, Baghdad-Iraq. The Artificial Neural Networks model was implemented utilizing measured data that were gathered from laboratories of water treatment plant, Baghdad-Iraq, during the year 2008. The correlation analysis between dissolved oxygen and dependent parameters were utilized in selecting the major inputs from water quality parameters for commencing of ANN models. The performance of ANN models were tested utilizing the coefficient of correlation (R), the efficiency coefficient of Nash-Sutcliffe (NS), mean square error (MSE) and mean absolute errors (MAE). The outputs revealed that the feed-forward neural networks using back-propagation learning algorithm which was prepared by temperature and biological oxygen demand offered a relatively high correlation coefficient of 0.885, and efficiency coefficient of 0.782, meanwhile a reasonably low mean square errors of 1.133, and mean absolute errors of 0.369 values for whole array period. The results of the present study demonstrate that the artificial neural networks using FFNN model is capable to forecast the dissolved oxygen values with acceptable accuracy. This is suggesting that the artificial neural network is a useful tool for Tigris River management in Baghdad-Iraq.

Final Performance of ANN versus MLR, COD Prediction models in Water Quality Monitoring.pdf

This paper examined the competence of artificial neural network (ANN) and multiple linear regression (MLR) models in prediction of modest water quality parameter in a Jayakwadi Reservoir. Chemical oxygen demand (COD) is the indirect indicators of organic matters as an envoy parameter; as it affects quality of surface water. ANN with two different architect Feed forward neural network (FFNN) and Cascade correlation neural network (CCNN) has been studied for comparative indices of the optimized ANN with input values of Biochemical oxygen Demand (BOD), Temperature (Temp), Dissolved Oxygen (DO), pH and Total dissolved solid (TDS). Performance of the ANN models was evaluated using coefficient of determination (R2), root mean square error (RMSE) and other statistics. MLR was studied for estimation of COD with same inputs parameters. The computed values of COD by model; ANN method and regression method were in close harmony with their respective measured values. It is found that the results of ANN give better result with less sensitive errors as compared to MLR in much lesser time.

Water Quality Monitoring Using Remote Sensing and an Artificial Neural Network

Water, Air, & Soil Pollution, 2012

In remotely located watersheds or large waterbodies, monitoring water quality parameters is often not feasible because of high costs and site inaccessibility. A cost-effective remote sensing-based methodology was developed to predict water quality parameters over a large and logistically difficult area. Landsat spectral data were used as a proxy, and a neural network model was developed to quantify water quality parameters, namely chlorophyll-a, turbidity, and phosphorus before and after ecosystem restoration and during the wet and dry seasons. The results demonstrate that the developed neural network model provided an excellent relationship between the observed and simulated water quality parameters. These correlated for a specific region in the greater Florida Everglades at R 2 >0.95 in 1998-1999 and in 2009-2010 (dry and wet seasons). Moreover, the root mean square error values for phosphorus, turbidity, and chlorophyll-a were below 0.03 mg L −1 , 0.5 NTU, and 0.17 mg m −3 , respectively, at the neural network training and validation phases. Using the developed methodology, the trends for temporal and spatial dynamics of the selected water quality parameters were investigated. In addition, the amounts of phosphorus and chlorophyll-a stored in the water column were calculated demonstrating the usefulness of this methodology to predict water quality parameters in complex ecosystems.

Implementation of Machine Learning Methods for Monitoring and Predicting Water Quality Parameters

2020

The importance of good water quality for human use and consumption can never be underestimated, and its quality is determined through effective monitoring of the water quality index. Different approaches have been employed in the treatment and monitoring of water quality parameters (WQP). Presently, water quality is carried out through laboratory experiments, which requires costly reagents, skilled labor, and consumes time. Thereby making it necessary to search for an alternative method. Recently, machine learning tools have been successfully implemented in the monitoring, estimation, and predictions of river water quality index to provide an alternative solution to the limitations of laboratory analytical methods. In this study, the potentials of one of the machine learning tools (artificial neural network) were explored in the predictions and estimation of the Kelantan River basin. Water quality data collected from the 14 stations of the River basin was used for modeling and predi...