Deep Learning for Prediction of Water Quality Index Classification: Tropical Catchment Environmental Assessment

Tiyasha, Tiyasha; Tung, Tran Minh; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.1007/s11053-021-09922-5

Cited by 53 publications

(13 citation statements)

References 54 publications

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“…Machine learning (ML) models including quantile regression forest (QRF), random forest (RF), radial support vector machine (SVM), stochastic gradient boosting (GBM), and gradient boosting machines are applied to predict water quality (WQ) [ 26 ]. Based on the small-scale catchment of Klang River, the novel

deep learning (DL) and RF models prediction of river WQI classification is better [ 27 ]. Combined with neural network models, the groundwater quality and changing trend would be predicted in advance [ 28 , 29 ].…”

Section: Related Workmentioning

confidence: 99%

Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study

Liu

et al. 2022

IJERPH

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Prediction of groundwater quality is an essential step for sustainable utilization of water resources. Most of the related research in the study area focuses on water distribution and rational utilization of resources but lacks results on groundwater quality prediction. Therefore, this paper introduces a prediction model of groundwater quality based on a long short-term memory (LSTM) neural network. Based on groundwater monitoring data from October 2000 to October 2014, five indicators were screened as research objects: TDS, fluoride, nitrate, phosphate, and metasilicate. Considering the seasonality of water quality time series data, the LSTM neural network model was used to predict the groundwater index concentrations in the dry and rainy periods. The results suggest the model has high accuracy and can be used to predict groundwater quality. The mean absolute errors (MAEs) of these parameters are, respectively, 0.21, 0.20, 0.17, 0.17, and 0.20. The root mean square errors (RMSEs) are 0.31, 0.29, 0.28, 0.27, and 0.31, respectively. People can be given early warnings and take measures according to the forecast situation. It provides a reference for groundwater management and sustainable utilization in the study area in the future and also provides a new idea for coastal cities with similar hydrogeological conditions.

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Section: Related Workmentioning

confidence: 99%

Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study

Liu

et al. 2022

IJERPH

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“…DO is such of the most widely used water quality indicators. Indeed, this index shows the health of the river water (Tiyasha et al 2021). This parameter means the amount of dissolved oxygen in the water (Yang et al 2021).…”

Section: Dissolved Oxygen (Do)mentioning

confidence: 99%

Modeling the Water Pollutants Nonlinear-based Problems Using Optimized Intelligence Techniques and Determination of Uncertainties

Poursaeid

Poursaeed

2022

Preprint

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Along with the global population growth, the human need for safe drinking water sources has increased. With global warming, the water challenge is perhaps the most crucial challenge for the world community. At the same time, scientific methods are one of the best tools to help humanity. Considering that in many natural phenomena, it is possible to describe them based on complex relationships, it is almost impossible to solve them analytically and mathematically. Therefore, it is necessary to use methods with the ability, accuracy, and high speed to justify nonlinear relationships. One of these methods is Artificial Intelligence (AI). This research used the Extreme Learning Machine (ELM) model and Genetic Algorithm (GA) to create a new hybrid model Genetic Extreme Learning Machine (GAELM). AI and hybrid models were used to simulate and predict the water quality parameter changes. The study area in this work was the Colorado River Basin in the United States. The desired qualitative parameters were Electrical Conductivity (EC) and Dissolved Oxygen (DO). Finally, using seven approaches, the models' performance was compared. The results showed that the best simulation related to the GAELM hybrid model in the EC parameter modeling with indices RMSE and R2 equal to 0.1304, and 0.8619, respectively. Also, the ELM model was ranked in second place in accuracy. Based on the uncertainty analysis (UA-WSM) results, the GAELM(EC) model was the most accurate, with the minimum average prediction error equal to 0.01.

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“…As a result, several studies have evaluated WQI models using binary classes (Islam Khan et al, 2021;Malek et al, 2022); even though most WQI models in the literature suggest using multiple classification schemes to evaluate water quality (Uddin et al, 2021. In order to solve the multiclass problem, many researchers have developed a number of classifier algorithms using state-of-the-art machine learning technique (Bourel and Segura, 2018;Chamasemani, 2011;Tiyasha et al, 2021). For the purposes of the multiclass problem analysis, most commonly used algorithms are support vector machines (SVM), Naïve Bayes (NB), random forest, decision trees, logistic regression, k-nearest neighbour (KNN), and gradient boosting (XGBoost) classifiers (Uddin et al, 2022b).…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of the water quality index model for predicting water state using machine learning techniques

Uddin

Rahman

Olbert

2023

Process Safety and Environmental Protection

140

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Deep Learning for Prediction of Water Quality Index Classification: Tropical Catchment Environmental Assessment

Cited by 53 publications

References 54 publications

Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study

Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study

Modeling the Water Pollutants Nonlinear-based Problems Using Optimized Intelligence Techniques and Determination of Uncertainties

Performance analysis of the water quality index model for predicting water state using machine learning techniques

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