Integration of advanced optimization algorithms into least-square support vector machine (LSSVM) for water quality index prediction

Abstract Machine learning models hybridized with optimization algorithms have been applied to many real-life applications, including the prediction of water quality. However, the emergence of newly developed advanced algorithms can provide new scopes and possibilities for further enhancements. In this study, the least-square support vector machine (LSSVM) integrated with advanced optimization algorithms is presented, for the first time, in the prediction of water quality index (WQI) at the Klang River of Malaysia. Thereafter, the LSSVM model using RBF kernel was optimized using the hybrid particle swarm optimization and genetic algorithm (HPSOGA), whale optimization based on self-adapting parameter adjustment and mix mutation strategy (SMWOA) as well as ameliorative moth-flame optimization (AMFO) separately. It was found that the SMWOA-LSSVM model had the better performance for WQI prediction by having the best achievement root means square error (RMSE), mean absolute error (MAE), coefficient of determination (R2) and mean absolute percentage error (MAPE). Comprehensive comparison was done using the global performance indicator (GPI), whereby the SMWOA-LSSVM had the highest average score of 0.31. This could be attributed to the internal architecture of the SMWOA, which was catered to avoid local optima within short optimization period.

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Prediction of water quality index (WQI) using support vector machine (SVM) and least square-support vector machine (LS-SVM)

International Journal of River Basin Management ◽

10.1080/15715124.2019.1628030 ◽

2019 ◽

pp. 1-8 ◽

Cited By ~ 10

Author(s):

Wei Cong Leong ◽

Alireza Bahadori ◽

Jie Zhang ◽

Z. Ahmad

Keyword(s):

Water Quality ◽

Support Vector Machine ◽

Water Quality Index ◽

Quality Index ◽

Least Square ◽

Support Vector

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Prediction of water quality index in constructed wetlands using support vector machine

Environmental Science and Pollution Research ◽

10.1007/s11356-014-3806-7 ◽

2014 ◽

Vol 22 (8) ◽

pp. 6208-6219 ◽

Cited By ~ 45

Author(s):

Reza Mohammadpour ◽

Syafiq Shaharuddin ◽

Chun Kiat Chang ◽

Nor Azazi Zakaria ◽

Aminuddin Ab Ghani ◽

...

Keyword(s):

Water Quality ◽

Support Vector Machine ◽

Constructed Wetlands ◽

Water Quality Index ◽

Quality Index ◽

Support Vector

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Prediction of Water Quality Index by Support Vector Machine: a Case Study in the Sefidrud Basin, Northern Iran

Water Resources ◽

10.1134/s0097807819010056 ◽

2019 ◽

Vol 46 (1) ◽

pp. 112-116 ◽

Cited By ~ 3

Author(s):

Forough Kamyab-Talesh ◽

Seyed-Farhad Mousavi ◽

Mohammadreza Khaledian ◽

Ozra Yousefi-Falakdehi ◽

Mojtaba Norouzi-Masir

Keyword(s):

Water Quality ◽

Support Vector Machine ◽

Water Quality Index ◽

Quality Index ◽

Support Vector ◽

Northern Iran

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Hybrid Support Vector Regression Model and K-Fold Cross Validation for Water Quality Index Prediction in Langat River, Malaysia

10.1101/2021.02.15.431242 ◽

2021 ◽

Author(s):

Naeimah Mamat ◽

Firdaus Mohamad Hamzah ◽

Othman Jaafar

Keyword(s):

Water Quality ◽

Prediction Model ◽

Support Vector Regression ◽

Water Quality Index ◽

Quality Index ◽

Cross Validation ◽

Oxygen Demand ◽

Support Vector ◽

Langat River ◽

Fold Cross Validation

AbstractWater quality analysis is an important step in water resources management and needs to be managed efficiently to control any pollution that may affect the ecosystem and to ensure the environmental standards are being met. The development of water quality prediction model is an important step towards better water quality management of rivers. The objective of this work is to utilize a hybrid of Support Vector Regression (SVR) modelling and K-fold cross-validation as a tool for WQI prediction. According to Department of Environment (DOE) Malaysia, a standard Water Quality Index (WQI) is a function of six water quality parameters, namely Ammoniacal Nitrogen (AN), Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), pH, and Suspended Solids (SS). In this research, Support Vector Regression (SVR) model is combined with K-fold Cross Validation (CV) method to predict WQI in Langat River, Kajang. Two monitoring stations i.e., L15 and L04 have been monitored monthly for ten years as a case study. A series of results were produced to select the final model namely Kernel Function performance, Hyperparameter Kernel value, K-fold CV value and sets of prediction model value, considering all of them undergone training and testing phases. It is found that SVR model i.e., Nu-RBF combined with K-fold CV i.e., 5-fold has successfully predicted WQI with efficient cost and timely manner. As a conclusion, SVR model and K-fold CV method are very powerful tools in statistical analysis and can be used not limited in water quality application only but in any engineering application.

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Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast)

Applied Water Science ◽

10.1007/s13201-021-01528-9 ◽

2021 ◽

Vol 11 (12) ◽

Author(s):

Saber Kouadri ◽

Ahmed Elbeltagi ◽

Abu Reza Md. Towfiqul Islam ◽

Samir Kateb

Keyword(s):

Water Quality ◽

Water Quality Index ◽

Quality Index ◽

Groundwater Resources ◽

Absolute Error ◽

Quality Parameters ◽

Support Vector ◽

Multilinear Regression ◽

Data Set ◽

Artificial Neural Network Ann

AbstractGroundwater quality appraisal is one of the most crucial tasks to ensure safe drinking water sources. Concurrently, a water quality index (WQI) requires some water quality parameters. Conventionally, WQI computation consumes time and is often found with various errors during subindex calculation. To this end, 8 artificial intelligence algorithms, e.g., multilinear regression (MLR), random forest (RF), M5P tree (M5P), random subspace (RSS), additive regression (AR), artificial neural network (ANN), support vector regression (SVR), and locally weighted linear regression (LWLR), were employed to generate WQI prediction in Illizi region, southeast Algeria. Using the best subset regression, 12 different input combinations were developed and the strategy of work was based on two scenarios. The first scenario aims to reduce the time consumption in WQI computation, where all parameters were used as inputs. The second scenario intends to show the water quality variation in the critical cases when the necessary analyses are unavailable, whereas all inputs were reduced based on sensitivity analysis. The models were appraised using several statistical metrics including correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and root relative square error (RRSE). The results reveal that TDS and TH are the key drivers influencing WQI in the study area. The comparison of performance evaluation metric shows that the MLR model has the higher accuracy compared to other models in the first scenario in terms of 1, 1.4572*10–08, 2.1418*10–08, 1.2573*10–10%, and 3.1708*10–08% for R, MAE, RMSE, RAE, and RRSE, respectively. The second scenario was executed with less error rate by using the RF model with 0.9984, 1.9942, 3.2488, 4.693, and 5.9642 for R, MAE, RMSE, RAE, and RRSE, respectively. The outcomes of this paper would be of interest to water planners in terms of WQI for improving sustainable management plans of groundwater resources.

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Spatio-temporal prediction of water quality parameters of a reservoir using genetic programming and least square support vector machine

International Journal of Hydrology Science and Technology ◽

10.1504/ijhst.2018.093597 ◽

2018 ◽

Vol 8 (3) ◽

pp. 273

Author(s):

Mrunalini Shivaji Jadhav ◽

Kanchan Chandrashekhar Khare ◽

Arundhati Suresh Warke

Keyword(s):

Water Quality ◽

Support Vector Machine ◽

Genetic Programming ◽

Quality Parameters ◽

Least Square ◽

Water Quality Parameters ◽

Support Vector ◽

Temporal Prediction ◽

Spatio Temporal

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Exploring Artificial Intelligence Techniques for Groundwater Quality Assessment

Water ◽

10.3390/w13091172 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1172

Author(s):

Purushottam Agrawal ◽

Alok Sinha ◽

Satish Kumar ◽

Ankit Agarwal ◽

Ashes Banerjee ◽

...

Keyword(s):

Artificial Intelligence ◽

Water Quality ◽

Water Quality Index ◽

Quality Index ◽

Traditional Approach ◽

Human Life ◽

Large Data ◽

Quality Parameters ◽

Support Vector ◽

Artificial Intelligence Techniques

Freshwater quality and quantity are some of the fundamental requirements for sustaining human life and civilization. The Water Quality Index is the most extensively used parameter for determining water quality worldwide. However, the traditional approach for the calculation of the WQI is often complex and time consuming since it requires handling large data sets and involves the calculation of several subindices. We investigated the performance of artificial intelligence techniques, including particle swarm optimization (PSO), a naive Bayes classifier (NBC), and a support vector machine (SVM), for predicting the water quality index. We used an SVM and NBC for prediction, in conjunction with PSO for optimization. To validate the obtained results, groundwater water quality parameters and their corresponding water quality indices were found for water collected from the Pindrawan tank area in Chhattisgarh, India. Our results show that PSO–NBC provided a 92.8% prediction accuracy of the WQI indices, whereas the PSO–SVM accuracy was 77.60%. The study’s outcomes further suggest that ensemble machine learning (ML) algorithms can be used to estimate and predict the Water Quality Index with significant accuracy. Thus, the proposed framework can be directly used for the prediction of the WQI using the measured field parameters while saving significant time and effort.

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