Prediction Of Water Quality Index Research Articles

Efficient Prediction of Water Quality Index (WQI) Using Machine Learning Algorithms

The quality of water has a direct influence on both human health and the environment. Water is utilized for a variety of purposes, including drinking, agriculture, and industrial use. The water quality index (WQI) is a critical indication for proper water management. The purpose of this work was to use machine learning techniques such as RF, NN, MLR, SVM, and BTM to categorize a dataset of water quality in various places across India. Water quality is dictated by features such as dissolved oxygen (DO), total coliform (TC), biological oxygen demand (BOD), Nitrate, pH, and electric conductivity (EC). These features are handled in five steps: data pre-processing using min-max normalization and missing data management using RF, feature correlation, applied machine learning classification, and model’s feature importance. The highest accuracy Kappa, Accuracy Lower, and Accuracy Upper findings in this research are 99.83, 99.17, 99.07, and 99.99, respectively. The finding showed that Nitrate, PH, conductivity, DO, TC, and BOD are the key qualities that contribute to the orderly classification of water quality, with Variable Importance values of 74.78, 36.805, 81.494, 105.770, 105.166, and 130.173, respectively.

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

Groundwater 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.

Applications of various data-driven models for the prediction of groundwater quality index in the Akot basin, Maharashtra, India.

Data-driven models are important to predict groundwater quality which is controlling human health. The water quality index (WQI) has been developed based on the physicochemical parameters of water samples. In this area, water quality is medium to poor and is found in saline zones; very high pH ranges are directly affected on the water quality in this study area. Conventional WQI computation demands more time and is often observed with enormous errors during the calculation of sub-indices. In the present work, four standalone methods such as additive regression (AR), M5P tree model (M5P), random subspace (RSS), and support vector machine (SVM) were employed to predict WQI based on variable elimination technique. The groundwater samples were collected from the Akot basin area, located in the Akola district, Maharashtra, in India. A total of nine different input combinations were developed in this study. The datasets were demarcated into two classes (ratio 80:20) for model construction (training dataset) and model verification (testing dataset) using a fivefold cross-validation approach. The models were assessed using statistical and graphical appraisal metrics. The best input combinations varied among the model, generally, the optimal input variables (EC, pH, TDS, Ca, Mg, and Cl) during the training and validation stages. Results show that AR outperformed the other data-driven models (R2 = 0.9993, MAE = 0.5243, RMSE = 0.0.6356, %RAE = 3.8449, and RRSE% = 3.9925). The AR is proposed as an ideal model with satisfactory results due to enhanced prediction precision with the minimum number of input parameters and can thus act as the reliable and precise method in the prediction of WQI at the Akot basin.

Forecasting Water Quality Index in Groundwater Using Artificial Neural Network

Groundwater quality monitoring in the vicinity of drilling sites is crucial for the protection of water resources. Selected physicochemical parameters of waters were marked in the study. The water was collected from 19 wells located close to a shale gas extraction site. The water quality index was determined from the obtained parameters. A secondary objective of the study was to test the capacity of the artificial neural network (ANN) methods to model the water quality index in groundwater. The number of ANN input parameters was optimized and limited to seven, which was derived using a multiple regression model. Subsequently, using the stepwise regression method, models with ever fewer variables were tested. The best parameters were obtained for a network with five input neurons (electrical conductivity, pH as well as calcium, magnesium and sodium ions), in addition to five neurons in the hidden layer. The results showed that the use of the parameters is a convenient approach to modeling water quality index with satisfactory and appropriate accuracy. Artificial neural network methods exhibited the capacity to predict water quality index at the desirable level of accuracy (RMSE = 0.651258, R = 0.9992 and R2 = 0.9984). Neural network models can thus be used to directly predict the quality of groundwater, particularly in industrial areas. This proposed method, using advanced artificial intelligence, can aid in water treatment and management. The novelty of these studies is the use of the ANN network to forecast WQI groundwater in an area in eastern Poland that was not previously studied—in Lublin.

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 the 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 mean 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.

Water Quality Index (WQI) for Evaluation of the Surface Water Quality of Bangladesh and Prediction of WQI from Limited Parameters

Water Quality Index (WQI) for Evaluation of the Surface Water Quality of Bangladesh and Prediction of WQI from Limited Parameters

Soft computing technique-based prediction of water quality index

Abstract Water quality plays a crucial role in management of water resources. Water quality indexes (WQIs) are frequently used methods to assess water quality for drinking purposes. A WQI can be predicted using chemical analysis which might not, however, be viable for a longer period in all country-scale rivers. Thus, in this investigation, two neural-based soft computing techniques – an artificial neural network (ANN) and a generalized regression neural network (GRNN) – and one hybrid soft computing techniques – an adaptive neuro-fuzzy interference system (ANFIS) with four membership functions – were used to predict WQIs in Khorramabad, Biranshahr and Alashtar sub-watersheds in Iran. Ten distinct physiochemical parameters were used as input variables and WQI as output. Simultaneously, a correlation plot and pairs were used to ascertain the relation of input and output variables. The soft computing techniques were compared using six fitness criteria: Nash-Sutcliffe efficiency (NSE), mean absolute error (MAE), Legates-McCabe Index (LMI), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of correlation (CC). Results indicated that ANN better predicted WQI than did GRNN and ANFIS. Among the different membership functions of ANFIS, ANFIS_trimf was far better than were the others. Thus, it was concluded that ANN was a viable tool for the prediction of a WQI.

Surface water quality status and prediction during movement control operation order under COVID-19 pandemic: Case studies in Malaysia.

Global concerns have been observed due to the outbreak and lockdown causal-based COVID-19, and hence, a global pandemic was announced by the World Health Organization (WHO) in January 2020. The Movement Control Order (MCO) in Malaysia acts to moderate the spread of COVID-19 through the enacted measures. Furthermore, massive industrial, agricultural activities and human encroachment were significantly reduced following the MCO guidelines. In this study, first, a reconnaissance survey was carried out on the effects of MCO on the health conditions of two urban rivers (i.e., Rivers of Klang and Penang) in Malaysia. Secondly, the effect of MCO lockdown on the water quality index (WQI) of a lake (Putrajaya Lake) in Malaysia is considered in this study. Finally, four machine learning algorithms have been investigated to predict WQI and the class in Putrajaya Lake. The main observations based on the analysis showed that noticeable enhancements of varying degrees in the WQI had occurred in the two investigated rivers. With regard to Putrajaya Lake, there is a significant increase in the WQI Class I, from 24% in February 2020 to 94% during the MCO month of March 2020. For WQI prediction, Multi-layer Perceptron (MLP) outperformed other models in predicting the changes in the index with a high level of accuracy. For sensitivity analysis results, it is shown that NH3-N and COD play vital rule and contributing significantly to predicting the class of WQI, followed by BOD, while the remaining three parameters (i.e. pH, DO, and TSS) exhibit a low level of importance.

Modelling of total dissolved solids in water supply systems using regression and supervised machine learning approaches

Monitoring of water quality through accurate predictions provides adequate information about water management. In the present study, three different modelling approaches: Gaussian process regression (GPR), backpropagation neural network (BPNN) and principal component regression (PCR) models were used to predict the total dissolved solids (TDS) as water quality indicator for the water quality management. The performance of each model was evaluated based on three different sets of inputs from groundwater (GW), surface water (SW) and drinking water (DW). The GPR, BPNN and PCR models used in this study gave an accurate prediction of the observed data (TDS) in GW, SW and DW, with the R2 consistently greater than 0.850. The GPR model gave a better prediction of TDS concentration, with an average R2, MAE and RMSE of 0.987, 4.090 and 7.910, respectively. For the BPNN, an average R2, MAE and RMSE of 0.913, 9.720 and 19.137, respectively, were achieved, while the PCR gave an average R2, MAE and RMSE of 0.888, 11.327 and 25.032, respectively. The performance of each model was assessed using efficiency based indicators such as the Nash and Sutcliffe coefficient of efficiency (ENS) and the index of agreement (d). The GPR, BPNN and PCR models, respectively, gave an ENS of (0.967, 0.915, 0.874) and d of (0.992, 0.977, 0.965). It is understood from this study that advanced machine learning approaches (e.g. GPR and BPNN) are appropriate for the prediction of water quality indices and would be useful for future prediction and management of water quality parameters of various water supply systems in mining communities where artificial intelligence technology is yet to be fully explored.

Prediction of Water Quality Index in Drinking Water Distribution System Using Activation Functions Based Ann

Determination of the drastic changes in water quality is an urgent need in this polluted era and is more essential for the survival of the existing and growing water demand. It has been very difficult to analyze the water quality data. This study focused on the Water Quality Index (WQI) prediction of water samples collected from 1944 different wells surrounding the Vellore district. WQI prediction is carried out by ANN (i.e.) Artificial Neural Networks implementation which has used 15 groundwater variables that are collected in different parts of the Vellore district from 2008 to 2017. If 15 underground variable values meet the desired range then WQI is considered as better and appropriate for drinking. But if any one of the value doesn’t meet the desired range then it is not considered as better and hence not suitable for drinking. In this study the pre-processing of the collected data has been completed to reduce the computational time. Further feature extraction techniques are used to extract the required features. The extracted features are passed on to ANN classifiers that possess three activation functions like Tanh, Maxout, and rectifier. The novelty of this paper is that WQI is determined by combining the three activation functions like Tanh, Maxout, and rectifier. A comparative analysis has been performed for proposed work related with various methodologies.

Machine learning method for quick identification of water quality index (WQI) based on Sentinel-2 MSI data: Ebinur Lake case study

Abstract Surface water quality is an important factor affecting the ecological environment and human living environment. The monitoring of surface water quality by remote sensing monitoring technology can provide important research significance for water resources protection and water quality evaluation. Finding the optimal spectral index sensitive to water quality for remote sensing monitoring of water quality is extremely important for surface water quality analysis and treatment in the Ebinur Lake Basin in arid areas. This study used Sentinel-2MSI data at 10 m resolution to quickly monitor the water quality of the watershed. Through laboratory experiments and measurement data from the Ebinur Lake Basin, 22 water quality parameters (WQPs) were obtained. Through Z-score and redundancy analysis, 9 WQPs with significant contributions were extracted. Based on the remote sensing spectral band, four water indexes (NDWI, NWI, EWI, AWEI-nsh) and 2D modeling spectral index (DI, RI, NDI), the correlation analysis between WQPs and two kinds of spectral band indexes is carried out, and it is concluded that the overall correlation between WQP and 2D spectral modeling is more relevant. This paper calculates the evaluation and models the 2D spectrum of the Water Quality Index (WQI). The WQI is predicted and modeled through four machine learning algorithms (RF, SVM, PLSR, PLSR-SVM).The results show that the inversion effect of the two-dimensional spectral modeling index on water quality parameters (WQPs) is superior to that of the water index, and the correlation coefficient of the DI (R12-R1) SWIR-2 and BLUE band interpolation index reaches 0.787. On this basis, three kinds of two-dimensional spectral modeling indexes are used to inversely synthesize the WQI, and the correlation coefficient of the ratio index of the RI (R11/R8) SWIR-1 and near-infrared (NIR) bands is preferably 0.69. In the WQI prediction, the partial least squares regression support vector machine (PLSR-SVM) model in machine learning algorithms has good modeling and prediction effects (R2c = 0.873, R2v = 0.87), which can provide a good basis. The research results provide references for remote monitoring of surface water in arid areas, and provide a basis for water quality prediction and safety evaluation.

Water Quality Prediction Using Artificial Intelligence Algorithms.

During the last years, water quality has been threatened by various pollutants. Therefore, modeling and predicting water quality have become very important in controlling water pollution. In this work, advanced artificial intelligence (AI) algorithms are developed to predict water quality index (WQI) and water quality classification (WQC). For the WQI prediction, artificial neural network models, namely nonlinear autoregressive neural network (NARNET) and long short-term memory (LSTM) deep learning algorithm, have been developed. In addition, three machine learning algorithms, namely, support vector machine (SVM), K-nearest neighbor (K-NN), and Naive Bayes, have been used for the WQC forecasting. The used dataset has 7 significant parameters, and the developed models were evaluated based on some statistical parameters. The results revealed that the proposed models can accurately predict WQI and classify the water quality according to superior robustness. Prediction results demonstrated that the NARNET model performed slightly better than the LSTM for the prediction of the WQI values and the SVM algorithm has achieved the highest accuracy (97.01%) for the WQC prediction. Furthermore, the NARNET and LSTM models have achieved similar accuracy for the testing phase with a slight difference in the regression coefficient (RNARNET = 96.17% and RLSTM = 94.21%). This kind of promising research can contribute significantly to water management.

River water quality index prediction and uncertainty analysis: A comparative study of machine learning models

The Water Quality Index (WQI) is the most common indicator to characterize surface water quality. This study introduces a new ensemble machine learning model called Extra Tree Regression (ETR) for predicting monthly WQI values at the Lam Tsuen River in Hong Kong. The ETR model performance is compared with that of the classic standalone models, Support Vector Regression (SVR) and Decision Tree Regression (DTR). The monthly input water quality data including Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Electrical Conductivity (EC), Nitrate-Nitrogen (NO3 -N), Nitrite-Nitrogen (NO2 -N), Phosphate (PO43-), potential for Hydrogen (pH), Temperature (T) and Turbidity (TUR) are used for building the prediction models. Various input data combinations are investigated and assessed in terms of prediction performance, using numerical indices and graphical comparisons. The analysis shows that the ETR model generally produces more accurate WQI predictions for both training and testing phases. Although including all the ten input variables achieves the highest prediction performance (R2test=0.98, RMSEtest=2.99), a combination of input parameters including only BOD, Turbidity and Phosphate concentration provides the second highest prediction accuracy (R2test=0.97, RMSEtest=3.74). The uncertainty analysis relative to model structure and input parameters highlights a higher sensitivity of the prediction results to the former. In general, the ETR model represents an improvement on previous approaches for WQI prediction, in terms of prediction performance and reduction in the number of input parameters.

Application of soft computing to predict water quality in wetland.

Prediction of water quality is a critical issue because of its significant impact on human and ecosystem health. This research aims to predict water quality index (WQI) for the free surface wetland using three soft computing techniques namely, adaptive neuro-fuzzy system (ANFIS), artificial neural networks (ANNs), and group method of data handling (GMDH). Seventeen wetland points for a period of 14months were considered for monitoring water quality parameters including conductivity, suspended solid (SS), biochemical oxygen demand (BOD), ammoniacal nitrogen (AN), chemical oxygen demand (COD), dissolved oxygen (DO), temperature, pH, phosphate nitrite, and nitrate. The sensitivity analysis performed by ANFIS indicates that the significant parameters to predict WQI are pH, COD, AN, and SS. The results indicated that ANFIS with Nash-Sutcliffe Efficiency (NSE= 0.9634) and mean absolute error (MAE = 0.0219) has better performance to predict the WQI comparing with ANNs (NSE = 0.9617 and MAE = 0.0222) and GMDH (NSE = 0.9594 and MAE = 0.0245) models. However, ANNs provided a comparable prediction and the GMDH can be considered as a technique with an acceptable prediction for practical purposes. The findings of this study could be used as an effective reference for policy makers in the field of water resource management. Decreasing variables, reduction of running time, and high speed of these approaches are the most important reasons to employ them in any aquatic environment worldwide.

Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index.

In recent decades, various conventional techniques have been formulated around the world to evaluate the overall water quality (WQ) at particular locations. In the present study, back propagation neural network (BPNN) and adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and one multilinear regression (MLR) are considered for the prediction of water quality index (WQI) at three stations, namely Nizamuddin, Palla, and Udi (Chambal), across the Yamuna River, India. The nonlinear ensemble technique was proposed using the neural network ensemble (NNE) approach to improve the performance accuracy of the single models. The observed WQ parameters were provided by the Central Pollution Control Board (CPCB) including dissolved oxygen (DO), pH, biological oxygen demand (BOD), ammonia (NH3), temperature (T), and WQI. The performance of the models was evaluated by various statistical indices. The obtained results indicated the feasibility of the developed data intelligence models for predicting the WQI at the three stations with the superior modelling results of the NNE. The results also showed that the minimum values for root mean square(RMS) varied between 0.1213 and 0.4107, 0.003 and 0.0367, and 0.002 and 0.0272 for Nizamuddin, Palla, and Udi (Chambal), respectively. ANFIS-M3, BPNN-M4, and BPNN-M3 improved the performance with regard to an absolute error by 41%, 4%, and 3%, over other models for Nizamuddin, Palla, and Udi (Chambal) stations, respectively. The predictive comparison demonstrated that NNE proved to be effective and can therefore serve as a reliable prediction approach. The inferences of this paper would be of interest to policymakers in terms of WQ for establishing sustainable management strategies of water resources.

Physicochemical parameters data assimilation for efficient improvement of water quality index prediction: Comparative assessment of a noise suppression hybridization approach

Water quality has a crucial impact on human health; therefore, water quality index modeling is one of the challenging issues in the water sector. The accurate prediction of water quality index is an essential requisite for water quality management, human health, public consumption, and domestic uses. A comprehensive review as an initial attempt is conducted on existing solutions through data-driven models. In addition, the ensemble Kalman filter is found to be a suitable data assimilation method, which is successfully applied in hydrological variables modeling and other complexes, nonlinear, and chaotic problems. In this study, a new application of ensemble Kalman filter-artificial neural network is proposed to predict water quality index using physicochemical parameters for two commonly pollutant rivers, namely Klang and Langat, in Malaysia. As a further attempt, in order to improve the models’ performance, a new preprocessing technique is adopted as the newly constructed assimilated model. The results confirm that ensemble hybrid based intrinsic time-scale decomposition has reduced root mean square error by 24% for Klang and 34% for Langat, respectively, compared with the intrinsic time-scale decomposition-conventional neural network model. Overall, the developed assimilated methodology shows the robustness of the proposed ensemble hybrid model in analyzing water quality index over monthly horizons that experts could evaluate the water quality of rivers more efficiently.

Evolutionary computational intelligence algorithm coupled with self-tuning predictive model for water quality index determination

Anthropogenic activities affect the water bodies and result in a drastic reduction of river water quality (WQ). The development of a reliable intelligent model for evaluating the suitability of water remains a challenging task facing hydro-environmental engineers. The current study is investigated the applicability of Extreme Gradient Boosting (XGB) and Genetic Programming (GP) in obtaining feature importance, and then abstracted input variables were imposed into the predictive model (the Extreme Learning Machine (ELM)) for the prediction of water quality index (WQI). The stand-alone modeling schema is compared with the proposed hybrid models where the optimum variables are supplied into the GP, XGB, linear regression (LR), stepwise linear regression (SWLR) and ELM models. The WQ data is obtained from the Department of Environment (DoE) (Malaysia), and results are evaluated in terms of determination coefficient (R2) and root mean square error (RMSE). The results demonstrated that the hybrid GPELM and XGBELM models outperformed the standalone GP, XGB, and ELM models for the prediction of WQI at Kinta River basin. A comparison of the hybrid models showed that the predictive skill of GPELM (RMSE = 3.441 training and RMSE = 3.484 testing) over XGBELM improving the accuracy by decreasing the values of RMSE by 5% and 9% for training and testing, respectively with regards to XGBELM (RMSE = 3.606 training and RMSE = 3.816 testing). Although regressions are often proposed as reference models (LR and SWLR), when combined with computational intelligence, they still provide satisfactory results in this study. The proposed hybrid GPELM and XGBELM models have improved the prediction accuracy with minimum number of input variables and can therefore serve as reliable predictive tools for WQI at Kinta River basin.

Improving prediction of water quality indices using novel hybrid machine-learning algorithms

River water quality assessment is one of the most important tasks to enhance water resources management plans. A water quality index (WQI) considers several water quality variables simultaneously. Traditionally WQI calculations consume time and are often fraught with errors during derivations of sub-indices. In this study, 4 standalone (random forest (RF), M5P, random tree (RT), and reduced error pruning tree (REPT)) and 12 hybrid data-mining algorithms (combinations of standalones with bagging (BA), CV parameter selection (CVPS) and randomizable filtered classification (RFC)) were used to create Iran WQI (IRWQIsc) predictions. Six years (2012 to 2018) of monthly data from two water quality monitoring stations within the Talar catchment were compiled. Using Pearson correlation coefficients, 10 different input combinations were constructed. The data were divided into two groups (ratio 70:30) for model building (training dataset) and model validation (testing dataset) using a 10-fold cross-validation technique. The models were evaluated using several statistical and visual evaluation metrics. Result show that fecal coliform (FC) and total solids (TS) had the greatest and least effect on the prediction of IRWQIsc. The best input combinations varied among the algorithms; generally variables with very low correlations displayed weaker performance. Hybrid algorithms improved the prediction power of several of the standalone models, but not all. Hybrid BA-RT outperformed the other models (R2 = 0.941, RMSE = 2.71, MAE = 1.87, NSE = 0.941, PBIAS = 0.500). PBIAS indicated that all algorithms, with the exceptions of RT, BA-RT and CVPS-REPT, overestimated WQI values.

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

ABSTRACT The current calculations of water quality index (WQI) were sometimes can be very complex and time-consuming which involves sub-index calculation like BOD and COD, however with the support vector machine (SVM) and least squares support vector machine (LS-SVM) models, the WQI can be predicted immediately using directly measured physical data by using the same predictors used in the numerical approach without any sub-index calculation. There were three main parameters that control the performance of the SVM model however only the type of kernel function was investigated, they were linear, radial basis function (RBF) and polynomial kernel functions. The results of the model were then analysed by using sum squares error (SSE), mean of sum squares error (MSSE) and coefficient of determination (R 2). It was found that the best kernel function for the SVM model was polynomial kernel function with R 2 of 0.8796. Furthermore, the LS-SVM model that trained with correct predictors had higher accuracy with R 2 of 0.9227 as compared with SVM model that trained with all the predictors with R 2 of 0.9184. The SSE and MSSE are 74.78 and 1.5594, 1.6454 for LS-SVM and SVM respectively.

Prediction of the five-day biochemical oxygen demand and chemical oxygen demand in natural streams using machine learning methods.

Rivers, as the most prominent component of water resources, have a key role to play in increasing the life expectancy of living creatures. The essential characteristics of water pollutants can be described by water quality indices (WQIs). Hence, a ferocious demand for obtaining an accurate prediction of WQIs is of high importance for perception of pollutant patterns in natural streams. Field studies conducted on different rivers indicated that there is no general relationship to yield water quality parameters with a permissible level of accuracy. Over the past decades, several artificial intelligence (AI) models have been employed to predict more precise estimation of WQIs rather than conventional models. In this way, through the current study, multivariate adaptive regression spline (MARS) and least square-support vector machine (LS-SVM), as machine learning methods, were used to predict indices of the five-day biochemical oxygen demand (BOD5) and chemical oxygen demand (COD). To improve the proposed approaches, 200 series of field data, collected from Karoun River southwest of Iran, pertain to the nine independent input parameters, namely electrical conductivity (EC), sodium (Na+), calcium (Ca2+), magnesium (Mg2+), orthophosphate ([Formula: see text]), nitrite ([Formula: see text]), nitrate nitrogen ([Formula: see text]), turbidity, and pH. The performances of the LS-SVM and MARS techniques were quantified in both training and testing stages by means of several statistical parameters. Furthermore, the results of the proposed AI models were compared with those obtained using artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and multiple regression equations. Results of the present research work indicated that the proposed artificial intelligence techniques, as machine learning classifiers, were found to be efficient in order to predict water quality parameters.