Water 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.

In this study, the ability of numerous statistical and machine learning models to impute water quality data was investigated at three monitoring stations along the Langat River in Malaysia. Inconsistencies in the percentage of missing data between monitoring stations (varying from 20 percent (moderate) to over 50 percent (high)) represent the greatest obstacle of the study. The main objective was to select the best method for imputation and compare whether there are differences between the methods used by the different stations. The paper focuses on different imputation methods such as Multiple Predictive Mean Matching (PMM), Multiple Random Forest Imputation (RF), Multiple Bayesian Linear Regression Imputation (BLR), Multiple Linear Regression (non-Bayesian) Imputation (LRNB), Multiple Classification and Regression Tree (CART), k-nearest neighbours (kNN) and Bootstrap-based Expectation Maximisation (EMB). Remarkably, among all seven imputation techniques, the kNN produces identically reliable results. The imputed data is all rated as ‘very good’ (NSE > 0.75). This was confirmed by the calculation of |PBIAS|<5.30 (all imputed data are‘very good’) and KGE≥0.87 (all imputations are rated as’ good’). Imputation performance improves for all three monitoring stations with an index of agreement, WI ≥ 0.94, despite varying percentages of missing data. According to the findings, the kNN imputation approach outperforms the others and should be prioritised in actual use. Future research with the existing methods could benefit from the addition of geographical data.

For more than 25 years, the Department of Environment (DOE) of Malaysia has implemented a water quality index (WQI) that uses six key water quality parameters: dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), pH, ammoniacal nitrogen (AN), and suspended solids (SS). Water quality analysis is an essential component of water resources management that must be properly managed to prevent ecological damage from pollution and to ensure compliance with environmental regulations. This increases the need to define an efficient method for WQI analysis. One of the major challenges with the current calculation of the WQI is that it requires a series of sub-index calculations that are time consuming, complex, and prone to error. In addition, the WQI cannot be calculated if one or more water quality parameters are missing. In this study, the optimization method of WQI was developed to address the complexity of the current process. The potential of data-driven modeling, i.e., Support Vector Machine (SVM) based on Nu-Radial basis function with 10-fold cross-validation, was developed and explored to improve the prediction of WQI in Langat watershed. A thorough sensitivity analysis under six scenarios was also conducted to determine the efficiency of the model in WQI prediction. In the first scenario, the model SVM-WQI showed exceptional ability to replicate the DOE-WQI and obtained statistical results at a very high level (correlation coefficient, r &gt; 0.95, Nash Sutcliffe efficiency, NSE &gt;0.88, Willmott’s index of agreement, WI &gt; 0.96). In the second scenario, the modeling process showed that the WQI can be estimated without any of the six parameters. It can be seen that the parameter DO is the most important factor in determining the WQI. The pH is the factor that affects the WQI the least. Moreover, scenarios three to six show the efficiency of the model in terms of time and cost by minimizing the number of variables in the input combination of the model (r &gt; 0.6, NSE &gt;0.5 (good), WI &gt; 0.7 (very good)). In summary, the model will greatly improve and accelerate data-driven decision making in water quality management by making data more accessible and attractive without human intervention.