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

Abstract

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.