Water Quality Prediction Model Based Support Vector Machine Model for Ungauged River Catchment under Dual Scenarios

Abstract

Water quality analysis is a crucial step in water resources management and needs to be addressed urgently to control any pollution that may adversely affect the ecosystem and to ensure the environmental standards are being met. Thus, this work is an attempt to develop an efficient model using support vector machine (SVM) to predict the water quality of Langat River Basin through the analysis of the data of six parameters of dual reservoirs that are located in the catchment. The proposed model could be considered as an effective tool for identifying the water quality status for the river catchment area. In addition, the major advantage of the proposed model is that it could be useful for ungauged catchments or those lacking enough numbers of monitoring stations for water quality parameters. These parameters, namely pH, Suspended Solids (SS), Dissolved Oxygen (DO), Ammonia Nitrogen (AN), Chemical Oxygen Demand (COD), and Biochemical Oxygen Demand (BOD) were provided by the Malaysian Department of Environment (DOE). The differences between dual scenarios 1 and 2 depend on the information from prior stations to forecast DO levels for succeeding sites (Scenario 2). This scheme has the capacity to simulate water-quality accurately, with small prediction errors. The resulting correlation coefficient has maximum values of 0.998 and 0.979 after the application of Scenario 1. The approach with Type 1 SVM regression along with 10-fold cross-validation methods worked to generate precise results. The MSE value was found to be between 0.004 and 0.681, with Scenario 1 showing a better outcome.

Gross Solids Content Prediction in Urban WWTPs Using SVM

“…This is because this method has shown good generalization ability, avoiding the problems of training overfitting that occur in other similar methods [29]. Recently, it has also been used in the field of wastewater treatment to predict different parameters of the treatment process [30][31][32][33][34][35][36][37].…”

Investigating the Influence of Meteorological Parameters on the Accuracy of Sea-Level Prediction Models in Sabah, Malaysia

“…Statistical correlation analysis is a common analytical technique for defining the proper input. Variables of data analysis that were used to select meteorological input parameters were analyzed based on the minimum and maximum value of each meteorological input, total sum, average, standard deviation (SD), and coefficient of variation (CV), which are listed in Table 1 [34].…”

Investigation on the Potential to Integrate Different Artificial Intelligence Models with Metaheuristic Algorithms for Improving River Suspended Sediment Predictions

“…A three-layer back propagation model was used to estimate SSL. Rainfall, water discharge, and sediment discharge were model inputs [4]. To investigate the ability of the model, error was computed by benchmarking the observed and simulated data.…”

“…Accurate prediction and estimation of sediment load is necessary for water resource management, flood control, water quality, and so on. [4]. In addition, other hydrological variables have a significant impact on sediment load prediction.…”