Faridah Othman

TL;DR: A Neuro-Fuzzy Inference System (WDT-ANFIS) based augmented wavelet de-noising technique has been recommended that depends on historical data of the water quality parameter and exhibited a significant improvement in predicting accuracy for all theWater quality parameters and outperformed all the recommended models.

TL;DR: In this paper, a support vector machine (SVM) was used 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.

TL;DR: This study presents the usefulness of several statistical methods in evaluating and interpreting water quality data for the purpose of monitoring the effectiveness of water resource management and finds that the resulting latent variables from the factor analysis are interpretable and beneficial for describing the water quality in the Klang River.

TL;DR: The hybrid ANFIS-MFO and ANFis-CSO models showed superior performance over the other models; hence, indicates significant implication in their application for other water resources and hydrological variables.

TL;DR: The present policy on managing river quality in the Klang River has produced encouraging results; the policy should be further improved alongside more vigorous monitoring of pollution discharge from various point sources such as industrial wastewater, municipal sewers, wet markets, sand mining and landfills.