Water Quality Index (WQI) is the most common determinant of the quality of the stream-flow. According to the Department of Environment (DOE, Malaysia), WQI is chiefly affected by six factors, which are, chemical oxygen demand (COD), biochemical oxygen demand (BOD), dissolved oxygen (DO), suspended solids (SS), -potential for hydrogen (pH), and ammoniacal nitrogen (AN). In fact, understanding the inter-relationships between these variables and WQI can improve predicting the WQI for better water resources management. The aim of this study is to create an input approach using ANNs (Artificial Neural Networks) to compute the WQI from input parameters instead of using the indices of the parameters when one of the parameters is absent. The data are collected from the nine water quality monitoring stations at the Klang River basin, Malaysia. In addition, comprehensive sensitivity analysis has been carried out to identify the most influential input parameters. The model is based on the frequency distribution of the significant factors showed exceptional ability to replicate the WQI and attained very high correlation (98.78%). Furthermore, the sensitivity analysis showed that the most influential parameter that affects WQI is DO, while pH is the least one. Additionally, the performance of models shows that the missing DO values caused deterioration in the accuracy.

水质指数（WQI）是确定水流质量的最常见决定因素。根据环境部（DOE，马来西亚）的说法，WQI主要受六个因素影响，分别是化学需氧量（COD），生化需氧量（BOD），溶解氧（DO），悬浮固体（SS），-氢（pH）和氨氮（AN）的电位。实际上，了解这些变量与WQI之间的相互关系可以改善对WQI的预测，从而更好地进行水资源管理。这项研究的目的是创建一种使用ANN（人工神经网络）从输入参数计算WQI的输入方法，而不是在缺少其中一个参数时使用参数索引。数据是从马来西亚巴生河流域的9个水质监测站收集的。此外，进行了全面的灵敏度分析，以找出最有影响力的输入参数。该模型基于重要因素的频率分布，显示出复制WQI的出色能力并获得了非常高的相关性（98.78％）。此外，敏感性分析表明，影响WQI的最具影响力的参数是DO，而pH值是最小的。此外，模型的性能表明，缺少的DO值会导致精度下降。敏感性分析表明，影响WQI的最有影响力的参数是DO，而pH值是最小的。此外，模型的性能表明，缺少的DO值会导致精度下降。敏感性分析表明，影响WQI的最有影响力的参数是DO，而pH值是最小的。此外，模型的性能表明，缺少的DO值会导致精度下降。