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Rainwater harvesting is an effective alternative practice, particularly within urban regions, during periods of water scarcity and dry weather. The collected water is mostly utilized for non-potable household purposes and irrigation. However, due to the increase in atmospheric pollutants, the quality of rainwater has gradually decreased. This atmospheric pollution can damage the climate, natural resources, biodiversity, and human health. In this study, the characteristics and physicochemical properties of rainfall were assessed using a qualitative approach. The three-year (2017–2019) data on rainfall in Peninsular Malaysia were analysed via multivariate techniques. The physicochemical properties of the rainfall yielded six significant factors, which encompassed 61.39% of the total variance as a result of industrialization, agriculture, transportation, and marine factors. The purity of rainfall index (PRI) was developed based on subjective factor scores of the six factors within three categories: good, moderate, and bad. Of the 23 variables measured, 17 were found to be the most significant, based on the classification matrix of 98.04%. Overall, three different groups of similarities that reflected the physicochemical characteristics were discovered among the rain gauge stations: cluster 1 (good PRI), cluster 2 (moderate PRI), and cluster 3 (bad PRI). These findings indicate that rainwater in Peninsular Malaysia was suitable for non-potable purposes. [ABSTRACT FROM AUTHOR]

Abstract The prediction models of MWQI in mangrove and estuarine zones were constructed. The 2011–2015 data employed in this study entailed 13 parameters from six monitoring stations in West Malaysia. Spatial discriminant analysis (SDA) had recommended seven significant parameters to develop the MWQI which were DO, TSS, O&G, PO 4 , Cd, Cr and Zn. These selected parameters were then used to develop prediction models for the MWQI using artificial neural network (ANN) and multiple linear regressions (MLR). The SDA-ANN model had higher R2 value for training (0.9044) and validation (0.7113) results than SDA-MLR model and was chosen as the best model in mangrove estuarine zone. The SDA-ANN model had also demonstrated lower RMSE (5.224) than the SDA-MLR (12.7755). In summary, this work suggested that ANN was an effective tool to compute the MWQ in mangrove estuarine zone and a powerful alternative prediction model as compared to the other modelling methods. Highlights • Comparison of SDA-ANN and SDA-MLR as a tool to predict Marine Water Quality Index (MWQI). • No ultimate test to evaluate the model for mangrove estuarine MWQ • SDA-ANN model provides very high MWQI prediction capacity (R2 = 0.9044) than SDA-MLR (R2 = 0.4863) • SDA-ANN provides acceptable models for uncomplicated, quick computation and prediction of MWQI [ABSTRACT FROM AUTHOR]