Langat River Research Articles

Investigating the Status of Cadmium, Chromium and Lead in the Drinking Water Supply Chain to Ensure Drinking Water Quality in Malaysia

Prolonged persistence of toxic cadmium (Cd), chromium (Cr) and lead (Pb) in the aquatic environment are due to its nonbiodegradable characteristic. A few studies have reported higher concentrations of these metals in the transboundary Langat River, Malaysia. This study determined the spatial and temporal distributions of Cd, Cr and Pb concentrations (2005–2015) in the Langat River along with assessing the status of these metals in the drinking water supply chain at the basin. Water samples were collected once in 2015 from the drinking water supply chain, i.e., from the river, treated water at plants, taps and filtration water at households. Determined mean concentrations of Cd, Cr and Pb by inductively coupled plasma mass spectrometry in the Langat River were within the drinking water quality standard of Malaysia and the WHO, except for the Pb (9.99 ± 1.40 µg/L) concentration, which was at the maximum limit, 10 µg/L. The spatial and temporal distribution of these metals’ concentrations indicate dilution of it downstream, along with the increasing trend in rainfall and water flow, especially during the northeast monsoon. Significant correlation and regression analysis of the Cd, Cr and Pb concentrations also indicate that the sources of this metal pollution are mainly the natural weathering of minerals along with anthropogenic activities in the basin. The determined overall water quality of the Langat River is categorized Class IIA (i.e., clean), which requires conventional treatment before drinking; however, the maximum removal efficiency of these metals by the plants at the basin was about 90.17%. Therefore, the proactive leadership roles of the local authorities will be appropriate to reduce the pollution of this river as well as introducing a two-layer water filtration system at the Langat River Basin to accelerate the achievement of a sustainable drinking water supply.

Treated water quality based on conventional method in Langat River Basin, Malaysia

The water of Langat River is not suitable for drinking without treatment because of chemical pollution from both the point and non-point pollution sources, although it is one of the primary sources of drinking water in Selangor state, Malaysia. There is no significant study in Malaysia that has investigated the efficiency of treatment plants to remove metals from treated water. Therefore, Chelex 100 resin-ion exchanged method was applied to investigate the raw water samples and treated water samples of eight treatment plants. Samples were analyzed by Inductive Coupled Plasma-Mass Spectrometry for the dissolved concentrations of aluminium, arsenic, cadmium, chromium, and lead, which have complied with the drinking water quality standard of Ministry of Health Malaysia and World Health Organization. However, the average metal removal efficiency is about 52.48 ± 8.89% from treated water by the treatment plants based on a single sampling and this could be considered a preliminary indication to upgrade the treatment technology in Malaysia, because the efficiency in removing trace metals by the plants may vary in hourly samplings.

Wavelet based hybrid ANN-ARIMA models for meteorological drought forecasting

Drought prediction is an important subject, particularly in drought-hydrology, and has a key role in risk management, drought readiness and alleviation. Hydrological time series data consists of nonlinear features and various time scales. With this view in mind, this study has combined the strengths of the Wavelet transformation, Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) to test a new method of a hybrid model for their ability to accurately predict future droughts. A 30-year rainfall data from the year 1986 to 2016 for Malaysia’s Langat River Basin was analyzed. Meteorological drought indices (DI) such as the Standardized Precipitation Index (SPI) and the Standard Index of Annual Precipitation (SIAP) were used to compute historical drought events. At first, each of these computed drought time series went through a process of decomposition to be divided as low frequency and high-frequency sub-series by discrete wavelet transform (DWT). Secondly, the high and low-frequency sub-series were passed through the predictive model of ANN and ARIMA techniques, respectively. Lastly, the predicted sub-series were used to reconstruct and develop a final drought prediction model. It was found that the Wavelet-ARIMA-ANN (which named as W-2A) model outperformed the single ANN and wavelet-ANN predictive models. The ANN model developed by SPI achieved an overall correlation co-efficient R-value of 0.423, but the wavelet-based ANN model decreased in the R-value to 0.415. Finally, two different models, which were established using drought indices SPI and SIAP, and discrete wavelet transformation-based hybrid ANN-ARIMA (W-2A), have achieved improved R values of 0.914 and 0.934 respectively.

Development of an erosion model for Langat River Basin, Malaysia, adapting GIS and RS in RUSLE

This study is aimed to predict potential soil erosion in the Langat River Basin, Malaysia by integrating Remote Sensing (RS) and Geographical Information System (GIS) with the Revised Universal Soil Loss Equation (RUSLE) model. In RUSLE model, parameters such as rainfall erosivity factor (R), soil erodibility factor (K), slope length and steepness factor (LS), vegetation cover and management factor (C) and support practice factor (P) are determined based on the input data followed by the spatial analysis process in the GIS platform. Rainfall data from 2008-2015 are collected from the 29 rain gauge stations located within the study area. From the analysis, the magnitude of RUSLE model obtained corresponding to the parameter R, K, LS, C and P factors is varied between 800 to 3000 MJ mm ha− 1 h− 1 yr− 1, 0.035–0.5 Mg h MJ− 1 mm− 1, 0–73.00, 0.075–0.77 and 0.2–1.00, respectively. Findings of this study indicates that based on the calculated RUSLE parameter values, about 95% of the Langat River Basin area have been classified as a very low to a low erosion vulnerability. Findings of this study would greatly benefits a decision maker in proposing a suitable soil management and conservation practices for the river basin.

Comparison of Rainfall Interpolation Methods in Langat River Basin

Rainfall is an element of climate that can be measured by a rain gauge. The rain gauge was set up for every station predefined by the Department of Irrigation and Drainage (DID) Malaysia. One millimeter (mm) of rainfall means that within a square meter of a flat surface, water can be as high as one mm. In the hydrology model, the rainfall data is very important in order to predict the flood or assist in the disaster mitigation plan. In this case, the availability of complete rainfall data in a region is essential. By performing spatial interpolation, rainfall data can predict values from the empty data at each point. In this study, Inverse Distance Weighting (IDW), Ordinary Kriging (OK), Simple Kriging (SK) and Kernel Smoothing (KS) method were considered in the rainfall interpolation for this area. Rainfall data at 20 points in Langat River Basin that obtained from DID Ampang for the period 2008–2017 were used as reference data. This study aimed to compare IDW, Kriging and Spline methods to obtain better interpolation methods. The interpolation is done by running a cross-validation using a geostatistical wizard in ArcGIS. The method effectiveness was evaluated by the calculation of mean error (ME), Root Mean Square Error (RMSE), Root mean Square Standardize Error (RMSSE) and Average Standard Error (ASE). For IDW method, only the ME and RMSE results are available. From the result, it can be seen that SK method outperforms the IDW, OK and KS method for these rainfall interpolations in Langat River Basin by showing better statistical evaluation.

Performance Enhancement Model for Rainfall Forecasting Utilizing Integrated Wavelet-Convolutional Neural Network

The core objective of this study is to carry out rainfall forecasting over the Langat River Basin through the integration of wavelet transform (WT) and convolutional neural network (CNN). The proposed method involves using CNN for feature extraction to efficiently learn from the raw rainfall dataset. With the aid of deep architecture, a highly abstracted representation of the inputs time series with a high level of interpretation is formed at each subsequent CNN layer. The use of WT in forecasting the rainfall time series is by preprocessing the raw rainfall dataset into a set of decomposed wavelet components as inputs for the CNN model using discrete wavelet transform (DWT). The conditions for discretizing the raw input through DWT are discussed, along with the criteria to be used. Daily datasets, ranging from January 2002 to December 2017, were used. The results showed that the proposed model could satisfactorily capture patterns of the rainfall time series, for both monthly rainfalls forecasting or daily rainfall forecasting. Three performance indices were used to evaluate the model accuracy: RMSE, RSR, and MAE. These statistical indices have a range of value from 0 to a finite value that depends on the scale of the number used. In general, a lower value is better than a higher one.

Carcinogenic and non-carcinogenic health risk of arsenic ingestion via drinking water in Langat River Basin, Malaysia.

The prolonged persistence of toxic arsenic (As) in environment is due to its non-biodegradable characteristic. Meanwhile, several studies have reported higher concentrations of As in Langat River. However, it is the first study in Langat River Basin, Malaysia, that As concentrations in drinking water supply chain were determined simultaneously to predict the health risks of As ingestion. Water samples collected in 2015 from the four stages of drinking water supply chain were analysed for As concentration by inductively coupled plasma mass spectrometry. Determined As concentrations along with the time series data (2004-2015) were significantly within the maximum limit 0.01mg/L of drinking water quality standard set by World Health Organization. The predicted As concentration by auto-regression moving average was 3.45E-03mg/L in 2020 at 95% level based on time series data including climatic control variables. Long-term As ingestion via household filtration water at Langat Basin showed no potential lifetime cancer risk (LCR) 9.7E-06 (t = 6.68; p = 3.37E-08) as well as non-carcinogenic hazard quotient (HQ) 4.8E-02 (t = 6.68; p = 3.37E-08) risk at 95% level. However, the changing landscape, ex-mining ponds and extensive use of pesticides for palm oil plantation at Langat Basin are considered as the major sources of increased As concentration in Langat River. Therefore, a two-layer water filtration system at Langat Basin should be introduced to accelerate the achievement of sustainable development goal of getting safe drinking water supply.

Correlation of Natural Radioactivity in Surface Soil and River Sediment along the Middle Reach of Langat River

Concentration of natural radionuclides (Ra-226, Ra-228 and K-40) in samples of catchment surface soil and river sediment at three (3) monitoring stations along the middle reach of Langat River were identified using HPGe detector. Spatial and temporal variations of the radioactivity at 3-month interval were analysed over a period of 9 years. Activity concentrations show sign of long-term fluctuation for K-40, and short-term fluctuation for Ra- 226 and Ra-228. Correlation of Ra-226 and Ra-228 is very strong for downstream stations, but weak for the upstream station. Meanwhile, their correlation with K-40 varies considerably possibly due to the high potassium solubility.

Assessing Cadmium and Chromium Concentrations in Drinking Water to Predict Health Risk in Malaysia.

Although toxic Cd (cadmium) and Cr (chromium) in the aquatic environment are mainly from natural sources, human activities have increased their concentrations. Several studies have reported higher concentrations of Cd and Cr in the aquatic environment of Malaysia; however, the association between metal ingestion via drinking water and human health risk has not been established. This study collected water samples from four stages of the drinking water supply chain at Langat River Basin, Malaysia in 2015 to analyze the samples by inductivity coupled plasma mass spectrometry. Mean concentrations of Cd and Cr and the time-series river data (2004–2014) of these metals were significantly within the safe limit of drinking water quality standard proposed by the Ministry of Health Malaysia and the World Health Organization. Hazard quotient (HQ) and lifetime cancer risk (LCR) values of Cd and Cr in 2015 and 2020 also indicate no significant human health risk of its ingestion via drinking water. Additionally, management of pollution sources in the Langat Basin from 2004 to 2015 decreased Cr concentration in 2020 on the basis of autoregression moving average. Although Cd and Cr concentrations were found to be within the safe limits at Langat Basin, high concentrations of these metals have been found in household tap water, especially due to the contamination in the water distribution pipeline. Therefore, a two-layer water filtration system should be introduced in the basin to achieve the United Nations Sustainable Development Goals (SDGs) 2030 agenda of a better and more sustainable future for all, especially via SDG 6 of supplying safe drinking water at the household level.

Analyzing Uncertainty Drivers of Climate Change Impact Studies in Tropical and Arid Climates

Uncertainty in climate change impact studies can be identified in general circulation models (GCM), emission scenarios, downscaling techniques, hydrological models and data. Despite being aleatory or epistemic -sourced uncertainties, their effect size on impact model output can be variable in different climate zones. To test this hypothesis, the present study explored the uncertainty level of each source in the projected streamflow scenarios of the Sarbaz river basin (SRB) with arid climate properties and the tropical Hulu Langat river basin (HLB). An ensemble of five GCMs bias-corrected using EquiDistant-CDF-matching method, two representative concentration pathways (RCPs), and hydrological model parameters sets sourced the overall uncertainty in each case study. Investigations were performed at mean monthly scale of three 30-year periods of baseline, 2016–2045 (2030s), and 2046–2075 (2060s). In both climates, on average, GCM uncertainty was the largest contributor at monthly scale analysis and its effect size escalated following the monsoon months. In the tropical HLB, GCM uncertainty increased across the periods, but it did not show a similar pattern in the arid region of SRB. The RCP uncertainty showed the least effect size during the baseline period and it peaked in 2030s in the HLB climate. However, the only pattern recognizable in the arid SRB was the intensified effect size of all uncertainty sources along with the tremendous impacts projected in monsoon months; at least twice the size of uncertainty sources effect and impacts projected for the tropical HLB. The uncertainty sources effect size altered dramatically as the climate—of the study area—changed. Thus, this research emboldens the need for seasonal-based analysis of uncertainty sources in climate change impact studies at dry climate zones.

Stable channel analysis with sediment transport for rivers in Malaysia: A case study of the Muda, Kurau, and Langat rivers

Assessments of a stable channel were done to evaluate the conditions of three rivers in Malaysia, using an analytical method that modifies the stable channel flowchart developed by Chang (1988) and Ariffin (2004). The analytical approach was selected to calculate the suitable dimensions for a stable channel, using equations that describe the physical relation of sediment transport, flow resistance, and dynamic equilibrium. Measured field data were used as the input data for the stable channel program, which then processed the data until the input discharge was equal to the output discharge. However, this method depends on the accuracy of the sediment transport equation that is used in the stable channel design. Existing equations recommended by the Department of Irrigation and Drainage (DID), Malaysia, were found to be unsuitable because of their low discrepancy ratio (DR) values, which were below 42%. These are the equations of Engelund and Hansen (1967) and Yang (1979), as well as existing local equations from Ariffin (2004) and Sinnakaudan et al. (2006). Therefore, revised equations were developed in the current study to increase the accuracy of the total bed material load equations for use in Malaysian rivers. The newly revised Ariffin (2004) and Sinnakaudan et al. (2006) equations yielded better DRs of 66.34 and 64.49%, respectively. River assessments done on the Kurau River (a small river), the Muda River (a medium-size river), and the Langat River (a large river) show that these rivers have experienced different levels of erosion. Only the Kurau River was found to have minimal erosion and sedimentation levels. Conversely, stable channel assessments for the Muda River and the Langat River revealed that both rivers had experienced severe erosion, due to excessive sand mining. Almost all the cross section sampling points on the Muda River and Langat River were deeper than the suggested stable channel heights.

Precision of raw and bias-adjusted satellite precipitation estimations (TRMM, IMERG, CMORPH, and PERSIANN) over extreme flood events: case study in Langat river basin, Malaysia

Abstract Although satellite precipitation products (SPPs) increasingly provide an alternative means to ground-based observations, these estimations exhibit large systematic and random errors which may cause large uncertainties in hydrologic modeling. Three approaches of bias correction (BC), i.e. linear scaling (LS), local intensity scaling (LOCI), and power transformation (PT), were applied on four SPPs (TRMM, IMERG, CMORPH, and PERSIANN) during 2014/2015 extreme floods in Langat river basin, and the performance in terms of rainfall and streamflow were investigated. The results show that the original TRMM had a potential to predict the peak streamflow although CMORPH show the best performance in general. After performing BC, it is found that the LS-IMERG and LOCI-TRMM show the best performance at both rainfall and streamflow analysis. Generally, it is indicated that the current SPP estimations are still imperfect for any hydrological applications. Cross validation of different datasets is required to avoid the calibration effects of datasets.

Application of the cellular oxidation biosensor to Toxicity Identification Evaluations for high-throughput toxicity assessment of river water

Toxicity Identification Evaluation (TIE) is a useful method for the classification and identification of toxicants in a composite environment water sample. However, its extension to a larger sample size has been restrained owing to the limited throughput of toxicity bioassays. Here we reported the development of a high-throughput method of TIE Phase I. This newly developed method was assisted by the fluorescence-based cellular oxidation (CO) biosensor fabricated with roGFP2-expressing bacterial cells in 96-well microplate format. The assessment of four river water samples from Langat river basin by this new method demonstrated that the contaminant composition of the four samples can be classified into two distinct groups. The entire toxicity assay consisted of 2338 tests was completed within 12 h with a fluorescence microplate reader. Concurrently, the sample volume for each assay was reduced to 50 μL, which is 600 to 4700 times lesser to compare with conventional bioassays. These imply that the throughput of the CO biosensor-assisted TIE Phase I is now feasible for constructing a large-scale toxicity monitoring system, which would cover a whole watershed scale.

Web-based system for visualisation of water quality index

Data visualisation is an essential tool for effective communication and interpretation of information. Visualisation of information using images is easier compared to numerical values. Google Earth provides a platform for a practical monitoring site visualisation system. A real-time web-based water quality monitoring system was deployed on Google Earth based on National Water Quality Index of Malaysia (NWQI). The system used graphical representation through vary shape and colour for easy interpretation of water quality status. NWQI index uses; pH, dissolved oxygen, chemical oxygen demand, biochemical oxygen demand, total suspended solids and ammonical nitrogen. The use of different colour, shades of a hue and shapes for the system can motivate public to participate in environmental conservation. The colour used to represent WQI value on the map is derived from a single hue scheme of different quantities which produce varying brightness and shades. Web-based WQI index can be used to identify pollution sources based on the graphical representation of water quality trend. The system is assessed by evaluating spatial variations water quality of a river in Peninsular Malaysia known as Langat River for 2005, 2010 and 2015. The water quality index status through visualisation can be accessed at the following URL – http://www.umlivinglabsystem.com/SiteViewer/SiteViewerAvgSL.

A review of climate change (floods) and economic attributes response to residential property value in Malaysia

Abstract The art and science of determining residential property value has evolved due to the changing external factors, such as the economy, environmental (climate change) and social aspects. This research aims to identify the impact of climate change (floods) to determine residential economic attributes that could affect the value for residential property in flood risk areas. The case study covers all residential housing schemes in Langat River Basin area, which has been considered as the highest flood risk area in the State of Selangor, Malaysia. The methodology of this research is based on the conceptual analysis from previous studies from local and international scenarios. The systematic analysis of previous literature of real estate valuation theory consists of economic attributes such as structural, locational and environmental attributes involved in residential property valuation in relation to flooding. The findings reveal that the economic attributes' response to flood hazards for residential properties can be divided into three conditions, and they are: positive, negative or no effect on the climate change factor.

Evaluation of bias-adjusted satellite precipitation estimations for extreme flood events in Langat river basin, Malaysia

Abstract Even though satellite precipitation products have received an increasing amount of attention in hydrology and meteorology, their estimations are prone to bias. This study investigates the three approaches of bias correction, i.e., linear scaling (LS), local intensity scaling (LOCI) and power transformation (PT), on the three advanced satellite precipitation products (SPPs), i.e., CMORPH, TRMM and PERSIANN over the Langat river basin, Malaysia by focusing on five selected extreme floods due to northeast monsoon season. Results found the LS scheme was able to match the mean precipitation of every SPP but does not correct standard deviation (SD) or coefficient of variation (CV) of the estimations regardless of extreme floods selected. For LOCI scheme, only TRMM and CMORPH estimations in certain floods have showed some improvement in their results. This might be due to the rainfall threshold set in correcting process. PT scheme was found to be the best method as it improved most of the statistical performances as well as the rainfall distribution of the floods. Sensitivity of the parameters used in the bias correction is also investigated. PT scheme is found to be least sensitive in correcting the daily SPPs compared to the other two schemes. However, careful consideration should be given for correcting the CMORPH and PERSIANN estimations.

Coupling fuzzy–SVR and boosting–SVR models with wavelet decomposition for meteorological drought prediction

Drought is a climatic occurrence of prolonged and abnormal moisture deficiency resulting from meteorological anomalies. Despite its negative impact to agricultural activity and water resources management, drought is still a poorly comprehended calamity, primarily due to the difficulties ascertaning its onset. Effective drought prediction is important for any development of a sustainable natural environment. This study discusses the wavelet–boosting–support vector regression (W–BS–SVR), multi-input wavelet–fuzzy–support vector regression (multi-input W–F–SVR) and weighted wavelet–fuzzy–support vector regression (weighted W–F–SVR) models for meteorological drought predictions, at the downstream of the Langat River Basin; with lead times of 1 month, 3 months, and 6 months. Drought severity is described by the Standardized Precipitation Evapotranspiration Indices (SPEIs) with different timescales of 1 month, 3 months, and 6 months, respectively, known as SPEI-1, SPEI-3, and SPEI-6. The observed SPEIs from 1976 to 2007 were used for model training, while the SPEIs from 2008 to 2015 were for model validation. The root-mean-square-error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and adjusted R2 were applied to assess the performance of models. In general, it was found that the fuzzy-based hybrid model, the weighted W–F–SVR predicted well for SPEI-1, SPEI-3, and SPEI-6 cases, with lead times of 3 and 6 months. As for the 1-month lead time predictions, the models’ performances were dominated by the temporal variation in the SPEIs, where the weighted W–F–SVR that is capable in reducing outlier effects, performed best for high variation SPEI-1 and SPEI-3, while the W–BS–SVR model was better for SPEI-6.

Improved SVR machine learning models for agricultural drought prediction at downstream of Langat River Basin, Malaysia

Abstract Drought is a harmful and little understood natural hazard. Effective drought prediction is vital for sustainable agricultural activities and water resources management. The support vector regression (SVR) model and two of its enhanced variants, namely, fuzzy-support vector regression (F-SVR) and boosted-support vector regression (BS-SVR) models, for predicting the standardized precipitation evapotranspiration indices (SPEI) (in this case, SPEI-1, SPEI-3 and SPEI-6, at various timescales) with a lead time of one month, were developed to minimize potential drought impact on oil palm plantations at the downstream end of the Langat River Basin, which has a tropical climate pattern. Observed SPEIs from periods 1976 to 2011 and 2012 to 2015 were used for model training and validation, respectively. By applying the MAE, RMSE, MBE and R2 as model assessments, it was found that the F-SVR model was best with the trend of improving accuracy when the timescale of the SPEIs increased. It was also found that differences in model performance deteriorates with increased timescale of the SPEIs. The outlier reducing effect from the fuzzy concept has better improvement for the SVR-based models compared to the boosting technique in predicting SPEI-1, SPEI-3 and SPEI-6 for a one-month lead time at the downstream of Langat River Basin.

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

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.

A proposed hybrid rainfall simulation model: bootstrap aggregated classification tree–artificial neural network (BACT-ANN) for the Langat River Basin, Malaysia

AbstractClimate change is a global issue posing threats to the human population and water systems. As Malaysia experiences a tropical climate with intense rainfall occurring throughout the year, accurate rainfall simulations are particularly important to provide information for climate change assessment and hydrological modelling. An artificial intelligence-based hybrid model, the bootstrap aggregated classification tree–artificial neural network (BACT-ANN) model, was proposed for simulating rainfall occurrences and amounts over the Langat River Basin, Malaysia. The performance of this proposed BACT-ANN model was evaluated and compared with the stochastic non-homogeneous hidden Markov model (NHMM). The observed daily rainfall series for the years 1975–2012 at four rainfall stations have been selected. It was found that the BACT-ANN model performed better however, with slight underproductions of the wet spell lengths. The BACT-ANN model scored better for the probability of detection (POD), false alarm rate (FAR) and the Heidke skill score (HSS). The NHMM model tended to overpredict the rainfall occurrence while being less capable with the statistical measures such as distribution, equality, variance and statistical correlations of rainfall amount. Overall, the BACT-ANN model was considered the more effective tool for the purpose of simulating the rainfall characteristics in Langat River Basin.