Search Results (11)

Landslides have been classified as the most dangerous threat around the world, causing huge damage to properties and loss of life. Increased human activity in landslide-prone areas has been a major contributor to the risk of landslide occurrences. Therefore, machine learning has been used in landslide studies to develop a landslide predictive model. The main objective of this study is to evaluate the most suitable sampling ratio for the predictive landslide model in the Langat River Basin (LRB) using Artificial Neural Networks (ANNs). The landslide inventory was divided randomly into training and testing datasets using four sampling ratios (50:50, 60:40, 70:30, and 80:20). A total of 12 landslide conditioning factors were considered in this study, including the elevation, slope, aspect, curvature, topography wetness index (TWI), distance to the road, distance to the river, distance to faults, soil, lithology, land use, and rainfall. The evaluation model was performed using certain statistical measures and area under the curve (AUC). Finally, the most suitable predictive model was chosen based on the model validation results using the compound factor (CF) method. Based on the results, the predictive model with an 80:20 ratio indicates a realistic finding and was classified as the first rank among others. The AUC value for the training dataset is 0.931, while the AUC value for the testing dataset is 0.964. These attempts will help a great deal when it comes to choosing the best ratio of training samples to testing samples to create a reliable and complete landslide prediction model for the LRB. Full article

Traditionally, water quality is evaluated using expensive laboratory and statistical procedures, making real-time monitoring ineffective. Poor water quality requires a more practical and cost-effective solution. Water pollution has been a severe issue, hurting water quality in recent years. Therefore, it is crucial to create a model that forecasts water quality to control water pollution and inform consumers in the event of the detection of poor water quality. For effective water quality management, it is essential to accurately estimate the water quality class. Motivated by these considerations, we utilize the benefits of machine learning methods to construct a model capable of predicting the water quality index and water quality class. This study aims to investigate the performance of machine learning models for multiclass classification in the Langat River Basin water quality assessment. Three machine learning models were developed using Artificial Neural Networks (ANN), Decision Trees (DT), and Support Vector Machines (SVM) to classify river water quality. Comparative performance analysis between the three models indicates that the SVM is the best model for predicting river water quality in this study. In addition, there is a statistically significant difference in performance between the SVM, DT, and ANN models at the 0.05 level of confidence. The use of the kernel function, the grid search method, and the multiclass classification technique used in this study significantly impacts the effectiveness of the SVM model. The findings bolster the idea that machine learning models, particularly SVM, can be used to forecast WQI with a high degree of accuracy, hence enhancing water quality management. Consequently, the model based on machine learning lowered the cost and complexity of calculating sub-indices of six water quality parameters and classifying water quality compared to the standard IKA-JAS formula. Full article

The shutdown of drinking water treatment plants (DWTPs) at the Langat River Basin, Malaysia, which provides drinking water to almost one-third population in the basin, is very frequent, especially due to chemical pollution in the river. This study explored the pollution sources in the Langat River based on eight specific water intake points of the respective DWTPs to suggest an integrated river basin management (IRBM). Analysis of Al (250.26 ± 189.24 µg/L), As (1.65 ± 0.93 µg/L), Cd (1.22 ± 0.88 µg/L), Cr (0.47 ± 0.27 µg/L), and Pb (9.99 ± 5.38 µg/L) by ICP-MS following the Chelex^® 100 column resin ion exchange method found that the mean concentrations except Al were within the water quality standard of the Ministry of Health (MOH) as well as the Dept. of Environment (DOE) Malaysia. However, the determined water quality index based on physicochemical parameters (2005–2015) at the midstream of Langat River was Class III, which needs substantial treatment before drinking. The linear regression model of Al, As, Cd, and Pb suggests that water quality parameters are significantly influencing the increase or decrease in these metal concentrations. Moreover, the principal component analysis (PCA) and the hierarchical cluster analysis (HCA) also support the regression models that the sources of pollution are both natural and man-made activities, and these pollution sources can be clustered into two categories, i.e., upstream (category 1) and mid to downstream (category 2) in the Langat River. The degraded water quality in the midstream compared to up and downstream of the river is mainly due to human activities apart from the natural weathering of minerals. Therefore, the implementation of policies should be effective at the local level for pollution management, especially via the proactive leadership roles of local government for this transboundary Langat River to benefit from IRBM. Full article

Landslides are a natural hazard that can endanger human life and cause severe environmental damage. A landslide susceptibility map is essential for planning, managing, and preventing landslides occurrences to minimize losses. A variety of techniques are employed to map landslide susceptibility; however, their capability differs depending on the studies. The aim of the research is to produce a landslide susceptibility map for the Langat River Basin in Selangor, Malaysia, using an Artificial Neural Network (ANN). A landslide inventory map contained a total of 140 landslide locations which were randomly separated into training and testing with ratio 70:30. Nine landslide conditioning factors were selected as model input, including: elevation, slope, aspect, curvature, Topographic Wetness Index (TWI), distance to road, distance to river, lithology, and rainfall. The area under the curve (AUC) and several statistical measures of analyses (sensitivity, specificity, accuracy, positive predictive value, and negative predictive value) were used to validate the landslide predictive model. The ANN predictive model was considered and achieved very good results on validation assessment, with an AUC value of 0.940 for both training and testing datasets. This study found rainfall to be the most crucial factor affecting landslide occurrence in the Langat River Basin, with a 0.248 weight index, followed by distance to road (0.200) and elevation (0.136). The results showed that the most susceptible area is located in the north-east of the Langat River Basin. This map might be useful for development planning and management to prevent landslide occurrences in Langat River Basin. Full article

Chemical pollution in the transboundary Langat River in Malaysia is common both from point and non-point sources. Therefore, the water treatment plants (WTPS) at the Langat River Basin have experienced frequent shutdown incidents. However, the Langat River is one of the main sources of drinking water to almost one-third of the population in Selangor state. Meanwhile, several studies have reported a high concentration of Arsenic (As) in the Langat River that is toxic if ingested via drinking water. However, this is a pioneer study that predicts the As concentration in the Langat River based on time-series data from 2005–2014 to estimate the health risk associated with As ingestion via drinking water at the Langat River Basin. Several time-series prediction models were tested and Gradient Boosted Tree (GBT) gained the best result. This GBT model also fits better to predict the As concentration until December 2024. The mean concentration of As in the Langat River for both 2014 and 2024, as well as the carcinogenic and non-carcinogenic health risks of As ingestion via drinking water, were within the drinking water quality standards proposed by the World Health Organization and Ministry of Health Malaysia. However, the ingestion of trace amounts of As over a long period might be detrimental to human health because of its non-biodegradable characteristics. Therefore, it is important to manage the drinking water sources to minimise As exposure risks to human health. Full article

Populations in the Langat River Basin, Malaysia, frequently experience water supply disruption due to the shutdown of water treatment plants (WTPs) mainly from the chemical pollution as well as point and non-point sources of pollution. Therefore, this study investigated the aluminium (Al), arsenic (As), cadmium (Cd), chromium (Cr), and lead (Pb) concentrations in the drinking water supply chain at the basin because of its prolonged persistence and toxic characteristics in the aquatic environment. Three replicates of water samples were collected from the river, outlets of WTPs, household tap and filtered water, respectively, in 2015, for analysis by Inductively Coupled Plasma Mass Spectrometry. Higher concentration of these metals was found in household tap water than in the treated water at the WTPs; however, the concentration of these metals at the four stages of the drinking water supply chain conformed to the drinking water quality standard set by the World Health Organization. The Mann-Whitney and Kruskal-Wallis tests also found that metal concentration removal significantly varied among the eight WTPs as well as the five types of household water filtration systems. With regards to the investigated household filtered water, the distilled filtration system was found to be more effective in removing metal concentration because of better management. Therefore, a two-layer water filtration system could be introduced in the Langat River Basin to obtain safe drinking water supply at the household level. Full article

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. Full article

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. Full article

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. Full article

The presence of toxic polonium-210 (Po-210) in the environment is due to the decay of primordial uranium-238. Meanwhile, several studies have reported elevated Po-210 radioactivity in the rivers around the world due to both natural and anthropogenic factors. However, the primary source of Po-210 in Langat River, Malaysia might be the natural weathering of granite rock along with mining, agriculture and industrial activities. Hence, this is the first study to determine the Po-210 activity in the drinking water supply chain in the Langat River Basin to simultaneously predict the human health risks of Po-210 ingestion. Therefore, water samples were collected in 2015–2016 from the four stages of the water supply chain to analyze by Alpha Spectrometry. Determined Po-210 activity, along with the influence of environmental parameters such as time-series rainfall, flood incidents and water flow data (2005–2015), was well within the maximum limit for drinking water quality standard proposed by the Ministry of Health Malaysia and World Health Organization. Moreover, the annual effective dose of Po-210 ingestion via drinking water supply chain indicates an acceptable carcinogenic risk for the populations in the Langat Basin at 95% confidence level; however, the estimated annual effective dose at the basin is higher than in many countries. Although several studies assume the carcinogenic risk of Po-210 ingestion to humans for a long time even at low activity, however, there is no significant causal study which links Po-210 ingestion via drinking water and cancer risk of the human. Since the conventional coagulation method is unable to remove Po-210 entirely from the treated water, introducing a two-layer water filtration system at the basin can be useful to achieve SDG target 6.1 of achieving safe drinking water supplies well before 2030, which might also be significant for other countries. Full article

Malaysia is one of the countries that has been experiencing droughts caused by a warming climate. This study considered the Standard Index of Annual Precipitation (SIAP) and Standardized Water Storage Index (SWSI) to represent meteorological and hydrological drought, respectively. The study area is the Langat River Basin, located in the central part of peninsular Malaysia. The analysis was done using rainfall and water level data over 30 years, from 1986 to 2016. Both of the indices were calculated in monthly scale, and two neural network-based models and two wavelet-based artificial neural network (W-ANN) models were developed for monthly droughts. The performance of the SIAP and SWSI models, in terms of the correlation coefficient (R), was 0.899 and 0.968, respectively. The application of a wavelet for preprocessing the raw data in the developed W-ANN models achieved higher correlation coefficients for most of the scenarios. This proves that the created model can predict meteorological and hydrological droughts very close to the observed values. Overall, this study helps us to understand the history of drought conditions over the past 30 years in the Langat River Basin. It further helps us to forecast drought and to assist in water resource management. Full article