Understanding spatial and temporal patterns of river water quality over a multi-year period is crucial for effective basin management and pollution control. This study applies functional data analysis (FDA) to evaluate monthly water quality index (WQI) data from 16 monitoring stations across the Klang River Basin, Malaysia, covering the period from 2020 to 2023, which spans both pre- and post-pandemic conditions. By treating water quality index (WQI) measurements as smooth functions over time, FDA captures underlying trends and variations that are not readily detected using classical statistical techniques. Functional principal component analysis (FPCA) reveals that the first component accounts for 97% of the total variation, reflecting the dominant pattern in water quality over time, which is characterized by relatively stable upstream conditions and gradual deterioration downstream. The second and third components capture seasonal fluctuations and short-term disturbances, potentially linked to monsoonal cycles and shifts in human activities during the pandemic. Functional clustering based on FPCA scores groups stations according to their temporal behavior, distinguishing upstream areas with stable conditions from downstream areas experiencing greater variability. Spatial interpretation of these clusters offers additional insight into localized pollution sources and environmental stressors. Compared to classical PCA, FDA provides a more detailed, curve-based understanding of time-dependent and location-specific changes in water quality. The result underscore the value of FDA in environmental monitoring, particularly for detecting pre- and post-pandemic shifts, and support its application in guiding adaptive and spatially targeted management strategies for river basins.

The study evaluated GPM-IMERG07, CHIRPS2.0, CPC-CMORPH, and PERSIANN-CDR against ground observations of rainfall for five gauging stations over 10 years (2013–2022) in the Niger Central Hydrological Area, Nigeria. This area is prone to severe annual floods, which lead to devastating downstream effects. The lack of high-density and evenly distributed rain gauge stations has hindered effective research on mitigating flood impacts, necessitating alternative rainfall data sources. Satellite precipitation products (SPPs) are used globally because of their high temporal and spatial resolution, free accessibility, and extensive coverage. However, these products have inherent biases and require comprehensive evaluation. The study employed scatter plots and descriptive statistics for daily and monthly comparisons. Daily SPPs were further analyzed using categorical statistics and a four-component error decomposition method. The findings revealed a higher correlation and greater errors at the monthly temporal resolution than at the daily resolution. PERSIANN-CDR performed better (daily and monthly) with a slightly higher median correlation (0.33 and 0.86), RMSE (9.66 mm and 57.59 mm), and Bias (-0.13 and 4.02). Furthermore, it demonstrated exceptional rainfall detection probability (POD = 85

Flooding is one of the most significant global disasters, causing severe social, economic, and environmental impacts, particularly in riverine regions. Retarding basins are effective flood mitigation measures that temporarily store excess water during peak flows, thereby reducing downstream flood risks. This study investigates the application of this concept by analyzing the Jabung Retarding Basin in Lamongan Regency, part of the Bengawan Solo River, Indonesia. The objective is to evaluate the basin's effectiveness in reducing flood extent, depth, and associated economic losses before and after implementation. Using HEC-RAS 6.5 and ArcGIS 10.3, flood inundation mapping was conducted for return periods of 10, 20, 25, and 50 years. The results show that the Jabung Retarding Basin reduced the flood-affected area by 39.72 %-42.69 %, with the most significant reductions observed in areas where inundation depths exceeded 1.50 m. However, its effect on average flood depth was limited, decreasing slightly from 1.036 to 1.046 m to 0.995-1.031 m after operation. In addition to reducing flood extent, the basin significantly lowered economic losses. For instance, a flood with a 50-year return period previously caused an estimated loss of approximately $9.55 million, which decreased to $5.89 million post-operation. Although this study is site-specific, the findings demonstrate that retarding basins can contribute to integrated flood risk management in other flood-prone or deltaic regions. The methodology employed can be adapted to similar hydrological and socio-economic contexts. Future mitigation efforts should also consider downstream impacts by enhancing floodway capacity, optimizing drainage systems, and implementing early warning systems.

Flash floods pose significant challenges, yet comprehensive data on their occurrences and impacts in Southeast Asia, particularly Malaysia, remain sparse. In this study, 745 flash flood events in Peninsular Malaysia from 2014 to 2019 were systematically compiled and analyzed to identify patterns and contributing factors. Events were categorized based on rainfall duration, flood duration, and causes. Pluvial flash floods, accounting for 60

Global climate change is the most serious challenge that modern society faces. Soil-biochar carbon sequestration is a promising natural solution for capturing carbon. This study monitored the CO2 emissions of five biochar incubated Malaysian Tropical soils (MT-Soil). The recalcitrance index of palm kernel shell biochar (PKS) was higher than that of wood chip biochar (WCB), bamboo biochar (BB), coconut shell biochar (CHB) and rice husk biochar (RHB), and was different from the observed CO2 emission characteristics (WCB > CHB > RHB > BB > PKS). Thus, the carbon sequestration potential of biochar could not be evaluated solely by the recalcitrance index. This CO2 emission is linked not only to the total organic carbon (TOC) and total carbon (TC) of the biochar but also associated with mobile matter (MM), water holding capacity (WHC), available phosphorus (AP), exchangeable potassium (AK), and nitrogen content. The multiple linear regression analysis (MLRA) shows that the weights of these factors on CO2 emissions are as follows: TC > pH > MM > WHC > AP > AK. The results show that in addition to biochar stability, pore structure and available phosphorus release also affect carbon dynamics through indirect effects on microbial activity. This means that to minimize CO2 emissions during application of biochar, it is necessary to use soil that is rich in phosphorus and biochar that has undeveloped pore structure and high stable carbon. Finally, this study provides valuable theoretical underpinnings biochar application in MT-Soil.

An effective drought monitoring tool is essential for the development of timely drought early warning system. This study evaluates Evaporative Demand Drought Index (EDDI) as a drought indicator in measuring spatiotemporal evolution of droughts over Peninsular Malaysia during 1989-2018. The modified Mann-Kendall and Sen's slope tests were performed to detect the presence of monotonic trends in EDDI, Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) and their related climate variables. The performance of EDDI in capturing the drought onset, evolutions and demise of historical severe droughts was also compared with SPI and SPEI at multiple timescales. EDDI demonstrates strong spatiotemporal correlations with SPI and SPEI and comparable performance in historical drought events identification. At sub-monthly timescale, 2-week EDDI displays equivalent drought severities and durations for all historical severe droughts corresponding to the monthly EDDI. In the case when rainfall deficits are normalized in an otherwise warm and dry month, EDDI may serve as a great alternative to SPI and SPEI due to it being sensitive to the changes in prevalent atmospheric conditions. Collectively, the results fill in the knowledge gaps on drought evolutions from the evaporative perspective and highlight the efficacy of EDDI as a valuable drought early warning tool for Peninsular Malaysia. Future study should explore the physical mechanisms behind the development of flash drought and the role of evaporation in the drought propagation processes.

A landslide is defined as the displacement of masses of soil or rock constituting a slope, or a combination thereof, resulting from the destabilization of the slope composition. This study aims to develop an integrated approach to managing landslide hazards in the Konto Watershed by assessing susceptibility through spatial mapping and providing a detailed risk assessment to inform disaster mitigation, landslide management, and land use planning. The susceptibility formula incorporates eight observational parameters: max 3-day rain, slope, rock geology, fault presence, regolith depth, land use, road infrastructure presence, and population density. Each parameter is assigned a weight value, representing its respective weighting factor. Primary data on landslide potential is gathered through direct field observations in critical areas with slopes exceeding 40%, while secondary data is sourced from Indonesian government agencies. The secondary data is then identified, mapped, and analyzed using spatial scoring analysis. The research findings indicate that landslide hazards in areas with high slope gradients and specific soil types, land use combined with elevated rainfall, are categorized as very low (1.16%), low (6.64%), medium (89.24%), and high (3.06%) of susceptibility from 235.22 km2 of watershed area. The regional function area converts 75.8% of existing land use into 61.2% protected zones, 32.2% buffer zones, and 6.6% cultivation zones, without altering 24.2% of existing settlement and rice field areas. Landslide control efforts encompass four treatments: protected and buffer area preservation, mechanical treatment, and cultivation area management based on susceptibility levels. Long-term vegetative treatments are designed according to regional function and susceptibility levels.

Groundwater, the world's most abundant source of freshwater, is rapidly depleting in many regions due to a variety of factors. Accurate forecasting of groundwater level (GWL) is essential for effective management of this vital resource, but it remains a complex and challenging task. In recent years, there has been a notable increase in the use of machine learning (ML) techniques to model GWL, with many studies reporting exceptional results. In this paper, we present a comprehensive review of 142 relevant articles indexed by the Web of Science from 2017 to 2023, focusing on key ML models, including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support vector regression (SVR), evolutionary computing (EC), deep learning (DL), ensemble learning (EN), and hybrid-modeling (HM). We also discussed key modeling concepts such as dataset size, data splitting, input variable selection, forecasting time-step, performance metrics (PM), study zones, and aquifers, highlighting best practices for optimal GWL forecasting with ML. This review provides valuable insights and recommendations for researchers and water management agencies working in the field of groundwater management and hydrology.

Multivariate spatial functional data consists of multiple functions of time-dependent attributes observed at each spatial point. This study focuses on detecting spatial outliers in spatial functional data. Firstly, we develop a new method called Mahalanobis Distance Spatial Outlier (MDSO) to detect functional outliers in the data. The method introduces the multivariate functional Mahalanobis semi-distance and multivariate pairwise functional Mahalanobis semi-distance metrics based on the multivariate functional principal components analysis to calculate the dissimilarity between functions at each spatial point. Via simulation, we show that MDSO performs better than the other competing methods. Secondly, MDSO has been extended to detect spatial functional outliers as well. The functional outliers can now be categorized as global or/and local functional outliers. The appropriate number of neighbors and the cut-off point for the degree of isolation are determined via simulation. Finally, we demonstrate the application of the MDSO on a water quality data set obtained from Sungai Klang basin in Malaysia. The results can be used to support the authority in making better decisions on the management of the river basin or other spatial data with time-independent attributes.

To promote environmentally resilient development of cities and their infrastructure, government policies and business models are shifting towards sustainable practices especially through implementation of circular economy (CE) principles. Within the construction industry, successful transition to CE should be supported by holistic and comprehensive evaluation of building materials and their sustainable alternatives. This study evaluates the regional environmental impacts of construction materials, specifically aggregates for concrete production in urban areas. The Malaysian construction industry was taken as an example for this purpose. Comparative life cycle assessment (LCA) was carried out on 3 concrete types each containing a different aggregate: natural aggregate (NA); recycled aggregate (RA); and palm oil clinker (POC). Route analysis determined the transportation impacts of materials from their respective origins to the city centres of 5 main Malaysian cities. Results showed that KL was the city with the highest potential for use of sustainable aggregates as it had the lowest transport distances for all materials. Based on these findings, this study proposes recommendations to encourage CE implementation in the local construction industry and their applicability to different cities in Malaysia.

Abstract Rivers are subject to different sources of pollution. Continuous monitoring of river water quality provides an important basis for the authorities to take appropriate action. Water quality monitoring stations located within the river basin can provide necessary water quality data to establish any changes observed in the river water quality. It is important to highlight lower water quality status at specific monitoring stations so that immediate action can be taken. Similarly, it is an utmost important to ensure water quality at monitoring stations close to water catchment areas always at an acceptable level. This study aims to identify such monitoring stations using descriptive and functional data analysis. The approaches were applied to water quality data collected by the Department of Environment Malaysia at 16 stations in the Klang River basin from January 2013 to December 2016. Specifically, the functional boxplot was applied to identify the monitoring station with outlying properties. We identified many occasions when water quality deteriorated or improved largely due to the increase of COD, BOD and TSS. In addition, three stations close to two main catchment areas and forest reserve showed consistently good water quality. These indicate that the surrounding areas of the stations at the upstream of the rivers are still protected from uncontrolled pollution sources. The study is critical for the authority to understand the overall pattern of water quality data at each station so that action can be planned locally to preserve good river water quality.

A modeling framework utilizing the coactive neuro-fuzzy inference system (CANFIS) has been developed for multi-lead time groundwater level (GWL) forecasting in four different wells located in Texas and Florida, USA. Various model input combinations, including GWL, precipitation, temperature, and surface water level variables, have been derived based on proposed correlation analysis using singular spectrum analysis (SSA) remainders. The models have been trained on data subsets of varying lengths to identify the optimal training data duration. Additionally, we have introduced the bagging ensemble learning method to enhance the performance of the CANFIS model. As part of a comprehensive model evaluation process, the best-performing CANFIS model for each forecasting scenario has undergone uncertainty analysis using bootstrap sampling. Our results reveal that the CANFIS model performs satisfactorily for daily forecasting but leaves room for improvement in monthly forecasting, particularly for two-month and three-month ahead forecasts. Moreover, we have identified several optimal input combinations, highlighting the significance of the temperature variable in monthly forecasting. Furthermore, our findings indicate that additional training data does not necessarily lead to improved performance. The ensemble CANFIS model has demonstrated significant performance enhancement, particularly for monthly forecasting. Finally, the CANFIS model uncertainty analysis has shown satisfactory results for daily forecasting scenarios, while monthly forecasting models exhibit higher uncertainties, particularly during periods with distinctly different GWL fluctuation patterns.

This study investigates the capability of both quantile mapping (QM) bias correction and kriging merging techniques to improve precipitation accuracy of Tropical Rainfall Measuring Mission (TRMM) and Integrated Multisatellite Retrievals for the Global Precipitation Measurement (IMERG) satellite estimations over the Langat River Basin, an important river basin in Malaysia as it is the main source of potable water supply to Kuala Lumpur, in the 5-year period (2014-2018). This analysis also integrates both techniques to investigate whether the estimations can be further improved. Findings show that the estimations that undergo QM first followed by kriging merging (QK-TRMM and QK-IMERG) give significant improvement at almost all aspects of rainfall and streamflow comparison. At point-to-pixel rainfall comparison, around 50% improvement can be seen in both time series- and frequency-based statistics as well as an able to perform with a coefficient of correlation (CC) over 0.80 in terms of areal rainfall. The study performs streamflow simulation by employing the hydrological modeling system (HEC-HMS) to validate the performance of raw and enhanced satellite estimations for the 2014-2015 extreme flood events. Both QK-TRMM and QK-IMERG show a great improvement in the overall streamflow simulation with a Nash-Sutcliffe efficiency (NSE) of more than 0.70. The results reveal that the newly proposed bias correction method (merging of the QM and kriging methods) has significantly contributed to the improvement of precipitation estimation, which is crucial in water resources planning and flood forecasting. (C) 2022 American Society of Civil Engineers.

Flash floods are not only the deadliest weather-related hazard but also one of the leading challenges with which governments and societies need to cope. Flash floods occur within a very limited time, which is insufficient to enable effective warnings and preparedness. Flash floods have become, for many reasons, the most frequent form of natural disaster in Malaysia, considerably affecting humans, property, and the economy. Modeling flash flood phenomena in the tropics is challenging due to the high topographic and meteorological complexity of these regions. The uncertain definition boundary of the monsoon flood and the multidisciplinary nature of flash flood studies also increase the challenge of the reviewing process. In this study, a systematic methodology was developed to review flash floods in Malaysia by considering all the possible related issues. This study revealed a gap in the data analysis of flash floods and that related studies in Malaysia are still not highly developed. Accordingly, the creation of a comprehensive Malaysian flash flood dataset is recommended to advance flash flood studies, modeling, and forecasting. Rainfall analysis based on Global Precipitation Measurement and Tropical Rainfall Measuring Mission data of different intensities also confirmed the high variability of rainfall in Malaysia. The highest variability in the hourly-based rainfall dataset was observed in the central region. The information and findings presented here will be useful for interested hydrologists and decision-makers by enabling better water management. Additionally, the proposed recommendations for future research could pave the way for a better understanding of flash floods in Malaysia, and the method could be applied in different river basins worldwide.

The rise of global surface temperature due to warming climate is expected to increase the intensity and occurrence of extreme precipitation events. Previous studies in Southeast Asia revealed complex variations in changes of precipitation extremes. This study presents a spatial–temporal analysis on changes of precipitation extremes in Peninsular Malaysia by utilizing long‐term daily rainfall records at 64 observed stations during 1989–2018. The modified Mann–Kendall and Sen's slope tests were performed to detect the significance and magnitude of trends in eight extreme precipitation indices recommended by the Expert Team on Climate Change Detection and Indices. Statistically significant increasing trends are observed for four of these extreme indices in the annual assessment. Spatial analysis demonstrates an obvious contrast between wet and dry regions in patterns of precipitation extremes. Seasonal analysis reveals the intensity and frequency of wet extremes are enhanced significantly during the northeast monsoon season. Significant correlations are found between precipitation extremes and El Niño–Southern Oscillation, particularly in the northern, eastern and southwest regions. Collectively, the evidence presented suggests that the occurrence of precipitation extremes in Peninsular Malaysia tends to be more frequent and intense over the year and is closely associated with the combined effects of tropical monsoon cycles and El Niño–Southern Oscillation.

Flooding has become a common occurrence in Malaysia, occurring every year in many states, particularly during the northeast monsoon. From 1926 until 2013, Johor State, in its most southern portion of Peninsular Malaysia, experienced severe floods. The Johor River watershed, on the other hand, was decimated by floods in December 2006 and January 2007. The floods flooded the relatively extensive catchment of the upstream Johor River, resulting in a substantial volume of discharge. The study's goal was to undertake river modelling and create a flood map for the Johor River upstream. The Johor River is 123 kms long and has a catchment area of 2,636 km. It starts from Mount Gemuruh and travels generally north–south before discharging into the Johor Strait. The data required in setting up this model includes the river spatial and geometrical data, hydraulics and hydrological data. The developing of the river model was starting by collecting data and insert the input data then the river model had been setup. The model had been calibrated and the results had been analyzed. The observed and simulated data have showed a reasonable agreement with the model. With a flood depth of 3.73 m and 100 ARI, Rantau Panjang is the most flooded area. The 100 ARI flood depth at Rantau Panjang is similar to the observed flood depth during the Johor River flood occurrences in 2007. The flood map River modelling can be a highly beneficial option because it is always possible to assess and anticipate with enough data.

Reservoir inflow (Q(flow)) forecasting is one of the crucial processes in achieving the best water resources management in a particular catchment area. Although physical models have taken place in solving this problem, those models showed a noticeable limitation due to their requirements for huge efforts, hydrology and climate data, and time-consuming learning process. Hence, the recent alternative technology is the development of the machine learning models and deep learning neural network (DLNN) is the recent promising methodology explored in the field of water resources. The current research was adopted to forecast Q(flow) at two different catchment areas characterized with different type of inflow stochasticity, (semi-arid and topical). Validation against two classical algorithms of neural network including multilayer perceptron neural network (MLPNN) and radial basis function neural network (RBFNN) was elaborated and discussed. The research was further investigated the potential of the feature selection algorithm "genetic algorithm (GA)", for identifying the appropriate predictors. The research finding confirmed the feasibility of the developed DLNN model for the investigated two case studies. In addition, the DLNN model confirmed its capability in solving daily scale Q more accurately in comparison with the monthly scale. The applied GA as feature selection algorithm was reduced the dimension and complexity of the learning process of the applied predictive model. Further, the research finding approved the adequacy of the data span used in the current investigation development of computerized ML algorithm.

Droughts are constantly threatening the global water availability and food securities worldwide. This study aims to evaluate the short- and long-term (1-, 6- and 12-month) drought conditions in Peninsular Malaysia during 1989-2018 using Standardized Precipitation Index and Evaporative Demand Drought Index. Historical trends of drought conditions were analyzed using modified Mann-Kendall test. Spearman’s ρ approach was also applied to examine the spatial patterns of correlations between these drought indices. Based on the findings, Evaporative Demand Drought Index shows increasing tendency towards drier conditions in the northern half of Peninsular Malaysia, but opposite trends are observed for Standardized Precipitation Index. The time series of Evaporative Demand Drought Index are generally well-correlated to that of Standardized Precipitation Index at all three timescales for the whole study area, except for the northern region. The evidence presented suggests Evaporative Demand Drought Index is a great alternative for drought monitoring applications in Peninsular Malaysia.

Accurate and reliable optimization and simulation of the dam reservoir system to ensure optimal use of water resources cannot be achieved without precise and effective models. Providing insight into reservoir system operation and simulation modeling through a comprehensive overview of the previous studies and expanding research horizons can enhance the potential for accurate and well-designed models. The current research reviews previous studies that have used optimization methods to find optimal operating policies for a reservoir system over the past 20 years. Indeed, successful operating policies cannot be obtained without achieving accurate predictions of the main hydrological parameters in the reservoir system, which are inflow and evaporation. The present study focuses on giving an overview of the applications of AI-based models for predicting reservoir inflow and evaporation. The advantages and disadvantages of both optimization algorithms and predictive models have been summarized. Several recommendations for future research have also been included in the present review paper.

A spatial outlier refers to the observation whose non-spatial attribute values are significantly different from those of its neighbors. Such observations can also be found in water quality data at monitoring stations within a river network. However, existing spatial outlier detection procedures based on distance measures such as the Euclidean distance between monitoring stations do not take into account the river network topology. In general, water quality levels in lower streams will be affected by the flow from the upper streams. Similarly, the water quality at some tributaries may have little influence on the other tributaries. Hence, a method for identifying spatial outliers in a river network, taking into account the effect of river flow connectivity on the determination of the neighbors of the monitoring stations, is proposed. While the robust Mahalalobis distance is used in both methods, the proposed method uses river distance instead of the Euclidean distance. The performance of the proposed method is shown to be superior using a synthetic river dataset through simulation. For illustration, we apply the proposed method on the water quality data from Sg. Klang Basin in 2016 provided by the Department of Environment, Malaysia. The finding provides a better identification of the water quality in some stations that significantly differ from their neighbouring stations. Such information is useful for the authorities in their planning of the environmental monitoring of water quality in the areas.