Involvement of important engineering applications in the interaction of free-moving objects with dispersed multi-phase flows are numerous in recent years. However, due to the challenge and complexity, few studies have been reported and modeling approaches remain very limited. This paper reviews the use of discrete particle models (DPMs) for the study of the fluid flow phenomena and elaborates several different types of multiple phases. The efficiency of particle retention and computational time were considered in determining the most appropriate multiphase model. Therefore, the dynamic modeling of multi-scale systems requires a method to solve the problem of structural fluid interaction (FSI) in the particle fluid flow.

Malaysia, being a tropical country located near the equatorial doldrums, experiences the annual occurrence of flood hazards due to monsoon rainfalls and urban development. In recent years, environmental policies in the country have shifted towards sustainable flood risk management. As part of the development of flood forecasting and warning systems, this study presented the urban flood simulation using InfoWorks ICM hydrological−hydraulic modeling of the Damansara catchment as a case study. The response of catchments to the rainfall was modeled using the Probability Distributed Moisture (PDM) model due to its capability for large catchments with long-term runoff prediction. The interferometric synthetic aperture radar (IFSAR) technique was used to obtain high-resolution digital terrain model (DTM) data. The calibrated and validated model was first applied to investigate the effectiveness of the existing regional ponds on flood mitigation. For a 100-year flood, the extent of flooded areas decreased from 12.41 km² to 3.61 km² as a result of 64-ha ponds in the catchment, which is equivalent to a 71% reduction. The flood hazard maps were then generated based on several average recurrence intervals (ARIs) and uniform rainfall depths, and the results showed that both parameters had significant influences on the magnitude of flooding in terms of flood depth and extent. These findings are important for understanding urban flood vulnerability and resilience, which could help in sustainable management planning to deal with urban flooding issues.

This study evaluates the impacts of construction activities from the Ulu Jelai Hydroelectric Project on the water quality of the Susu Reservoir, a critical freshwater system in Malaysia fed by the Telom, Mensun, Bertam, Menlock, Lemoi, and Tekai rivers. A systematic spatial–temporal analysis was conducted using 15 water quality monitoring stations (WQ1–WQ18), strategically distributed across tributaries, inflow points, and the Susu Dam. Key physicochemical, biological, and hydrological parameters, including turbidity, total suspended solids (TSS), pH, dissolved oxygen (DO), ammonia (NH₃-N), E. coli, and oil and grease (O&G), and flow rate, were analyzed across wet and dry seasons. The results revealed significant seasonal variability in water quality parameters. Dry periods were characterized by elevated DO, O&G, and flow rate levels with an average of 8.98 mg/L, 1932.98 mg/L, and 7.48 m³/s, respectively, alongside reduced TSS and E. coli with an average of 300.23 mg/L and 656.47 CFU/100mL, respectively. In contrast, wet seasons exhibited heightened turbidity, BOD, and nutrient influx, with an average of 201.73 NTU, 1.84mg/L, and 0.16 mg/L, due to runoff within the Upper Susu Watershed. Principal component analysis (PCA) attributed dry-season conditions to climatic and physicochemical drivers, whereas wet-season water quality degradation correlated with anthropogenic activities, such as agricultural runoff, livestock operations, and rainfall conditions. Turbidity levels exceeded regulatory thresholds at multiple monitoring stations, underscoring localized sediment mobilization near construction zones. The findings underscore the cumulative impacts of land-use practices and hydroelectric infrastructure on reservoir integrity. Elevated contaminant levels, driven by seasonal hydrological dynamics and anthropogenic pressures, necessitate adaptive management strategies. This study provides a framework for balancing hydroelectric development with ecological sustainability in tropical montane systems, emphasizing evidence-based practices to minimize anthropogenic degradation in freshwater reservoirs.

The Kelantan River Basin, situated in the northeastern Malaysian Peninsula, suffers serious flood/inundation damage, related to the northeast monsoon season (November–January), every few years. In this river basin, rainfall observation systems have been progressively developed since 1948, and long-term time-series data at distributed rainfall stations have been accumulated. This study firstly investigated the homogeneity of the accumulated time-series data for the purpose of constructing a reliable database for various hydrologic analyses. The homogeneity of rainfall time-series data was established using four absolute homogeneity tests: the Pettitt test, standard normal homogeneity test, Buishand range test, and von Neumann ratio test. It was found that among 50 rainfall stations within the river basin, 9 were flagged by the tests. Of these, inhomogeneous time-series data from four stations were omitted from further analysis. Secondly, using the homogenous time-series rainfall data, a trend analysis of annual rainfall variability was conducted by means of the Mann–Kendall (MK) test. To investigate long- and short-term characteristics of rainfall variability, three sampling methods of the MK test were applied: 30-year, sequentially increased, and 10-year moving segmented sampling. The MK test using the 30-year sampling showed a decreasing trend between 1957 and 1987, and an increasing trend between 1981 and 2011. The MK test using the sequentially increased sampling detected neither significant increasing nor decreasing trends through the same 70-year period. The MK test using 10-year moving segmented sampling clearly showed significant rainfall variability, which corresponded to the El Niño and La Niña events of 1972, 1982, 1988, the mid-1990s, and 2000–2004. Extending the plot, by comparing the turning points with the occurrence of El Niño and La Niña events, the results showed that the influence of the El Niño southern oscillation (ENSO) is the most likely to significantly influence rainfall trends in the Kelantan River Basin.

Currently, the Water Quality Index (WQI) model becomes a widely used tool to evaluate surface water quality for agriculture, domestic and industrial. WQI is one of the simplest mathematical tools that can assist water operator in decision making in assessing the quality of water and it is widely used in the last years. The water quality analysis and prediction is conducted for Johor River Basin incorporating the upstream to downstream water quality monitoring station data of the river. In this research, the numerical method is first used to calculate the WQI and identify the classes for validating the prediction results. Then, two ensemble and optimized machine learning models including gradient boosting regression (GB) and random forest regression (RF) are employed to predict the WQI. The study area selected is the Johor River basin located in Johor, Peninsular Malaysia. The initial phase of this study involves analyzing all available data on parameters concerning the river, aiming to gain a comprehensive understanding of the overall water quality within the river basin. Through temporal analysis, it was determined that Mg, E. coli, SS, and DS emerge as critical factors affecting water quality in this river basin. Then, in terms of WQI calculation, feature importance method is used to identify the most important parameters that can be used to predict the WQI. Finally, an ensemble-based machine learning model is designed to predict the WQI using three parameters. Two ensemble ML approaches are chosen to predict the WQI in the study area and achieved a R² of 0.86 for RF-based regression and 0.85 for GB-based ML technique. Finally, this research proves that using only the biochemical oxygen demand (BOD), the chemical oxygen demand (COD) and percentage of dissolved oxygen (DO%), the WQI can be predicted accurately and almost 96 times out of 100 sample, the water class can be predicted using GB ensembled ML algorithm. Moving forward, stakeholders may opt to integrate this research into their analyses, potentially yielding economic reliability and time savings.

Urbanization is growing rapidly in Malaysia. Rapid urbanization has known to have several negative impacts towards hydrological cycle due to decreasing of pervious area and deterioration of water quality in stormwater runoff. One of the negative impacts of urbanization is the congestion of the stormwater drainage system and this situation leading to flash flood problem and water quality degradation. There are many urban stormwater management softwares available in the market such as Storm Water Drainage System design and analysis program (DRAINS), Urban Drainage and Sewer Model (MOUSE), InfoWorks River Simulation (InfoWork RS), Hydrological Simulation Program-Fortran (HSPF), Distributed Routing Rainfall-Runoff Model (DR3M), Storm Water Management Model (SWMM), XP Storm Water Management Model (XPSWMM), MIKE-SWMM, Quality-Quantity Simulators (QQS), Storage, Treatment, Overflow, Runoff Model (STORM), and Hydrologic Engineering Centre-Hydrologic Modelling System (HEC-HMS). In this paper, we are going to discuss briefly about several softwares and their functionality, accessibility, characteristics and components in the quantity analysis of the hydrological design software and compare it with MSMA Design Aid and Database. Green Infrastructure (GI) is one of the main topics that has widely been discussed all over the world. Every development in the urban area is related to GI. GI can be defined as green area build in the develop area such as forest, park, wetland or floodway. The role of GI is to improve life standard such as water filtration or flood control. Among the twenty models that have been compared to MSMA SME, ten models were selected to conduct a comprehensive review for this study. These are known to be widely accepted by water resource researchers. These ten tools are further classified into three major categories as models that address the stormwater management ability of GI in terms of quantity and quality, models that have the capability of conducting the economic analysis of GI and models that can address both stormwater management and economic aspects together.