In recent decades, Malaysia has become one of the world’s most urbanized nations, causing severe flash flooding. Urbanization should meet the population’s needs by increasing the development of paved areas, which has significantly changed the catchment’s hydrological and hydraulic characteristics. Therefore, the frequency of flash flooding in Malaysia’s urban areas has grown year after year. Numerous techniques have been used, including the statistical approach, modeling, and storm design methods, in flood simulation. This research integrated hydrology and hydraulic models to simulate the urban flood events in the Aur River catchment. The primary objective is to determine water level and forecast peak flow based on hydrological assessment in the drainage system using XPSWMM software. The rainfall data for 60 min was used for this study in the hydrological analysis by obtaining an intensity-duration-frequency curve and peak flow value (Q peak). XPSWMM is used to simulate the response of a catchment to rainfall events in which runoff, water depth profile, and outflow hydrograph are obtained. Peak runoff is also obtained from the modified rational method for validation purposes. The proposed method was verified by comparing the result with the standard method. This is essential to identify flash flooding, which can lead to efficient flood mitigation planning and management in the urban catchment. The increase in residential areas results in the alteration of time of concentration, water quantity, and flow rate. Thus, to mitigate present and future problems, the effects of urbanization on water resources and flood should be analyzed.

This study examines the benefits and drawbacks of autonomous public transit vehicles among 210 Indonesians. Some 25% of respondents knew nothing about driverless vehicles, whereas 14% did. The average 5-point Likert scale response was 3.12 (SD = 1.05), indicating intermediate expertise. Some 42% of respondents used autonomous vehicle sources such as public transport, 47% had no experience, and 11% were doubtful. The survey items’ Cronbach’s alpha score is 0.873, indicating strong internal consistency and reliability. Most respondents supported the deployment of autonomous road vehicles for public transportation and said they would improve public transportation quality and accessibility. Technical issues and legal liabilities worried responders. The mean scores for the seven autonomous car benefits were similar, showing that respondents did not strongly prefer any benefit. After assessing the socioeconomic status and concerns, the study indicated that people who saw greater benefits were more tolerant of autonomous vehicles. Most respondents also wanted a clearer explanation of their legal responsibilities in case of an accident, thought human operators should play a major role in the future, and supported government trials of autonomous vehicles before their widespread usage. The study’s findings can help policymakers and stakeholders increase public acceptance of new transportation solutions such as autonomous vehicles, and improve future mobility safety and sustainability.

Missing values in hydrological studies are a common issue for hydrologists, especially in statistical analyses as a complete dataset is required. This work evaluates the performance of the multiple imputations by chained equations (MICE) approach to predicting recurrence in streamflow datasets. To evaluate and verify the effectiveness of the MICE approach in treating missing streamflow data, complete historical daily streamflow data from 2012 to 2014 were used. Later, MICE methods coupled with multiple linear regression (MLR) were applied to restore streamflow rates in Malaysia’s Langat River basin from 1978 to 2016. The best estimation methods are validated with tests such as adjusted R-squared (Adj R²), residual standard error (RSE), and mean absolute percentage error (MAPE). The findings revealed that the classification and regression tree (CART) method combined with MLR outperformed the other approaches tested, with the highest Adj R² value and the lowest RSE and MAPE values observed regardless of missing conditions.

Climate change has often led to severe impact on the environment. This study aimed to investigate the monthly trends and linearity of meteorological parameters at four locations during the period from 1970 to 2016. These locations represent the south, north, east, and west of Peninsular Malaysia. The meteorological parameters used were monthly total precipitation (mm) and monthly average temperature (°C). To illustrate the methodology, the Mann–Kendall (MK) trend test and a non-parametric regression model were used. The MK trend test did not indicate significant trends in precipitation, but indicated a trend in temperature for all locations. The Sen value gives the amount of fluctuation of precipitation and temperature for every year. The results of the linearity test exhibited a linear trend for precipitation and temperature for most of the months throughout the study period. Thus, this study gives insights into the monthly trends of meteorological parameters, especially in Peninsular Malaysia.

Predicting future PM2.5 concentrations based on knowledge obtained from past observational data is very useful for predicting air pollution. This paper aims to develop a hybrid forecasting model using an Artificial Neural Network (ANN) and Triple Exponential Smoothing (TES) on clustered PM2.5 data from a HPR (High Pollution Region), MPR (Medium Pollution Region), and LPR (Low Pollution Region) in Malaysia. Historical PM2.5 concentrations in Malaysia from January 2018 to December 2019 were used to develop a hybrid model. The proposed hybrid model was then evaluated in terms of Mean Absolute Percentage Error (MAPE) values by comparing them with real PM2.5 data from the year 2020 in the HPR, MPR and LPR. The results showed that the hybrid model of ANN and TES presented the lowest RMSE (Root Mean Squared Error) (4.25–8.56 µg m–3), MAE (Mean Absolute Error) (2.51–4.95 µg m–3), MAPE (0.13–0.2%), and MASE (Mean Absolute Scaled Error) (1.45–2.01) in different areas of pollution compared with other models. The comparison between the ANN and TES hybrid models and the real PM2.5 data in 2020 showed that the models gave sufficient accuracy in the HPR and MPR with MAPE values of between 20% and 50%, while the LPR showed less accuracy due to the high value of MAPE of more than 50%. Overall, the hybrid model developed in this study opens up a new prediction method for air quality forecasting and is sufficiently accurate to be used as a tool for air quality management.

Fine particulate matter is one of the atmospheric contaminants that exist in the atmosphere. The purpose of this study is to evaluate spatial–temporal changes in PM_2.5 concentrations in the central and south zones of Peninsular Malaysia from 2019 to 2020. The study area involves twenty-one monitoring stations in the central and south zones of Peninsular Malaysia, using monthly and annual means of PM_2.5 concentrations. The spatial autocorrelation of PM_2.5 is calculated using Moran’s I, while three semi-variogram models are used to measure the spatial variability of PM_2.5. Three kriging methods, ordinary kriging (OK), simple kriging (SK), and universal kriging (UK), were used for interpolation and comparison. The results showed that the Gaussian model was more appropriate for the central zone (MSE = 14.76) in 2019, while the stable model was more suitable in 2020 (MSE = 19.83). In addition, the stable model is more appropriate for both 2019 (MSE = 12.68) and 2020 (8.87) for the south zone. Based on the performance indicator, universal kriging was chosen as the best interpolation method in 2019 and 2020 for both the central and south zone. In conclusion, the findings provide a complete map of the variations in PM_2.5 for two different zones, and show that interpolation methods such as universal kriging are beneficial and could be extended to the investigation of air pollution distributions in other areas of Peninsular Malaysia.