A regional inundation early warning system is crucial to alleviating flood risks and reducing loss of life and property. This study aims to provide real-time multi-step-ahead forecasting of flood inundation maps during storm events for flood early warnings in inundation-prone regions. For decades, the Kemaman River Basin, located on the east coast of the West Malaysia Peninsular, has suffered from monsoon floods that have caused serious damage. The downstream region with an area of approximately 100 km² located on the east side of this basin is selected as the study area. We explore and implement a hybrid ANN-based regional flood inundation forecast system in the study area. The system combines two popular artificial neural networks—the self-organizing map (SOM) and the recurrent nonlinear autoregressive with exogenous inputs (RNARX)—to sequentially produce regional flood inundation maps during storm events. The results show that: (1) the 4 × 4 SOM network can effectively cluster regional inundation depths; (2) RNARX networks can accurately forecast the long-term (3–12 h) regional average inundation depths; and (3) the hybrid models can produce adequate real-time regional flood inundation maps. The proposed ANN-based model was shown to very quickly carry out multi-step-ahead forecasting of area-wide inundation depths with sufficient lead time (up to 12 h) and can visualize the forecasted results on Google Earth using user devices to help decision makers and residents take precautionary measures against flooding.

Flood disasters have had a great impact on city development. Early flood warning systems (EFWS) are promising countermeasures against flood hazards and losses. Machine learning (ML) is the kernel for building a satisfactory EFWS. This paper first summarizes the ML methods proposed in this special issue for flood forecasts and their significant advantages. Then, it develops an intelligent hydroinformatics integration platform (IHIP) to derive a user-friendly web interface system through the state-of-the-art machine learning, visualization and system developing techniques for improving online forecast capability and flood risk management. The holistic framework of the IHIP includes five layers (data access, data integration, servicer, functional subsystem, and end-user application) and one database for effectively dealing with flood disasters. The IHIP provides real-time flood-related data, such as rainfall and multi-step-ahead regional flood inundation maps. The interface of Google Maps fused into the IHIP significantly removes the obstacles for users to access this system, helps communities in making better-informed decisions about the occurrence of floods, and alerts communities in advance. The IHIP has been implemented in the Tainan City of Taiwan as the study case. The modular design and adaptive structure of the IHIP could be applied with similar efforts to other cities of interest for assisting the authorities in flood risk management.

The impact of Big Data (BD) creates challenges in selecting relevant and significant data to be used as criteria to facilitate flood management plans. Studies on macro domain criteria expand the criteria selection, which is important for assessment in allowing a comprehensive understanding of the current situation, readiness, preparation, resources, and others for decision assessment and disaster events planning. This study aims to facilitate the criteria identification and selection from a macro domain perspective in improving flood management planning. The objectives of this study are (a) to explore and identify potential and possible criteria to be incorporated in the current flood management plan in the macro domain perspective; (b) to understand the type of flood measures and decision goals implemented to facilitate flood management planning decisions; and (c) to examine the possible structured mechanism for criteria selection based on the decision analysis technique. Based on a systematic literature review and thematic analysis using the PESTEL framework, the findings have identified and clustered domains and their criteria to be considered and applied in future flood management plans. The critical review on flood measures and decision goals would potentially equip stakeholders and policy makers for better decision making based on a disaster management plan. The decision analysis technique as a structured mechanism would significantly improve criteria identification and selection for comprehensive and collective decisions. The findings from this study could further improve Malaysia Adaptation Index (MAIN) criteria identification and selection, which could be the complementary and supporting reference in managing flood disaster management. A proposed framework from this study can be used as guidance in dealing with and optimising the criteria based on challenges and the current application of Big Data and criteria in managing disaster events.

In this study, a regional climate model was used to dynamically downscale 15 future climate projections from three GCMs covering four emission scenarios (SRES B1, A1FI, A1B, A2) based on Coupled Model Intercomparison Project phase 3 (CMIP3) datasets to 6-km horizontal resolution over the whole Peninsular Malaysia. Impacts of climate change in the 21st century on the precipitation, air temperature, and soil water storage were assessed covering ten watersheds and twelve coastal regions. Then, by coupling a physical hydrology model with the regional climate model, the impacts of the climate change on river flows were assessed at the outlets of ten watersheds in Peninsular Malaysia. It was found that the increase in the 30-year mean annual precipitation from 1970–2000 to 2070–2100 will vary from 17.1 to 36.3 percent among the ten watersheds, and from 22.9 to 45.4 percent among twelve coastal regions. The ensemble average of the basin-average annual mean air temperature will increase about 2.52 °C to 2.95 °C from 2010 to 2100. In comparison to the historical period, the change in the 30-year mean basin-average annual mean soil water storage over the ten watersheds will vary from 0.7 to 10.9 percent at the end of 21st century, and that over the twelve coastal regions will vary from −1.7 to 15.8 percent. Ensemble averages of the annual mean flows of the 15 projections show increasing trends for the 10 watersheds, especially in the second half of the 21st century. In comparison to the historical period, the change in the 30-year average annual mean flows will vary from −2.1 to 14.3 percent in the early 21st century, 4.4 to 23.8 percent in the middle 21st century, and 19.1 to 45.8 percent in the end of 21st century.

In this work, an atmospheric water generator (AWG) system called the medium-scale AWG (MSAWG) was designed, fabricated, and tested in Universiti Sains Malaysia (USM) under the outdoor tropical climate conditions of Malaysia to investigate the amount of fresh water production in successive periods of 24 h. The MSAWG consisted of 18 thermoelectric cooler (TEC) units, 18 internal finned heat sinks, 18 external finned heat sinks, 18 direct current (DC) cooling fans, an air-conditioner mesh air filter, and an axial ventilation fan. It was found from the results that the hourly values of water production of MSAWG were proportional to the hourly rates of relative humidity, but they were inversely affected by the corresponding hourly values of the ambient temperature. Night-time and early morning were the most effective times to produce the highest amount of fresh water from Malaysia’s atmosphere using MSAWG, as the increase in the rates of relative humidity and the decrease in the values of ambient temperature occurred over these periods. Based on the varied hourly rates of relative humidity with the ranges between the minimum rate of 55% and maximum rate of 85%, an equation of Y = 0.2285X + 36.675 with R² of 0.9404 was achieved from the 48 h experimental work to estimate the water production of MSAWG in Malaysia, where Y and X were indicated as the rate of relative humidity and the value of water production, respectively, in this equation. In conclusion, the MSAWG produced a consecutive cumulative water volume of 3.432 and 6.997 L experimentally in the periods of 24 and 48 h, respectively. The estimated cost per liter for MSAWG was USD 0.466. Some water quality parameters of the fresh water produced by MSAWG were analysed in the laboratory, which showed that they met the World Health Organization (WHO) drinking water standards. Therefore, MSAWG can be employed as a sustainable alternative to generate annual daily fresh water from Malaysia’s tropical atmosphere and aid in mitigating the problem of unpredicted water shortage in the country.

This study aims to assess future water deficit and availability in Klang valley in the context of climate change. A surface water budget model for Klang river basin was developed using MIKE BASIN. The parameters for the Nedbør‐Afrstrømnings (NAM) model were determined and examined by trial-and-error approach and evaluated based on correlation coefficient (R), Nash–Sutcliffe efficiency (R_NS), and overall water balance error (WB_er). The climate change scenario assessment used downscaled projected future rainfall data from 18 Global Circulation Models for the period 2046–2065. The assessment of water availability was carried out for Klang Gates Dam. The calibration and validation results for NAM model were satisfactory. The reservoir model verification result showed that the observed and simulated dam level were slightly dissimilar, which may be due to insufficient input data, particularly rainfall data as rainfall stations were very sparse upstream of the dam. The water deficit analysis for 2046–2065 showed that there will be water shortage in most of the months. The results of this assessment will help decision-makers evaluate the water availability and existing water infrastructure capacity in the vicinity of Klang river basin, particularly with respect to future raw and treated water yield.