This study rigorously assesses Malaysia's development projects' alignment with the Sendai Framework for Disaster Risk Reduction 2015-2030 to identify synergies that can be improved, with a focus on flood risk management challenges and opportunities. The primary goal is to establish a flexible framework that integrates national and grassroots initiatives to advance economic, social, and environmental responsibility in conjunction with development objectives. As Malaysia undergoes rapid development, it is increasingly exposed to flood risks, exacerbated by climate change and urbanization. The Sendai Framework provides guidelines to mitigate these escalating threats. Nonetheless, there is a discernible gap in the existing literature that neither adequately evaluates the development patterns amplifying flood risks nor scrutinizes Malaysia's conformity with the Sendai Framework's objectives. Utilizing a systematic and scoping literature review in conjunction with a qualitative approach. The findings underscore that despite notable progress in Malaysia's flood risk management, significant deficiencies persist in policy execution, financial allocation, and public cognizance. This research refined the flood risk management conceptual framework to adapt to changing environmental contexts. Crucially, the proactive involvement of the federal government, local authorities from various Malaysian states, and the Department of Irrigation and Drainage Malaysia is indispensable in flood risk mitigation. While frameworks like the National Physical Plan 4 (NPP4) have improved, local planning must adapt them to local needs. Every development project should mandatorily embed a comprehensive flood risk assessment, ensuring alignment with national guidelines. This increases compliance and construction sector interest, elevating flood disaster risk reduction. This enables a future where innovation, professionalism, and knowledge improve life quality while prioritizing safety and sustainability. 

Flood Risk Management in Development Projects: A Review of Malaysian Perspective within the Sendai Framework for Disaster Risk Reduction 2015-2030

Flood resilience assessment is an important step for any community as it gives the actual scenario of its ability to resist and recover from flood disasters. However, operationalising and quantifying resilience is still a challenge. In Pakistan, very limited research has been done to assess community resilience to floods. The present study proposed an integrated flood resilience model called the “capacity-based flood resilience model (CapFlooR-M)”, which is based on, machine learning (ML), geographical information system (GIS), remote sensing (RS), and the analytical hierarchy process (AHP). The CapFlooR-M incorporates four main components—flood hazard susceptibility (Is), coping capacity (Cc), adaptive capacity (Ac), and transformative capacity (Tc). Random Forest (RF) and Support Vector Machine (SVM) models were used to create a flood susceptibility map, and the AHP was used to compute the relative scores of core capacities, such as Cc, Ac, and Tc and their respective maps were generated. Finally, the susceptibility map was integrated with Cc, Ac, and Tc maps via overlay analysis in GIS to develop a flood resilience map. The overall results reveal that the northwestern and southwestern parts (36.64%; 505 km2) of the study area have moderate to very high resilience, while the central and southeastern parts (63.46%; 877 km2) have very low to low resilience. The findings of this novel approach can support policymakers, land use planners, and other relevant stakeholders to build resilience against flood hazards. 

Development of a new integrated flood resilience model using machine learning with GIS-based multi-criteria decision analysis

Increasingly frequent intense rainfall events have caused flood disasters of metro systems worldwide. Flood management based on flood resilience is a novel strategy for dealing with floods. Nevertheless, limited research has been conducted on the correlation between metro systems and flood resilience. In this study, an index system-based framework is proposed to evaluate the flood resilience of metro station entrances, the selected indices are multidimensional, emcompassing external environment, entrance structure, socio-economic, and post-disaster response factors. The flood resilience assessment of metro station entrances is carried out via a case study of the Donghaochong Basin, Guangzhou, China. The pressure-state-response (PSR) model is used to establish the index system for the assessment of the flood resilience. Indices of inundation at the entrance sites are simulated and extracted using the InfoWorks ICM model. Two rainfall scenarios are chosen to simulate inundation, and the resilience results of metro station entrances in each scenario are compared. The results suggest that in the 200-year (200a) return period scenario, 70% of the entrances are at the high and highest resilience levels, whereas in the Zhengzhou "720" scenario, the proportion of that is 43.33%. The resilience of metro station entrances is significantly reduced under the higher rainfall scenario. However, factors unrelated to rainfall are found to mitigate the extent to which total resilience is reduced. Before and during a flood disaster, the indices indicate the resistance and adaptability of metro entrances to the disaster. After a flood disaster, they indicate the ability of entrances to recover to regular functioning. Specific steps should be implemented before and after a disaster to effectively enhance the flood resilience of metro entrances. This study provides valuable insights into enhancing the pertinence and effectiveness of flood disaster management at metro station entrances, and reducing flood damage to metro systems. 

Flood resilience assessment of metro station entrances based on the PSR model framework: A case study of the Donghaochong Basin, Guangzhou

Nepal, known for its challenging topography and fragile geology is confronted with the constant threat of floods leading to substantial socio-economic losses annually. However, the country's efforts in planning and managing flood risks remain insufficient, especially in the vulnerable Mohana-Khutiya River. Therefore, this study focused on the Mohana-Khutiya River and utilizes the Maximum Entropy (MaxEnt) model to comprehensively map flood susceptibility and fill crucial gaps in flood risk assessments. This study employed a combination of 10 geospatial environmental layers and field-based past flood inventory to implement the MaxEnt machine learning model for flood susceptibility modeling. The available past flood data were divided into two sets, with 75% allocated for model construction and the remaining 25% for model validation. This study demonstrated that the proximity of the river had a significant impact (33.1%) on the occurrence of the flood. Surprisingly, the amount of annual precipitation throughout the year exhibited no detectable contribution to the flood event in the study site. About 4.9% area came under the high flood susceptible zone followed by 12.75 % in the moderate zone and 82.34% in the low-risk zone. The model exhibited excellent performance with an Area Under Curve (AUC) value of 0.935 and a low standard deviation of 0.018, indicating accurate predictions and consistent precision. These results highlight the model's reliability and its significance for developing disaster management policy by local government in the study site. Future research should refine the MaxEnt model by including more variables, validating against observed flood events, and exploring integration with other flood modeling approaches. 

Flood susceptibility assessment using machine learning approach in the Mohana-Khutiya River of Nepal

Effective reduction of urban flood risk can be accomplished through structural mitigation measures such as underground drainage tunnels and implementing drainage practices, such as green infrastructure (GI) techniques. In this study, we employed an urban runoff model to quantitatively evaluate the impact of these flood mitigation measures and GI technology in reducing urban flood vulnerability (UFV). Specifically, we assessed the flood control capabilities of underground drainage tunnels and detention reservoirs, among others, exploring various combinations to formulate a comprehensive flood prevention plan (CFPP) for the studied basin. Additionally, our study analyzes the effectiveness of GI technologies, such as impervious area reduction (IAR) and ground retention (GR) and introduces a method for assessing their impact on flood-prone areas. To enhance flood mitigation strategies, we propose a hybrid approach that combines CFPP with GI techniques. The effectiveness of this hybrid strategy was assessed through comparisons with a basic scenario and multiple GI technology scenarios. As a result of applying the hybrid strategy proposed in this study, the reduction rates of peak flow (PF), flood volume (FV), and flooded area (FA) in this basin were up to 37.3%, 71.5%, and 93.6%, respectively. These findings offer valuable insights for designing strategies to enhance urban flood resilience in the face of a changing climate. 

Mitigating Urban Flood Hazards: Hybrid Strategy of Structural Measures

A review of the flood management: from flood control to flood resilience

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A review of the flood management: from flood control to flood resilience