This study is the first to demonstrate the interlinkages between several flash flood mitigation strategies and sustainable development goals (SDGs) by understanding the basins' morphometric characteristics. The basins' features were described by eighteen morphometric parameters, covering the watershed geometry, drainage network, and relief. The flash flood hazard degree (FFHD) was significantly (p < 0.05) correlated with texture ratio (R²=0.712), surface slope (R²=0.705), relief (R²=0.690), basin area (R²=0.666), ruggedness number (R²=0.615), and sinuosity index (R²=0.551). These parameters were used to define the sustainability of flash flood management actions, viz., protecting water bodies and livestock, harvesting freshwater from rainfall, restoring water-related ecosystems, implementing safe working areas, and applying hydro-electrical facilities for energy generation. Essential non-structural and structural measures were suggested to mitigate flood damages. The proposed mitigation measures showed high relationships with the environmental, economic, and social pillars of sustainability, reducing the negative impacts of flash floods on the fulfilment of SDGs.

Landslides and flash floods are significant natural hazards with substantial risks to human settlements and the environment, and understanding their interconnection is vital. This research investigates the hazards of landslides and floods in two adopted basins in the Yamaguchi and Shimane prefectures, Japan. This study utilized ten environmental variables alongside categories representing landslide-prone, non-landslide, flooded, and non-flooded areas. Employing a machine-learning approach, namely, a LASSO regression model, we generated Landslide Hazard Maps (LHM), Flood Hazard Maps (FHM), and a Composite Hazard Map (CHM). The LHM identified flood-prone low-lying areas in the northwest and southeast, while central and northwest regions exhibited higher landslide susceptibility. Both LHM and FHM were classified into five hazard levels. Landslide hazards predominantly covered high- to moderate-risk areas, since the high-risk areas constituted 38.8% of the study region. Conversely, flood hazards were mostly low to moderate, with high- and very high-risk areas at 10.49% of the entire study area. The integration of LHM and FHM into CHM emphasized high-risk regions, underscoring the importance of tailored mitigation strategies. The accuracy of the model was assessed by employing the Receiver Operating Characteristic (ROC) curve method, and the Area Under the Curve (AUC) values were determined. The LHM and FHM exhibited an exceptional AUC of 99.36% and 99.06%, respectively, signifying the robust efficacy of the model. The novelty in this study is the generation of an integrated representation of both landslide and flood hazards. Finally, the produced hazard maps are essential for policymaking to address vulnerabilities to landslides and floods.

Identifying regions susceptible to co-occurring flash floods and landslides is essential for devising effective mitigation strategies and minimizing future impacts, particularly in urbanized river basins within tropical and subtropical climates. This study introduces Memetic Programming (MP), an innovative data-driven technique, alongside Ridge Regression (RR) and Support Vector Regression (SVR), to produce highly accurate flood and landslide susceptibility maps for the Nishikigawa and Takatsu River basins in the western Chugoku region of Japan. MP, which is rarely applied in combined flood and landslide susceptibility mapping, demonstrated superior predictive performance and greater spatial consistency than conventional models, enabling more precise identification of high- and low-risk zones. MP performed risk assessments, identifying 75% of the basins as low-risk for flooding and only 12% as high-risk, thereby providing clearer guidance for targeted interventions in the future. In contrast, SVR tended to spatially overestimate moderate- and high-hazard zones, likely because of suboptimal parameter tuning and its sensitivity to outliers. Beyond its technical accuracy, MP offers a robust and flexible approach that enhances hazard mapping and supports informed decision making. The findings highlight that implementing mitigation measures based on MP results can directly contribute to achieving four primary SDGs and support three additional SDGs. Overall, this study underscores the potential of MP to advance sustainable risk management in urban river basins globally.