Flash floods, rapid and devastating inundations of water, are increasingly linked to the intensifying effects of climate change, posing significant challenges for both vulnerable communities and sustainable environmental management. The primary goal of this research is to investigate and predict a Flood Susceptibility Map (FSM) for the Ibaraki prefecture in Japan. This research utilizes a Random Forest (RF) regression model and GIS, incorporating 11 environmental variables (involving elevation, slope, aspect, distance to stream, distance to river, distance to road, land cover, topographic wetness index, stream power index, and plan and profile curvature), alongside a dataset comprising 224 instances of flooded and non-flooded locations. The data was randomly classified into a 70 % training set for model development, with the remaining 30 % used for model validation through Receiver Operating Characteristics (ROC) curve analysis. The resulting map indicated that approximately two-thirds of the prefecture as exhibiting low to very low flood susceptibility, while approximately one-fifth of the region is categorized as high to very high flood susceptibility. Furthermore, the RF model achieved a noteworthy validation with an area under the ROC curve of 99.56 %. Ultimately, this FSM serves as a crucial tool for policymakers in guiding appropriate spatial planning and flood mitigation strategies.

Flash floods stand as a substantial peril linked to climate change, imposing a severe menace to both human existence and built structures. This study aims to assess and compare the effectiveness of four distinct machine learning (ML) methodologies in the production of flood susceptibility maps (FSMs) in Ibaraki prefecture, Japan. Additionally, the investigation aims to examine the influence of excluding plan and profile curvature factors on the accuracy of the resulting maps. The dataset comprised 224 spots, consisting of 112 flooded and 112 non-flooded locations, and 11 environmental factors. The models were trained using 70% of the dataset, while the remaining 30% was utilized for model evaluation using the ROC curve method. The results indicated that both the ANN-MLP and SVR models achieved notable accuracy, with area under curve values of 95.23% and 95.83% respectively. An intriguing observation was made when the plan and profile curvature factors were excluded, as it led to an improvement in the accuracy of the ANN-MLP model, resulting in an accuracy of 96.7%. Furthermore, the generated FSMs were classified into five distinct hazard levels. The northern region of the maps predominantly exhibited very low and low hazard levels, while areas located in the southern region, closer to main streams, demonstrated considerably higher hazard levels categorized as very high and high. Ultimately, this study marks novel endeavor to investigate the impact of the curvature factor on the precision of machine learning algorithms in the creation of FSMs, which serve as fundamental tools for subsequent investigations.

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.