Floods pose a significant risk for Bangladesh due to the country's geographical and climatic conditions. Traditional methods of predicting flood risk often fail to do justice to the complex dynamics of flood vulnerability in this region. This report provides a comprehensive overview of the use of advanced machine learning (ML) algorithms for flood risk prediction in Bangladesh. It addresses four primary areas of research: (a) factors influencing floods considered in ML-based studies, (b) performance metrics of ML models, and (c) research gaps and future challenges in ML-based flood risk prediction. This review identified 42 unique factors that influence flooding, with precipitation, distance from the river, elevation, orientation, land use and land cover, and soil type emerging as the most important. ML models showed high predictive performance with an accuracy of 82% to 95%, depending on the algorithm and dataset used. However, there are still problems with data quality and regional variability that affect the reliability of the models. To improve flood forecasting, integrating real-time data, combining ML with physical models and promoting stakeholder engagement are crucial. Future research should focus on improving data quality, combining ML and physical models, and integrating future climate projections to refine flood hazard mapping. By considering these aspects, this study contributes to improving flood risk assessment and sustainable flood management strategies in Bangladesh, which could reduce socio-economic losses and environmental damage -in high-risk areas by 20-30.

Ouagadougou, the capital city of Burkina Faso, is facing significant economic and social damages due to recurring floods. This study aimed to develop a flood susceptibility map for Ouagadougou using a logistic regression (LR) model and 14 flood conditioning factors, including elevation, slope, aspect, profile curvature, plan curvature, topographic position index (TPI), topographic roughness index (TRI), flow direction, topographic wetness index (TWI), distance to river, rainfall, land use/land cover (LULC), normalized difference vegetation index (NDVI) and soil type. A historical flood inventory map was created from household survey data, identifying 1026 flooded sites which were divided into a training dataset (70%) and a validation dataset (30%). The factors that had a statistically significant influence (p-value 1.96) at the 95% confidence level were, in order of importance, elevation, distance to river, rainfall, plan curvature and NDVI. The receiver operating characteristic (ROC) curve method was used to validate the model. The area under the curve (AUC) values of the model were 81% for the prediction rate and 82% for the success rate indicating its effectiveness in identifying areas susceptible to flooding. The results showed that 18.48% of the city is very high susceptible to flooding, 18.99% has high susceptibility, 18.43% has moderate susceptibility, and 19.98% and 24.18% have low and very low susceptibility, respectively. This research provides valuable information for policy makers to develop effective flood prevention and urban development strategies.

The Earth is currently experiencing severe economic and social consequences as a result of frequent floods. This study is crucial for effective risk management and mitigation, protecting lives and property from potential flood damage in the Deme watershed. This study endeavors to assess the efficacy of a logistic regression model in generating a flood susceptibility map for the Deme watershed in Ethiopia. Fourteen factors contributing to flooding were considered, including digital elevation model, slope, aspect, profile curvature, plane curvature, Topographic Position Index (TPI), Topographic Roughness Index (TRI), flow direction, Topographic WetnessIindex (TWI), distance to the river, rainfall, land use/land cover (LULC), Normalized Difference Vegetation Index (NDVI), and soil type. The receiver operating characteristic (ROC) curve method was employed to validate the model. The area under the curve (AUC) values for the model were determined to be 81% for the training dataset and 82% for the validation dataset, indicating its effectiveness in delineating flood-prone areas. The findings revealed that 18% of the watershed is very highly susceptible to flooding, 19% exhibits high susceptibility, 18% shows moderate susceptibility, while 20 and 24% have low and very low susceptibility, respectively. This research provides insights into comprehensive flood prevention and urban development strategies. HIGHLIGHTS center dot Flood susceptibility is determined by historical flood patterns and their influencing factors. center dot Logistic regression can be used to map flood-susceptible areas in a small watershed. center dot A multicollinearity test is necessary to ensure a linear relationship in flood conditioning factors. center dot Factors with high multicollinearity should be removed from models to improve prediction accuracy.

Flooding presents a formidable challenge in the United States, endangering lives and causing substantial economic damage, averaging around $5 billion annually. Addressing this issue and improving community resilience is imperative. This project employed machine learning techniques and publicly available data to explore the factors influencing flooding and to develop flood susceptibility maps at various spatial resolutions. Six machine learning algorithms, including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Knearest neighbor (KNN), Adaptive Boosting (Ada Boost), and Extreme Gradient Boosting (XGB) were used. Geospatial datasets comprising thirteen predictor variables and 1528 flood inventory data collected since 1996 were analyzed. The predictor variables are rainfall, elevation, slope, aspect, flow direction, flow accumulation, Topographic Wetness Index (TWI), distance from the nearest stream, evapotranspiration, land cover, impervious surface, land surface temperature, and hydrologic soil group. Five hundred twenty-eight non-flood data points were randomly created using a stream buffer for two scenarios. A total of 2964 data points were classified into flooded (1) and non-flooded (0) categories and used as a target. Overall, testing results showed that the XGB and RF models performed relatively well in both cases over multiple resolutions compared to other models, with an accuracy ranging from 0.82 to 0.97. Variable importance analysis depicted that predictor variables such as distance from the streams, hydrologic soil type, rainfall, elevation, and impervious surfaces significantly affected flood prediction, suggesting a strong association with the underlying driving process. The improved performance and the variation of the susceptible areas across two scenarios showed that considering predictor variables with multiple resolutions and appropriate non-flooding training points is critical for developing flood-susceptibility models. Furthermore, using tree-based ensemble algorithms like RF and XG boost in the stack generalization approach can help achieve robustness in a flood susceptibility model where multiple algorithms are being evaluated.

Floods are natural occurrences that pose serious risks to human life and the environment, including significant property and infrastructure damage and subsequent socioeconomic challenges. Recent floods in Cheongju County, South Korea have been linked to river overflow. In this study, we created flood susceptibility maps of Cheongju, South Korea using machine learning techniques including support vector regression (SVR), boosted tree (BOOST), and long short-term memory (LSTM) algorithms, based on environmental factors. Potentially influential variables were selected based on flood data gathered through field surveys; these included the slope, aspect, length -slope factor, wind exposition index, terrain wetness index, plan curvature, normalized difference water index, geology, soil drainage, soil depth, soil texture, land use type, and forest density. To improve the robustness of the flood susceptibility model, the most influential factors were identified using the frequency ratio method. Implementing machine learning techniques like SVR and BOOST produced encouraging outcomes, achieving the area under the curve (AUC) of 83.16% and 86.70% for training, and 81.65% and 86.43% for testing, respectively. While, the LSTM algorithm showed superior flood susceptibility mapping performance, with an AUC value of 87.01% for training and 86.91% for testing, demonstrating its robust performance and reliability in accurately assessing flood susceptibility. The results of this study enhance our understanding of flood susceptibility in South Korea and demonstrate the potential of the proposed approach for informing and guiding crucial regional policy decisions, contributing to a more resilient and prepared future.