Random forest and naïve Bayes approaches as tools for flash flood hazard susceptibility prediction, South Ras El-Zait, Gulf of Suez Coast, Egypt

Flood vulnerability and buildings’ flood exposure assessment in a densely urbanised city: comparative analysis of three scenarios using a neural network approach

An ensemble random forest tree with SVM, ANN, NBT, and LMT for landslide susceptibility mapping in the Rangit River watershed, India

Abstract Natural hazards and severe weather events are a matter of serious threat to humans, economic activities, and the environment. Flash floods are one of the extremely devastating natural events around the world. Consequently, the prediction and precise assessment of flash flood-prone areas are mandatory for any flood mitigation strategy. In this study, a new hybrid approach of machine learning (ML) algorithm and hydrologic indices opted to detect impacted and highly vulnerable areas. The obtained models were trained and validated using a total of 189 locations from Wadi Ghoweiba and surrounding area (case study). Various controlling factors including varied datasets such as stream transport index (STI), stream power index (SPI), lithological units, topographic wetness index (TWI), slope angle, stream density (SD), curvature, and slope aspect (SA) were utilized via hyper-parameter optimization setting to enhance the performance of the proposed model prediction. The hybrid machine learning (HML) model, developed by combining naïve Bayes (NïB) approach and hydrologic indices, was successfully implemented and utilized to investigate flash flood risk, sediment accumulation, and erosion predictions in the studied site. The synthesized new hybrid model demonstrated a model accuracy of 90.8% compared to 87.7% of NïB model, confirming the superior performance of the obtained model. Furthermore, the proposed model can be successfully employed in large-scale prediction applications. 

https://link.springer.com/content/pdf/10.1007/s11356-022-19903-7.pdf

Hybrid-based Bayesian algorithm and hydrologic indices for flash flood vulnerability assessment in coastal regions: machine learning, risk prediction, and environmental impact

Abstract The objective of the present study was to introduce a novel methodological approach for flash flood susceptibility modeling based on a stacking ensemble (SE) model. Two SE models, Random Forest (RF) and Artificial Neural Network (ANN) were developed, whereas LDA, CART, LR, k-NN and SVM were the basic models of the two SE models. The performance of the developed methodology was evaluated at the Island of Rhodes, Greece. The database included 54 flash floods locations and 14 flood-related parameters. The SE-RF model produced slightly higher predictive results, in terms of accuracy (0.844), kappa index (0.687) and the area under the receiver operating characteristic curve (0.870), followed by the SE-ANN with values of 0.812, 0.625 and 0.773, respectively. Overall, the study provides evidence about the higher accuracy SE models can achieve since they are capable of combining in an intelligent way a number of weak predictive models.

Flash flood susceptibility mapping using stacking ensemble machine learning models

An arid climate and lacking adequate flood management systems are the main reasons for flash flood events in arid and semi-arid areas. The study area is a part of the Sinai Peninsula in Egypt, Wadi Gharandal subjected to several flooded events during the past decades. The present study incorporated hydrologic indices and morpho-tectonic parameters to identify the potential sediment accumulation and erosion. Integration of hydrologic indices, stream power index, sediment transport, and topographic wetness indices with morpho-tectonic parameters to produce and detect erosion and sedimentation associated with flash floods is the main objective. Consequently, the geographic information system (GIS) and the fuzzy k-means clustering algorithm were implemented for spatial data classification and management. The applied method revealed that the hydrologic and morphometric parameters play major roles in flash flood contributing factors. The low slope areas are associated with low run-off connected with a high sediment accumulation. Conversely, a high level of erosion is encountered in the steeper slope areas. Furthermore, terrain and lithology are decisive in sediment accumulation and erosion risk in the study area. The present study demonstrates the hydrologic and morpho-tectonic parameters with remote sensing data set are efficient tools in evaluating flash floods, potential erosion, and management, supporting the urban planner for future development.

H. Orabi Orabi

Papers

An integrated approach for evaluating the flash flood risk and potential erosion using the hydrologic indices and morpho-tectonic parameters