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Solving the spatial extrapolation problem in flood susceptibility using hybrid machine learning, remote sensing, and

Huu Duy Nguyen¹, Quoc-Huy Nguyen², Quang-Thanh Bui²

¹Faculty of Geography, VNU University of Science, Vietnam National University, Hanoi, Vietnam. nguyenhuuduy@hus.edu.vn.

Environmental Science and Pollution Research International

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Summary

This study introduces advanced machine learning (ML) models, including deep neural networks (DNNs) with various optimization algorithms, to effectively address flood susceptibility modeling challenges. These models successfully solved the extrapolation problem, improving flood prediction accuracy in new regions.

Keywords:

Extrapolation problem Flood Machine learning Nghe an Quang Nam Vietnam

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Area of Science:

Environmental Science
Geospatial Analysis
Artificial Intelligence

Background:

Floods are a major natural hazard with increasing impacts on human life and economies.
Effective water resource management requires improved flood susceptibility modeling.
The extrapolation of flood models to new regions remains a significant challenge.

Purpose of the Study:

To develop and evaluate machine learning (ML) models for addressing the extrapolation problem in flood susceptibility modeling.
To compare the performance of deep neural network (DNN) models integrated with various optimization algorithms.

Main Methods:

Utilized deep neural networks (DNNs) combined with six optimization algorithms: Earthworm Optimization Algorithm (EOA), Wildebeest Herd Optimization (WHO), Biogeography-Based Optimization (BBO), Satin Bowerbird Optimizer (SBO), Grasshopper Optimization Algorithm (GOA), and Particle Swarm Optimization (PSO).
Applied models to flood susceptibility mapping in Quang Nam Province and tested extrapolation to Nghe An Province.
Evaluated model performance using Root Mean Square Error (RMSE), Receiver Operating Characteristic (ROC), Area Under the ROC Curve (AUC), and Accuracy (ACC).

Main Results:

All developed models demonstrated high performance in flood susceptibility mapping, with AUC values greater than 0.9.
The DNN-BBO model achieved the highest AUC (0.99), closely followed by DNN-WHO (0.99) and DNN-SBO (0.98).
The models successfully addressed the extrapolation problem, proving their capability to assess flood susceptibility in new geographical areas.

Conclusions:

The novel ML models effectively solve the extrapolation problem in flood susceptibility modeling.
These models offer a valuable reference for urban planners and decision-makers in coastal regions facing flood risks.
The developed approach can be adapted to evaluate flood susceptibility globally, enhancing disaster preparedness.