Article: Flood disaster prediction using multi-scale deep learning and neuro-fuzzy inference Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.41 pp.91 - 106 Abstract: Flood disaster prediction is crucial for disaster prevention and mitigation, but traditional models face dual challenges: insufficient feature extraction and difficulty quantifying uncertainty. This paper proposes multi-scale adaptive neuro-fuzzy inference system. It integrates a multi-scale convolutional feature pyramid network for hierarchical spatiotemporal feature extraction from remote sensing hydrological data with an adaptive neural fuzzy inference system handling rainfall-runoff nonlinear uncertainties. Using global flood alert system and tropical rainfall measuring mission data, experiments in five major river basins (Yangtze, Mississippi, etc.), selected to represent diverse climatic zones and hydrological regimes, show significantly improved 72-hour prediction accuracy, achieving 15-22% root mean square error reduction. Constructed confidence intervals cover 92% of extreme flood events. multi-scale adaptive neuro-fuzzy inference system provides a robust, interpretable tool for smart water management, integrable into real-time flood warning platforms. Inderscience Publishers - linking academia, business and industry through research

Title: Flood disaster prediction using multi-scale deep learning and neuro-fuzzy inference

Authors: Haonan Zhao; Tingjing Xia

Addresses: Chongqing Survey and Design Institute of Water Conservancy, Hydro Power and Construction Co., Ltd., Chongqing 401120, China ' Chongqing Survey and Design Institute of Water Conservancy, Hydro Power and Construction Co., Ltd., Chongqing 401120, China

Abstract: Flood disaster prediction is crucial for disaster prevention and mitigation, but traditional models face dual challenges: insufficient feature extraction and difficulty quantifying uncertainty. This paper proposes multi-scale adaptive neuro-fuzzy inference system. It integrates a multi-scale convolutional feature pyramid network for hierarchical spatiotemporal feature extraction from remote sensing hydrological data with an adaptive neural fuzzy inference system handling rainfall-runoff nonlinear uncertainties. Using global flood alert system and tropical rainfall measuring mission data, experiments in five major river basins (Yangtze, Mississippi, etc.), selected to represent diverse climatic zones and hydrological regimes, show significantly improved 72-hour prediction accuracy, achieving 15-22% root mean square error reduction. Constructed confidence intervals cover 92% of extreme flood events. multi-scale adaptive neuro-fuzzy inference system provides a robust, interpretable tool for smart water management, integrable into real-time flood warning platforms.

Keywords: flood disaster prediction; multi-scale feature fusion; neural fuzzy inference; spatio-temporal deep learning; uncertainty quantification.

DOI: 10.1504/IJICT.2025.149987

International Journal of Information and Communication Technology, 2025 Vol.26 No.41, pp.91 - 106

Received: 13 Aug 2025
Accepted: 27 Sep 2025
Published online: 20 Nov 2025 *

Title: Flood disaster prediction using multi-scale deep learning and neuro-fuzzy inference

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