TECHNOLOGY AND CODE article
Front. Environ. Sci.
Sec. Big Data, AI, and the Environment
Volume 13 - 2025 | doi: 10.3389/fenvs.2025.1630673
This article is part of the Research TopicAdvanced Applications of Artificial Intelligence and Big Data Analytics for Integrated Water and Agricultural Resource Management: Emerging Paradigms and MethodologiesView all 5 articles
Cumulative probability and Regression analysis of ecosystem disruption by an integrated mechanism of AI with FF-Flood dynamical model
Provisionally accepted
- 1Prince Sultan University, Riyadh, Saudi Arabia
- 2Qassim University, Qassim, Saudi Arabia
- 3INTI International University, Putra Nilai, Malaysia
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This article highlights the applications of artificial intelligence in the flood dynamics analysis with its effects on the ecosystem with the help of mathematical modeling and simulations.• Problem Statement: Flood prediction with control remains critical for all walks of lives. Due to nonlinear hydrological mechanism and delayed responses within natural systems, the integerorder models often fail to capture memory effects.• Results: A FF-Flood dynamical system is developed with five variables to capture the dynamics of flood more precisely. The theoretical results of the model ensure the existence of solution, uniqueness of solution, and stability analysis. Ecosystem disruption is inferred through dynamic water level changes, surface runoff and water contamination.• Methodology: A novel FF-Flood dynamical system is constructed which is integrating the surface storage, runoff, river flow, water level and flood area. Existence and boundedness are analytically verified with reference of fixed-point theory, and time-domain simulations demonstrate sensitivity patterns. The results are affirmed by the help of AI deep learning analysis.
Keywords: Flood dynamical system, Simulations, artificial intelligence, Probability, regression
Received: 18 May 2025; Accepted: 15 Oct 2025.
Copyright: © 2025 Khan, Alrebdi, Alzabut and Thinakaran. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Hasib Khan, hkhan@psu.edu.sa
Reem Alrebdi, r.rebdi@qu.edu.sa
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