Cumulative probability and regression analysis of ecosystem disruption by an integrated mechanism of AI with FF-flood dynamical model

Introduction 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 integer-order 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: as process innovation.