LULC changes to riverine flooding: A case study on the Jamuna River, Bangladesh using the multilayer perceptron model

Abstract

In riverine nations like Bangladesh, floods are frequent, bringing disaster that impairs people's lives and the national economy. Due to climate change, uncontrolled growth, rapid urbanization, expansion of agriculture plantations, and other factors influence Land-Use-Land-Cover (LULC) changes which can make flooding conditions more unpredictable. The aim of this study is to examine the relationship between changes in LULC and floods in the Jamuna River over a 30-year period from 1990 to 2020 through a quantitative correlational approach. Due to spatial nonlinearity, the relationship between LULC changes and other flood-influencing factors of different areas may vary with flooding. As a result, applying a global regression model can produce biased results in certain locations. We employed the multilayer perceptron neural network and Geographically Weighted Regression (GWR) method to solve this issue. The GWR method applies local regression based on carefully chosen criteria to handle spatial variability. A multilayer perceptron neural network was used to determine the potential for riverine flooding. The study's findings revealed that LULC changes in the area far from the river had a significant impact on changes in riverine flooding, as shown by the fact that 29.71% of those locations had a Pearson coefficient greater than 0.75. In contrast, LULC changes near the river did not strongly correlate with changes in riverine flood potential because 19.83% of those locations had a very low Pearson coefficient of less than 0.75.