Integrated model for land-use transformation analysis based on multi-layer perception neural network and agent-based model

Abstract

The efficacy of land-use changes on aquatic ecosystems has been extensively studied in recent decades. Water resource management needs to understand the relationship between land-use change patterns and water quality, especially in urban areas. Hence, recognizing spatial-temporal changes in land use is required for sustainable development and proper water resource management. This research has developed an integrated model based on agent-based model (ABM) and multi-layer perceptron (MLP) neural network technique to predict the future land-use transformation tested on the North Ahvaz watershed, Iran. Random forest-supervised classification technique was applied to derive the land-use maps using Landsat 1989, 2004, and 2019 images in the Google Earth Engine (GEE) platform. The overall accuracy of classified land-use images was 0.82, 0.81, and 0.84, respectively, with the kappa coefficient of 0.74, 0.72, and 0.78. Land-use change analysis and generating transition potential maps were carried out in land change modeler (LCM) through MLP based on seven driving factors. Then, the land-use map for 2019 (for validation) and 2040 was simulated using the transition potential map and an agent-based approach. The ABM scenario was farmers' and urban landowners' decisions to convert undeveloped and unprotected lands to residential lands. The results showed that residential areas and pasture lands would grow by 67.96 km² and 64.63 km², and agricultural and barren lands would degrade about 84.19 km² and 47.98 km² during 2019-2040, respectively. Predicting land-use change through the integrated MLP-ABM model may be used to evaluate the effects of land-use change coming out of human decision-making.

Keywords: Agent-based model; Google Earth Engine; Human decision-making; Land-use change; Multi-layer perceptron (MLP); Transition potential maps.