Using Neural Networks for Sustainable Land Use Prediction in Sumbawa Regency, Indonesia

Abstract

Agriculture is vital to Sumbawa Regency's economy, with key activities such as rice cultivation, corn production, onion farming, and cattle rearing. This study applies artificial neural networks (ANN) to predict land cover changes, focusing on agricultural land expansion. Using land cover datasets from ESRI, digital elevation model, and topographical maps, we analyzed land cover changes from 2017 to 2023 and generated future projections for 2050 with the MOLUSCE plugin in qGIS. The predictive model achieved an 85% accuracy rate when comparing 2023 actual data with predictions. Results indicate a significant increase in agricultural land cover by 2050. The key finding is that over a long-term period, the simulation of land use and land cover (LULC) change in Sumbawa reveals an increase of crop areas in the Lunyuk and Labangka Districts. This study highlights the effectiveness of ANN in land cover prediction and emphasizes the need for sustainable practices to balance agricultural expansion. AI-driven insights can aid policymakers in opti-mizing resource allocation and ensuring long-term environmental and economic stability in Sumbawa Regency. Future research should refine models and incorporate additional factors for improved accuracy.