Agriculture and aquaculture land-use change prediction in five central coastal provinces of Vietnam using ANN, SVR, and SARIMA models

Abstract

Abstract Vietnam’s economy with agriculture and aquaculture still account for roughly 26% of the country’s gross domestic product, and nearly 70% of the Vietnamese population lives in rural areas; therefore, agriculture and aquaculture land use play a crucial role in the development process of Vietnam. Rapidly increasing population and infrastructure in rural areas and industrial zones lead to these land-use changes. Hence, these land-use change predictions are crucial for local authorities and the local people to make land-resource funds and set up planning. This article suggests support vector regression (SVR), artificial neural network (ANN), and seasonal autoregressive integrated moving average (SARIMA) methods to predict land-use change. By comparing the three models, the results indicate that almost all of the SVR models improve the accurate performance more than ANN and SARIMA in Quangtri, ThuThienHue, Danang, and Quảngnam provinces. Furthermore, the ANN model indicates more accurate forecasting than the SVR and SARIMA models in Quan Binh province. The result may be support for the Ministry of Natural Resources and Environment to conduct the land-use inventory and upgrade agriculture and aquaculture land-use change maps every 5 years. Afterward, the Department of Natural Resources and Environment’s provinces use the estimating database and update it manually.