Urban Land Use Changes Simulation with CA-ANN Model: A Case Study of Mae Sot District, Tak Province, Thailand

Abstract

To comprehend environmental changes and develop sustainable land management plans, it is crucial to thoroughly understand the complexities involved in land use change. This study investigated land use changes in Mae Sot district, Tak province, Thailand, in the years 2003, 2013, and 2023 using pixel-based supervised classification employing the Random Forest (RF) algorithm. The findings revealed that a substantial portion of the area is dedicated to agricultural purposes, accompanied by a decrease in forest areas and an increase in urban development. Furthermore, five spatial factors—elevation, slope, distance from the city center, distance from the main road, and population—were integrated as explanatory variables in training the cellular automata-artificial neuron network (CA-ANN) model to construct a land use change simulation for 2023. A comparison of the projected and actual land use maps for 2023 demonstrated a high level of agreement, with a kappa coefficient of 0.81, confirming the reliability of the model’s predictions. Subsequently, the CA-ANN model was used to forecast future land use for the year 2033. The projection suggested a significant expansion of built-up areas, primarily originating from the core of Mae Sot sub-district and spreading towards Tha Sai Luat, Mae Pa, and Mae Tao, and potentially extending northward along the national highway to Mae Kasa sub-district. Additionally, forested areas are anticipated to diminish, transitioning into agricultural zones, while certain existing agricultural regions are expected to be converted into urban spaces. These findings underscore the imminent expansion of urban areas within Mae Sot, emphasizing the necessity for well-thought-out planning to ensure long-term development.