Application of Nighttime Light Data Simulation Based on Multi-Indicator System and Machine Learning Model in Predicting Potentially Suitable Economic Development Areas: A Case Study of the Turpan–Hami Region - Guangpeng Zhang; Li Zhang; Yiyang Chen; Meng Chen; Jingjing Tian; Yin Wu (Remote Sensing)
In recent years, the accelerated urbanization process in China has led to increased land resource constraints and unregulated expansion, imposing significant pressure on ecosystems and the environment. As a critical node along the Silk Road Economic Belt, the Turpan–Hami region has experienced rapid urban development under policy support but faces challenges in resource utilization efficiency and sustainable development. To address these challenges, this study innovatively combines nighttime light remote sensing data to quantify urban economic development intensity and integrates socioeconomic and natural environment indicators based on previous research. Four tree-based ensemble learning models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost)—were employed to predict potential urban economic development suitability zones and their suitability intensity. The results show that the CatBoost model performed the best in suitability prediction, revealing significant spatial disparities: high-suitability areas are concentrated in regions with superior resource conditions and well-developed infrastructure, whereas areas with terrain constraints and inadequate infrastructure exhibit lower suitability. An analysis of changes over historical periods (2010, 2015, and 2020) demonstrates a gradual expansion of high-suitability regions over time.