Seven geographical regions of China.

Land development intensity is a comprehensive indicator to measure the degree of saving and intensive land construction and economic production activities. It is also the result of the joint action of natural, social, economic, and ecological elements in land development and utilization. Scientific prediction of land development intensity has particular reference significance for future regional development planning and the formulation of reasonable land use policies. Based on the inter-provincial land development intensity and its influencing factors in China, this study applied four algorithms, XGBoost, random forest model, support vector machine, and decision tree, to simulate and predict the land development intensity, and then compared the prediction accuracy of the four algorithms, and also carried out hyperparameter adjustment and prediction accuracy verification. The results show that the model with the best prediction performance among the four algorithms is XGBoost, and its R2 and MSE between predicted and valid values are 95.66% and 0.16, respectively, which are higher than the other three models. During the training process, the learning curve of the XGBoost model exhibited low fluctuation and fast fitting. Hyperparameter tuning is crucial to exploit the model’s potential. The XGBoost model has the best prediction performance with the best hyperparameter combination of max_depth:19, learning_rate: 0.47, and n_estimatiors:84. This study provides some reference significance for the simulation of land development and utilization dynamics.土地开发强度是衡量土地建设节约集约程度与经济生产活动强度的综合指标，亦是自然、社会、经济与生态要素共同作用于土地开发利用过程的产物。对土地开发强度进行科学预测，对未来区域发展规划制定以及合理土地利用政策的出台具有重要参考价值。本研究基于我国省际土地开发强度数据及其影响因子，采用XGBoost、随机森林模型、支持向量机与决策树四种算法开展土地开发强度的模拟预测工作，对比了四种算法的预测精度，并完成了超参数调优与预测效果验证。研究结果表明，四种算法中预测性能最优的模型为XGBoost，其预测值与实测值间的决定系数（R², Coefficient of Determination）为95.66%，均方误差（MSE, Mean Squared Error）为0.16，各项指标均优于其余三种模型。训练过程中，XGBoost模型的学习曲线波动幅度小、拟合速度快。超参数调优对充分挖掘模型潜力至关重要，当超参数组合为max_depth:19、learning_rate:0.47、n_estimators:84时，XGBoost模型的预测性能达到最佳。本研究可为土地开发利用动态模拟相关工作提供参考借鉴。