Spatial suitability evaluation based on multisource data and random forest algorithm: a case study of Yulin, China

Abstract

With a large population and rapid urbanization, there are still many challenges to optimize the ecological-agricultural-urban space. Here, taking Yulin City, situated on the Loess Plateau of China as a case in point, we explored the spatial suitability evaluation of ecological-agricultural-urban space. Building upon the Chinese government’s concept of “resource and environmental carrying capacity and territorial development suitability evaluation” (hereinafter referred to as “double evaluation”), this study applies machine learning to the planning of ecological-agricultural-urban space. It explores an intelligent evaluation method for land space patterns using multi-source data. Based on the random forest (RF) algorithm and geographic information system (GIS), resulting in evaluated spatial patterns for ecological-agricultural-urban in the Yulin area. The results showed the constructed random forest models achieved an accuracy of 93% for ecology, 90% for agriculture, and 92% for urban space in Yulin City on the test dataset. By means of suitability analysis, the results indicated that the extremely important ecological space were predominantly located in the southwestern and eastern regions of the study area, while suitable space for agricultural production were primarily scattered throughout the southeast. In contrast, suitable space for urban construction were concentrated mainly in the central part of the study area. The use of machine learning has proven to be effective in addressing multicollinearity among spatial evaluation factors across three different areas. By eliminating human subjectivity in weight assignment during evaluation, it introduces fresh perspectives for land space planning and status assessment. These findings may offer support for the scientific delineation of ecological-agricultural-urban space (three districts and three lines) in China.

Evaluation method for ecology-agriculture-urban spaces based on deep learning

“…To assess the accuracy of the deep learning model, we conducted validation on the constructed dataset using our network architecture. We compared the predictive performance of our model with five machine learning methods, namely, LR 32 , NB 22 , GBDT 33 , RF 21 , 41 , and ANN 42 . This evaluation was performed to validate the feasibility of the model in predicting the suitability of the three spatial zones in Yulin City.…”

“…In comparison to traditional expert ratings or other subjective weighting methods, this enhances the objectivity of the evaluation results, reinforcing the scientific and practical aspects of suitability assessment. Furthermore, a comparative validation is conducted with five other deep learning and machine learning models, including Ann and GBDT 21 , 32 , 33 , 41 , 50 . The results of the validation indicate that the performance of the deep learning model surpasses that of machine learning models.…”

“… Random Forest (RF) An ensemble learning method that mitigates overfitting by training multiple decision trees and aggregating their voting results. Used for both classification and regression, RF demonstrates good performance on high-dimensional and large-scale datasets 21 , 41 . Artificial Neural Network (Ann) A model designed based on neural network architecture, capable of complex nonlinear modeling through multiple layers of neurons.…”

“…Random Forest (RF) An ensemble learning method that mitigates overfitting by training multiple decision trees and aggregating their voting results. Used for both classification and regression, RF demonstrates good performance on high-dimensional and large-scale datasets 21 , 41 .…”