Coordinated analysis of county geological environment carrying capacity and sustainable development under remote sensing interpretation combined with integrated model.

Geological environment carrying capacity (GECC) is the key to regional sustainable development (RSD). The study has become an international heat due to frequent global geological disasters, which have resulted in socio-economic losses, weakened GECC. Therefore, this study combined with quantity of information (QI), random forest (RF) and XGBoost (XGB) algorithms, constructed a GECC assessment model in Xiuyan, China based on remote sensing (RS) and geographic information system (GIS) techniques. The validity of the model was verified by disaster information in two period. The results reveal that the indicator system of RS image can reflect the geological factors' influence on GECC more truthfully, and avoided duplication of indicators. The correlation coefficients of the indicators were all less than 0.9, which showed the validity of the indicator system. The QI_RF model performed best with high accuracy (0.96). The mean absolute error (MAE) is 0.098. The disaster management cost (DMC), elevation and rainfall were the main indicators with weights over 0.1. The GECC in study area is mainly balanced. Although the overloaded area was smallest, it was the key area to limit the RSD, which had been improved by adjusting the land or human activities planning. Compared with the results of geological disasters in two period, the disasters in overloaded area were reduced by 58%, which showed that the improvement measures under this model were effective. The method of GECC assessment based on RS and integrated model proposed in this study takes into account the common indicators that affect the development of geological disasters. It can provide reference for RSD under the influence of geological disasters and has universal applicability.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)