Application of novel hybrid model for land subsidence susceptibility mapping

Abstract

Land subsidence (LS) is one of the challenging geo‐hazards in several regions of the world that is mainly caused by the overexploitation of groundwater and subsequent collapse of underground cavities. The prediction of LS is difficult due to limitations in monitoring, proper surveys, and knowledge related to the functioning and behaviour of the phenomenon. The application of remote sensing (RS) and global positioning system (GPS) for the proper location identification of LS in the ground and its implementation in susceptibility mapping can help to better constrain this geohazard. However, proper LS susceptibility maps to identify LS prone areas and to mitigate this problem in a way are generally lacking. Several methodologies have been developed for preparing LS susceptibility maps through RS techniques coupled with statistical and machine learning (ML) algorithms. Furthermore, different regions have unique natural and anthropogenic causes for the occurrence of LS, such as subsidence due to groundwater over‐exploitation, road construction, consolidation of alluvial soil, mining activities, and so forth, resulting in severe economic losses. The LS susceptibility mapping (LSSM) can help to prevent natural and human catastrophes and reduce the economic damages. ML techniques are becoming increasingly proficient at modelling such occurrences and have wide application in LSSM. This study compares the performances of single and hybrid ML models to predict and map the susceptible areas prone to LS. A combination of several algorithms helps to improve the overall results. In order to achieve this, the gradient‐boosting machine‐evolutionary genetic algorithm (GBM‐EG) was adopted in this study for LS assessment and compared to three algorithms, namely random forest, artificial neural networks, and alternating decision tree, that stand as commonly used benchmarks. This case study on LSSM was carried out in the Abozidabad watershed, Isfahan Province, Iran, and a total of 155 LS locations were identified by using GPS. Measures of 12 conditioning factors were compiled for each of the sites. These data were input into the models in five different training‐to‐validation configurations of sample ratios: D1 (90%:10%), D2 (80%:20%), D3 (70%:30%), D4 (60%:40%), and D5 (50%:50%). The performance of the algorithms were evaluated with the receiver operating characteristic curve and seed cell area index (SCAI). The results show that the best area under curve value (0.989) and SCAI value were achieved by the GBM‐EG hybrid model in an 80:20 ratio. The results demonstrate that LSSMs can be effectively produced to support planning and land use for management decisions.

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