Land subsidence modelling using tree-based machine learning algorithms

Omid Rahmati; Fatemeh Falah; Seyed Amir Naghibi; Trent Biggs; Milad Soltani; Ravinesh C Deo; Artemi Cerdà; Farnoush Mohammadi; Dieu Tien Bui

doi:10.1016/j.scitotenv.2019.03.496

Land subsidence modelling using tree-based machine learning algorithms

Sci Total Environ. 2019 Jul 1:672:239-252. doi: 10.1016/j.scitotenv.2019.03.496. Epub 2019 Apr 2.

Authors

Omid Rahmati¹, Fatemeh Falah², Seyed Amir Naghibi³, Trent Biggs⁴, Milad Soltani⁵, Ravinesh C Deo⁶, Artemi Cerdà⁷, Farnoush Mohammadi⁸, Dieu Tien Bui⁹

Affiliations

¹ Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam. Electronic address: omid.rahmati@tdtu.edu.vn.
² Young Researchers and Elites Club, Khorramabad Branch, Islamic Azad University, Khorramabad, Iran.
³ Department of Watershed Management Engineering, Tarbiat Modares University, Mazandaran, Iran.
⁴ Department of Geography, San Diego State University, San Diego, CA 92182, USA.
⁵ Department of Watershed Management, Sari Agricultural Sciences and Natural Resources University, Sari, Iran.
⁶ School of Agricultural, Computational and Environmental Sciences, Centre for Sustainable Agricultural Systems & Centre for Applied Climate Sciences, Institute of Life Sciences and the Environment, University of Southern Queensland, Springfield, QLD 4300, Australia.
⁷ Soil Erosion and Degradation Research Group, Department of Geography, Valencia University, Blasco Ibàñez, 28, 46010, Valencia, Spain.
⁸ Department of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran.
⁹ Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam. Electronic address: buitiendieu@gmail.com.

PMID: 30959291
DOI: 10.1016/j.scitotenv.2019.03.496

Abstract

Land subsidence (LS) is among the most critical environmental problems, affecting both agricultural sustainability and urban infrastructure. Existing methods often use either simple regression models or complex hydraulic models to explain and predict LS. There are few studies that identify the risk factors and predict the risk of LS using machine learning models. This study compares four tree-based machine learning models for land subsidence hazard modelling at a study area in Hamadan plain (Iran). The study also analyzes the importance of six risk factors including topography (elevation, slope), geomorphology (distance from stream, drainage density), hydrology (groundwater drawdown) and lithology on LS. Thematic layers of each variable related to the LS phenomenon are prepared and utilized as the inputs to the four tree-based machine learning models, including the Rule-Based Decision Tree (RBDT), Boosted Regression Trees (BRT), Classification And Regression Tree (CART), and the Random Forest (RF) algorithms to produce a consolidated LS hazard map. The accuracy of the generated maps is then evaluated using the area under the receiver operating characteristic curve (AUC) and the True Skill Statistics (TSS). The RF approach had the lowest predictive error for mapping the LS hazard (i.e., AUC 96.7% for training, AUC 93.8% for validation, TSS 0.912 for training, TSS 0.904 for validation) followed by BRT. Groundwater drawdown was seen to be the most influential factor that contributed to land subsidence in the present study area, followed by lithology and distance from the stream network.

Keywords: Artificial intelligence; Environmental management; GIS; Hazard; Spatial analysis.