Earth fissure hazard prediction using machine learning models

Bahram Choubin; Amir Mosavi; Esmail Heydari Alamdarloo; Farzaneh Sajedi Hosseini; Shahaboddin Shamshirband; Kazem Dashtekian; Pedram Ghamisi

doi:10.1016/j.envres.2019.108770

Earth fissure hazard prediction using machine learning models

Environ Res. 2019 Dec;179(Pt A):108770. doi: 10.1016/j.envres.2019.108770. Epub 2019 Sep 23.

Authors

Bahram Choubin¹, Amir Mosavi², Esmail Heydari Alamdarloo³, Farzaneh Sajedi Hosseini³, Shahaboddin Shamshirband⁴, Kazem Dashtekian⁵, Pedram Ghamisi⁶

Affiliations

¹ Soil Conservation and Watershed Management Research Department, West Azarbaijan Agricultural and Natural Resources Research and Education Center, AREEO, Urmia, Iran.
² School of the Built Environment, Oxford Brookes University, Oxford, OX30BP, UK; Kalman Kando Faculty of Electrical Engineering, Obuda University, Budapest, Hungary.
³ Department of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran.
⁴ Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Viet Nam. Electronic address: shahaboddin.shamshirband@tdtu.edu.vn.
⁵ Yazd Agricultural and Natural Resources Research Center, AREEO, Yazd, Iran.
⁶ Exploration Devision, Helmholtz Institute Freiberg for Resource Technology, Helmholtz-Zentrum Dresden-Rossendorf Helmholtz Institute Freiberg for Resource Technology, Freiberg, Germany.

PMID: 31577962
DOI: 10.1016/j.envres.2019.108770

Abstract

Earth fissures are the cracks on the surface of the earth mainly formed in the arid and the semi-arid basins. The excessive withdrawal of groundwater, as well as the other underground natural resources, has been introduced as the significant causing of land subsidence and potentially, the earth fissuring. Fissuring is rapidly turning into the nations' major disasters which are responsible for significant economic, social, and environmental damages with devastating consequences. Modeling the earth fissure hazard is particularly important for identifying the vulnerable groundwater areas for the informed water management, and effectively enforce the groundwater recharge policies toward the sustainable conservation plans to preserve existing groundwater resources. Modeling the formation of earth fissures and ultimately prediction of the hazardous areas has been greatly challenged due to the complexity, and the multidisciplinary involved to predict the earth fissures. This paper aims at proposing novel machine learning models for prediction of earth fissuring hazards. The Simulated annealing feature selection (SAFS) method was applied to identify key features, and the generalized linear model (GLM), multivariate adaptive regression splines (MARS), classification and regression tree (CART), random forest (RF), and support vector machine (SVM) have been used for the first time to build the prediction models. Results indicated that all the models had good accuracy (>86%) and precision (>81%) in the prediction of the earth fissure hazard. The GLM model (as a linear model) had the lowest performance, while the RF model was the best model in the modeling process. Sensitivity analysis indicated that the hazardous class in the study area was mainly related to low elevations with characteristics of high groundwater withdrawal, drop in groundwater level, high well density, high road density, low precipitation, and Quaternary sediments distribution.

Keywords: Earth fissure; Geohazard; Hazard prediction; Machine learning.

MeSH terms

Environmental Monitoring / methods
Geological Phenomena*
Groundwater*
Machine Learning
Proportional Hazards Models*