首页|A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility

A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility

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  • NETL
  • NSTL
  • Elsevier
Land subsidence (LS) is a significant problem that can cause loss of life, damage property, and disrupt local economies. The Semnan Plain is an important part of Iran, where LS is a major problem for sustainable development and management. The plain represents the changes occurring in 40% of the country. We introduce a novel-ensemble intelligence approach (called ANN-bagging) that uses bagging as a meta- or ensemble-classifier of an artificial neural network (ANN) to predict LS spatially on the Semnan Plain in Semnan Province, Iran. The ensemble model's goodness-of-fit (to training data) and prediction accuracy (of the validation data) are compared to benchmarks set by ANN-bagging. A total of 96 locations of LS and 12 LS conditioning factors (LSCFs) were collected. Each feature in the LS inventory map (LSIM) was randomly assigned to one of four groups or folds, each comprising 25% of cases. The novel ensemble model was trained using 75% (3 folds) and validated with the remaining 25% (1 fold) in a four-fold cross-validation (CV) system, which is used to control for the effects of the random selection of the training and validation datasets. LSCFs for LS prediction were selected using the information-gain ratio and multi-collinearity test methods. Factor significance was evaluated using a random forest (RF) model. Groundwater drawdown, land use and land cover, elevation, and lithology were the most important LSCFs. Using the k-fold CV approaches, twelve LS susceptibility maps (LSSMs) were prepared as each fold employed all three models (ANN-bagging, ANN, and bagging). The LS susceptibility mapping showed that between 5.7% and 12.6% of the plain had very high LS susceptibility. All three models produced LS susceptibility maps with acceptable prediction accuracies and goodness-of-fits, but the best maps were produced by the ANN-bagging ensemble method. Overall, LS risk was highest in agricultural areas with high groundwater drawdown in the flat lowlands on quaternary sediments (Qcf). Groundwater extraction rates should be monitored and potentially limited in regions of severe or high LS susceptibility. This investigation details a novel methodology that can help environmental planners and policy makers to mitigate LS to help achieve sustainability.

Artificial neural network (ANN)BaggingEnsemble methodK-fold cross-validation (CV)Land-subsidence susceptibilitySemnan Plain

Alireza Arabameri、Sunil Saha、Jagabandhu Roy、John P. Tiefenbacher、Artemi Cerda、Trent Biggs、Biswajeet Pradhan、Phuong Thao Thi Ngo、Adrian L. Collins

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Department of Geomorphology, Tarbiat Modares University, Tehran 14117-13116, Iran

Department of Geography, University of Gour Banga, Malda 732101, West Bengal India

Department of Geography, Texas State University, San Marcos, TX 78666, USA

Soil Erosion and Degradation Research Group, Departament de Geografia, Universitat de Valencia, Blasco Ibanez, 28,46010 Valencia, Spain

Department of Geography, San Diego State University, San Diego, CA 92182, USA

Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW 2007, Australia , Department of Energy and Mineral Resources Engineering, Choongmu-gwan, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Republic of Korea

Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam

Sustainable Agriculture Sciences Department, Rothamsted Research, North Wyke, Okehampton, Devon EX20 2SB, UK

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2020

10.1016/j.scitotenv.2020.138595

Science of the Total Environment

Science of the Total Environment

EIISTP
ISSN：0048-9697
年,卷(期)：2020.726(Jul15)
  • 32
  • 132