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

A - Papers appearing in refereed journals

Arabameri, A., Saha, S., Roy, J., Tiefenbacher, J. P., Cerda, A., Biggs, T., Pradhan, B., Ngo, P. T. T. and Collins, A. L. 2020. A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility . Science of the Total Environment. 726, p. 138595. https://doi.org/10.1016/j.scitotenv.2020.138595

AuthorsArabameri, A., Saha, S., Roy, J., Tiefenbacher, J. P., Cerda, A., Biggs, T., Pradhan, B., Ngo, P. T. T. and Collins, A. L.
Abstract

Land subsidence (LS) is a significant problemthat 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 significancewas 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 bemonitored 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.

KeywordsEnsemble method; K-fold cross-validation (CV); Land-subsidence susceptibility; Semnan Plain
Year of Publication2020
JournalScience of the Total Environment
Journal citation726, p. 138595
Digital Object Identifier (DOI)https://doi.org/10.1016/j.scitotenv.2020.138595
Open accessPublished as non-open access
FunderBiotechnology and Biological Sciences Research Council
Funder project or codeS2N - Soil to Nutrition - Work package 3 (WP3) - Sustainable intensification - optimisation at multiple scales
Output statusPublished
Publication dates
Online12 Apr 2020
Publication process dates
Accepted07 Apr 2020
PublisherElsevier Science Bv
ISSN0048-9697

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