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Land subsidence hazard modeling: Machine learning to identify predictors and the role of human activities

Author:
Omid Rahmati
,
علی گل کاریان
,
Trent Biggs
,
Saskia Keesstra
,
Farnoush Mohammadi
,
Ioannis N. Daliakopoulos
,
Omid Rahmati
,
Ali Golkarian
,
Trent Biggs
,
Saskia Keesstra
,
Farnoush Mohammadi
,
Ioannis N. Daliakopoulos
Year
: 2019
Abstract: Land subsidence caused by land use change and overexploitation of groundwater is an example of mismanagement of natural resources, yet subsidence remains difficult to predict. In this study, the relationship between land subsidence features and geo-environmental factors is investigated by comparing two machine learning algorithms (MLA): maximum entropy (MaxEnt) and genetic algorithm rule-set production (GARP) algorithms in the Kashmar Region, Iran. Land subsidence features (N=79) were mapped using field surveys. Land use, lithology, the distance from traditional groundwater abstraction systems (Qanats), from afforestation projects, from neighboring faults, and the drawdown of groundwater level (DGL) (1991–2016) were used as predictive variables. Jackknife resampling showed that DGL, distance from afforestation projects, and distance from Qanat systems are major factors influencing land subsidence, with geology and faults being less important. The GARP

algorithm outperformed the MaxEnt algorithm for all performance metrics. The performance of both models, as measured by the area under the receiver-operator characteristic curve (AUROC), decreased from 88.9–94.4% to 82.5–90.3% when DGL was excluded as a predictor, though the performance of GARP was still good to excellent even without DGL. MLAs produced maps of subsidence risk with acceptable accuracy, both with and without data on groundwater drawdown, suggesting that MLAs can usefully inform efforts to manage subsidence in datascarce regions, though the highest accuracy requires data on changes in groundwater level.
DOI: 10.1016/j.jenvman.2019.02.020
URI: http://libsearch.um.ac.ir:80/fum/handle/fum/3368500
Keyword(s): Groundwater overexploitation,Subsidence,Land use change,Sustainability,Iran
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    Land subsidence hazard modeling: Machine learning to identify predictors and the role of human activities

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contributor authorOmid Rahmatien
contributor authorعلی گل کاریانen
contributor authorTrent Biggsen
contributor authorSaskia Keesstraen
contributor authorFarnoush Mohammadien
contributor authorIoannis N. Daliakopoulosen
contributor authorOmid Rahmatifa
contributor authorAli Golkarianfa
contributor authorTrent Biggsfa
contributor authorSaskia Keesstrafa
contributor authorFarnoush Mohammadifa
contributor authorIoannis N. Daliakopoulosfa
date accessioned2020-06-06T13:46:28Z
date available2020-06-06T13:46:28Z
date issued2019
identifier urihttp://libsearch.um.ac.ir:80/fum/handle/fum/3368500
description abstractLand subsidence caused by land use change and overexploitation of groundwater is an example of mismanagement of natural resources, yet subsidence remains difficult to predict. In this study, the relationship between land subsidence features and geo-environmental factors is investigated by comparing two machine learning algorithms (MLA): maximum entropy (MaxEnt) and genetic algorithm rule-set production (GARP) algorithms in the Kashmar Region, Iran. Land subsidence features (N=79) were mapped using field surveys. Land use, lithology, the distance from traditional groundwater abstraction systems (Qanats), from afforestation projects, from neighboring faults, and the drawdown of groundwater level (DGL) (1991–2016) were used as predictive variables. Jackknife resampling showed that DGL, distance from afforestation projects, and distance from Qanat systems are major factors influencing land subsidence, with geology and faults being less important. The GARP

algorithm outperformed the MaxEnt algorithm for all performance metrics. The performance of both models, as measured by the area under the receiver-operator characteristic curve (AUROC), decreased from 88.9–94.4% to 82.5–90.3% when DGL was excluded as a predictor, though the performance of GARP was still good to excellent even without DGL. MLAs produced maps of subsidence risk with acceptable accuracy, both with and without data on groundwater drawdown, suggesting that MLAs can usefully inform efforts to manage subsidence in datascarce regions, though the highest accuracy requires data on changes in groundwater level.
en
languageEnglish
titleLand subsidence hazard modeling: Machine learning to identify predictors and the role of human activitiesen
typeJournal Paper
contenttypeExternal Fulltext
subject keywordsGroundwater overexploitationen
subject keywordsSubsidenceen
subject keywordsLand use changeen
subject keywordsSustainabilityen
subject keywordsIranen
identifier doi10.1016/j.jenvman.2019.02.020
journal titleJournal of Environmental Managementfa
pages466-480
journal volume236
journal issue0
identifier linkhttps://profdoc.um.ac.ir/paper-abstract-1075487.html
identifier articleid1075487
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