Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 548327
Title Land subsidence hazard modeling : Machine learning to identify predictors and the role of human activities
Author(s) Rahmati, Omid; Golkarian, Ali; Biggs, Trent; Keesstra, Saskia; Mohammadi, Farnoush; Daliakopoulos, Ioannis N.
Source Journal of Environmental Management 236 (2019). - ISSN 0301-4797 - p. 466 - 480.
DOI https://doi.org/10.1016/j.jenvman.2019.02.020
Department(s) Soil, Water and Land Use
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2019
Keyword(s) Groundwater overexploitation - Iran - Land use change - Subsidence - Sustainability
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 data-scarce regions, though the highest accuracy requires data on changes in groundwater level.

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