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Open AccessArticle

An Automated Python Language-Based Tool for Creating Absence Samples in Groundwater Potential Mapping

1
Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City 70000, Viet Nam
2
Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City 70000, Viet Nam
3
Department of Watershed Management, Faculty of Agriculture and Natural Resources, Lorestan University, Khorramabad 68151-44316, Iran
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Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Tehran 46417-76489, Iran
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Department of Physical Geography and Bolin Centre for Climate Research, Stockholm University, SE-106 91 Stockholm, Sweden
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Faculty of Natural Resources, University of Tehran, Karaj 31587-77871, Iran
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Division of Geoscience Research Platform, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124 Gwahang-no, Yuseong-gu, Daejeon 305-350, Korea
8
Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam
*
Authors to whom correspondence should be addressed.
Remote Sens. 2019, 11(11), 1375; https://doi.org/10.3390/rs11111375
Received: 2 May 2019 / Revised: 30 May 2019 / Accepted: 5 June 2019 / Published: 9 June 2019
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Abstract

Although sampling strategy plays an important role in groundwater potential mapping and significantly influences model accuracy, researchers often apply a simple random sampling method to determine absence (non-occurrence) samples. In this study, an automated, user-friendly geographic information system (GIS)-based tool, selection of absence samples (SAS), was developed using the Python programming language. The SAS tool takes into account different geospatial concepts, including nearest neighbor (NN) and hotspot analyses. In a case study, it was successfully applied to the Bojnourd watershed, Iran, together with two machine learning models (random forest (RF) and multivariate adaptive regression splines (MARS)) with GIS and remotely sensed data, to model groundwater potential. Different evaluation criteria (area under the receiver operating characteristic curve (AUC-ROC), true skill statistic (TSS), efficiency (E), false positive rate (FPR), true positive rate (TPR), true negative rate (TNR), and false negative rate (FNR)) were used to scrutinize model performance. Two absence sample types were produced, based on a simple random method and the SAS tool, and used in the models. The results demonstrated that both RF (AUC-ROC = 0.913, TSS = 0.72, E = 0.926) and MARS (AUC-ROC = 0.889, TSS = 0.705, E = 0.90) performed better when using absence samples generated by the SAS tool, indicating that this tool is capable of producing trustworthy absence samples to improve groundwater potential models. View Full-Text
Keywords: groundwater; spatial modeling; SAS tool; sampling strategy; GIS; LiDAR; remote sensing groundwater; spatial modeling; SAS tool; sampling strategy; GIS; LiDAR; remote sensing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Rahmati, O.; Moghaddam, D.D.; Moosavi, V.; Kalantari, Z.; Samadi, M.; Lee, S.; Tien Bui, D. An Automated Python Language-Based Tool for Creating Absence Samples in Groundwater Potential Mapping. Remote Sens. 2019, 11, 1375.

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