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An Automated Python...
An Automated Python Language-Based Tool for Creating Absence Samples in Groundwater Potential Mapping
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Rahmati, Omid (author)
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Moghaddam, Davoud Davoudi (author)
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Moosavi, Vahid (author)
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- Kalantari, Zahra (author)
- Stockholms universitet,Institutionen för naturgeografi,Stockholm Univ, Dept Phys Geog, SE-10691 Stockholm, Sweden
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Samadi, Mahmood (author)
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Lee, Saro (author)
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Dieu, Tien (author)
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(creator_code:org_t)
- 2019-06-09
- 2019
- English.
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In: Remote Sensing. - : MDPI AG. - 2072-4292. ; 11:11
- Related links:
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https://doi.org/10.3...
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https://www.mdpi.com...
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https://urn.kb.se/re...
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Geovetenskap och miljövetenskap (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences (hsv//eng)
Keyword
- groundwater
- spatial modeling
- SAS tool
- sampling strategy
- GIS
- LiDAR
- remote sensing
Publication and Content Type
- ref (subject category)
- art (subject category)
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