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

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 sy...

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Veröffentlicht in:Remote sensing (Basel, Switzerland) Jg. 11; H. 11; S. 1375
Hauptverfasser: Rahmati, Omid, Moghaddam, Davoud Davoudi, Moosavi, Vahid, Kalantari, Zahra, Samadi, Mahmood, Lee, Saro, Tien Bui, Dieu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 2019
Schlagworte:
Accuracy
Artificial intelligence
Automation
Earth science
Geographic information systems
GIS
Groundwater
Groundwater data
Groundwater potential
Learning algorithms
LiDAR
Machine learning
Mapping
Methods
Model accuracy
Natural resources
Programming languages
Python
R&D
Random sampling
Regression analysis
Remote sensing
Research & development
sampling strategy
SAS tool
spatial modeling
Splines
Statistical analysis
Statistical methods
Statistical sampling
Studies
Trustworthiness
Watershed management
Iran
Vietnam
India
ISSN:2072-4292, 2072-4292
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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.
AbstractList 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.
Author Kalantari, Zahra
Samadi, Mahmood
Lee, Saro
Rahmati, Omid
Moghaddam, Davoud Davoudi
Moosavi, Vahid
Tien Bui, Dieu
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  surname: Tien Bui
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Snippet Although sampling strategy plays an important role in groundwater potential mapping and significantly influences model accuracy, researchers often apply a...
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StartPage 1375
SubjectTerms Accuracy
Artificial intelligence
Automation
Earth science
Geographic information systems
GIS
Groundwater
Groundwater data
Groundwater potential
Learning algorithms
LiDAR
Machine learning
Mapping
Methods
Model accuracy
Natural resources
Programming languages
Python
R&D
Random sampling
Regression analysis
Remote sensing
Research & development
sampling strategy
SAS tool
spatial modeling
Splines
Statistical analysis
Statistical methods
Statistical sampling
Studies
Trustworthiness
Watershed management
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