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Open AccessArticle

Groundwater Augmentation through the Site Selection of Floodwater Spreading Using a Data Mining Approach (Case study: Mashhad Plain, Iran)

1
Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Noor 46414-356, Iran
2
Department of Water Resources Engineering & Center for Middle Eastern Studies, Lund University, Box 201, 221 00 Lund, Sweden
3
The Centre for Advanced Modeling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, Australia
4
Department of Energy and Mineral Resources Engineering, Choongmu-gwan, Sejong University, 209 Neungdong ro Gwangjin-gu, Seoul 05006, Korea
5
Department of Forestry, College of Natural Resources, Tarbiat Modares University, Noor 46414-356, Iran
*
Author to whom correspondence should be addressed.
Water 2018, 10(10), 1405; https://doi.org/10.3390/w10101405
Received: 22 August 2018 / Revised: 2 October 2018 / Accepted: 2 October 2018 / Published: 10 October 2018
(This article belongs to the Special Issue Flood Modelling: Regional Flood Estimation and GIS Based Techniques)
It is a well-known fact that sustainable development goals are difficult to achieve without a proper water resources management strategy. This study tries to implement some state-of-the-art statistical and data mining models i.e., weights-of-evidence (WoE), boosted regression trees (BRT), and classification and regression tree (CART) to identify suitable areas for artificial recharge through floodwater spreading (FWS). At first, suitable areas for the FWS project were identified in a basin in north-eastern Iran based on the national guidelines and a literature survey. Using the same methodology, an identical number of FWS unsuitable areas were also determined. Afterward, a set of different FWS conditioning factors were selected for modeling FWS suitability. The models were applied using 70% of the suitable and unsuitable locations and validated with the rest of the input data (i.e., 30%). Finally, a receiver operating characteristics (ROC) curve was plotted to compare the produced FWS suitability maps. The findings depicted acceptable performance of the BRT, CART, and WoE for FWS suitability mapping with an area under the ROC curves of 92, 87.5, and 81.6%, respectively. Among the considered variables, transmissivity, distance from rivers, aquifer thickness, and electrical conductivity were determined as the most important contributors in the modeling. FWS suitability maps produced by the proposed method in this study could be used as a guideline for water resource managers to control flood damage and obtain new sources of groundwater. This methodology could be easily replicated to produce FWS suitability maps in other regions with similar hydrogeological conditions. View Full-Text
Keywords: artificial recharge; data mining; floodwater spreading; GIS; groundwater artificial recharge; data mining; floodwater spreading; GIS; groundwater
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Naghibi, S.A.; Vafakhah, M.; Hashemi, H.; Pradhan, B.; Alavi, S.J. Groundwater Augmentation through the Site Selection of Floodwater Spreading Using a Data Mining Approach (Case study: Mashhad Plain, Iran). Water 2018, 10, 1405.

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