Delineation of groundwater prospective resources by exploiting geo-spatial decision-making techniques for the Kingdom of Saudi Arabia
Saudi Arabia is a water deficit and an arid land with limited fresh water supplies. Owing to the burgeoning population, ascending living standards and desert agriculture, there is a tremendous stress on the current water reserves. Towards such ends, this paper introduces a remote sensing and geograp...
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| Veröffentlicht in: | Neural computing & applications 2019-09, Vol.31 (9), p.5379-5399 |
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| creator | Mumtaz, Rafia Baig, Shahbaz Kazmi, Syed Saqib Ali Ahmad, Farooq Fatima, Iram Ghauri, Badar |
| description | Saudi Arabia is a water deficit and an arid land with limited fresh water supplies. Owing to the burgeoning population, ascending living standards and desert agriculture, there is a tremendous stress on the current water reserves. Towards such ends, this paper introduces a remote sensing and geographic information system (GIS)-based multi-factor decision-making system to identify the regions in Saudi Arabia with groundwater potential. The proposed model is capable of producing groundwater suitability map built on the synthesis of several hydrological factors such as rainfall, slope, lithological features, land use/land cover, geological structures, soil type, lineaments density and drainage network using an analytical hierarchical process. The synthesis of these parameters has resulted in improved rendering of groundwater probable hotspots in the study area. According to obtained results, 0.66% (587.90 km
2
), 38.97% (34162.12 km
2
), 58.55% (51328.34 km
2
), 1.8% (1588.36 km
2
) and 0.004% (3.26 km
2
) of the study area was ranked as ‘Best’, ‘Very good’, ‘Good’, ‘Fair’ and ‘Poor’, respectively. The delineated zones were validated by mapping water wells on the suitability map. The results showed that 35.88 and 62.35% of the water wells belong to ‘Very good’ and ‘Good’ regions, while 0.58 and 1.17% belong to ‘Best’ and ‘Fair’ regions. This indicates that model-generated results were in good agreement with the ground truth and majority of the existing wells belong to ‘Very good’ to ‘Good’ regions of the generated map. This demonstrated the potential of remote sensing and GIS techniques to successfully discover groundwater plausible regions which could be further exploited to find suitable locations for groundwater withdrawal. |
| doi_str_mv | 10.1007/s00521-018-3370-z |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2299372864</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2299372864</sourcerecordid><originalsourceid>FETCH-LOGICAL-c374t-62696b7d794fea8906968dfaf6f8357b74a47047bb62413c901044a0d64b5d2a3</originalsourceid><addsrcrecordid>eNp1kE1PxCAQhonRxHX1B3gj8YwOhZb2uFk_4yYe1DOhLe2ydkuFVt29-7-lqYknTwRmnneYB6FzCpcUQFx5gDiiBGhKGBNA9gdoRjljhEGcHqIZZDxUE86O0Yn3GwDgSRrP0Pe1bkyrVW9si22Fa2eHtvxUvXa4c9Z3uujNh8ZOezu4Qnuc77D-6hpretPWuNaW-C7gqsGlLowPOWSr3sZar4t1a96HAFXW4X6t8WN4L-12nPSshtLghVO5UafoqFKN12e_5xy93t68LO_J6unuYblYkYIJ3pMkSrIkF6XIeKVVmkG4pmWlqqRKWSxywRUXwEWeJxGnrMiAAucKyoTncRkpNkcXU25YbfxXLzdhqzaMlFGUZUxEaVA0R3TqKoIA73QlO2e2yu0kBTnalpNtGWzL0bbcByaaGB9621q7v-T_oR8Bj4T2</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2299372864</pqid></control><display><type>article</type><title>Delineation of groundwater prospective resources by exploiting geo-spatial decision-making techniques for the Kingdom of Saudi Arabia</title><source>SpringerLink Journals - AutoHoldings</source><creator>Mumtaz, Rafia ; Baig, Shahbaz ; Kazmi, Syed Saqib Ali ; Ahmad, Farooq ; Fatima, Iram ; Ghauri, Badar</creator><creatorcontrib>Mumtaz, Rafia ; Baig, Shahbaz ; Kazmi, Syed Saqib Ali ; Ahmad, Farooq ; Fatima, Iram ; Ghauri, Badar</creatorcontrib><description>Saudi Arabia is a water deficit and an arid land with limited fresh water supplies. Owing to the burgeoning population, ascending living standards and desert agriculture, there is a tremendous stress on the current water reserves. Towards such ends, this paper introduces a remote sensing and geographic information system (GIS)-based multi-factor decision-making system to identify the regions in Saudi Arabia with groundwater potential. The proposed model is capable of producing groundwater suitability map built on the synthesis of several hydrological factors such as rainfall, slope, lithological features, land use/land cover, geological structures, soil type, lineaments density and drainage network using an analytical hierarchical process. The synthesis of these parameters has resulted in improved rendering of groundwater probable hotspots in the study area. According to obtained results, 0.66% (587.90 km
2
), 38.97% (34162.12 km
2
), 58.55% (51328.34 km
2
), 1.8% (1588.36 km
2
) and 0.004% (3.26 km
2
) of the study area was ranked as ‘Best’, ‘Very good’, ‘Good’, ‘Fair’ and ‘Poor’, respectively. The delineated zones were validated by mapping water wells on the suitability map. The results showed that 35.88 and 62.35% of the water wells belong to ‘Very good’ and ‘Good’ regions, while 0.58 and 1.17% belong to ‘Best’ and ‘Fair’ regions. This indicates that model-generated results were in good agreement with the ground truth and majority of the existing wells belong to ‘Very good’ to ‘Good’ regions of the generated map. This demonstrated the potential of remote sensing and GIS techniques to successfully discover groundwater plausible regions which could be further exploited to find suitable locations for groundwater withdrawal.</description><identifier>ISSN: 0941-0643</identifier><identifier>EISSN: 1433-3058</identifier><identifier>DOI: 10.1007/s00521-018-3370-z</identifier><language>eng</language><publisher>London: Springer London</publisher><subject>Arid lands ; Aridity ; Artificial Intelligence ; Computational Biology/Bioinformatics ; Computational Science and Engineering ; Computer Science ; Data Mining and Knowledge Discovery ; Decision making ; Fresh water ; Geographic information systems ; Ground truth ; Groundwater ; Hot spots (geology) ; Hydrology ; Image Processing and Computer Vision ; Land cover ; Land use ; Mapping ; Original Article ; Probability and Statistics in Computer Science ; Rainfall ; Remote sensing ; Soil structure ; Synthesis ; Water supply ; Water wells</subject><ispartof>Neural computing & applications, 2019-09, Vol.31 (9), p.5379-5399</ispartof><rights>The Natural Computing Applications Forum 2018</rights><rights>Neural Computing and Applications is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c374t-62696b7d794fea8906968dfaf6f8357b74a47047bb62413c901044a0d64b5d2a3</citedby><cites>FETCH-LOGICAL-c374t-62696b7d794fea8906968dfaf6f8357b74a47047bb62413c901044a0d64b5d2a3</cites><orcidid>0000-0002-0966-3957</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00521-018-3370-z$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00521-018-3370-z$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Mumtaz, Rafia</creatorcontrib><creatorcontrib>Baig, Shahbaz</creatorcontrib><creatorcontrib>Kazmi, Syed Saqib Ali</creatorcontrib><creatorcontrib>Ahmad, Farooq</creatorcontrib><creatorcontrib>Fatima, Iram</creatorcontrib><creatorcontrib>Ghauri, Badar</creatorcontrib><title>Delineation of groundwater prospective resources by exploiting geo-spatial decision-making techniques for the Kingdom of Saudi Arabia</title><title>Neural computing & applications</title><addtitle>Neural Comput & Applic</addtitle><description>Saudi Arabia is a water deficit and an arid land with limited fresh water supplies. Owing to the burgeoning population, ascending living standards and desert agriculture, there is a tremendous stress on the current water reserves. Towards such ends, this paper introduces a remote sensing and geographic information system (GIS)-based multi-factor decision-making system to identify the regions in Saudi Arabia with groundwater potential. The proposed model is capable of producing groundwater suitability map built on the synthesis of several hydrological factors such as rainfall, slope, lithological features, land use/land cover, geological structures, soil type, lineaments density and drainage network using an analytical hierarchical process. The synthesis of these parameters has resulted in improved rendering of groundwater probable hotspots in the study area. According to obtained results, 0.66% (587.90 km
2
), 38.97% (34162.12 km
2
), 58.55% (51328.34 km
2
), 1.8% (1588.36 km
2
) and 0.004% (3.26 km
2
) of the study area was ranked as ‘Best’, ‘Very good’, ‘Good’, ‘Fair’ and ‘Poor’, respectively. The delineated zones were validated by mapping water wells on the suitability map. The results showed that 35.88 and 62.35% of the water wells belong to ‘Very good’ and ‘Good’ regions, while 0.58 and 1.17% belong to ‘Best’ and ‘Fair’ regions. This indicates that model-generated results were in good agreement with the ground truth and majority of the existing wells belong to ‘Very good’ to ‘Good’ regions of the generated map. This demonstrated the potential of remote sensing and GIS techniques to successfully discover groundwater plausible regions which could be further exploited to find suitable locations for groundwater withdrawal.</description><subject>Arid lands</subject><subject>Aridity</subject><subject>Artificial Intelligence</subject><subject>Computational Biology/Bioinformatics</subject><subject>Computational Science and Engineering</subject><subject>Computer Science</subject><subject>Data Mining and Knowledge Discovery</subject><subject>Decision making</subject><subject>Fresh water</subject><subject>Geographic information systems</subject><subject>Ground truth</subject><subject>Groundwater</subject><subject>Hot spots (geology)</subject><subject>Hydrology</subject><subject>Image Processing and Computer Vision</subject><subject>Land cover</subject><subject>Land use</subject><subject>Mapping</subject><subject>Original Article</subject><subject>Probability and Statistics in Computer Science</subject><subject>Rainfall</subject><subject>Remote sensing</subject><subject>Soil structure</subject><subject>Synthesis</subject><subject>Water supply</subject><subject>Water wells</subject><issn>0941-0643</issn><issn>1433-3058</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp1kE1PxCAQhonRxHX1B3gj8YwOhZb2uFk_4yYe1DOhLe2ydkuFVt29-7-lqYknTwRmnneYB6FzCpcUQFx5gDiiBGhKGBNA9gdoRjljhEGcHqIZZDxUE86O0Yn3GwDgSRrP0Pe1bkyrVW9si22Fa2eHtvxUvXa4c9Z3uujNh8ZOezu4Qnuc77D-6hpretPWuNaW-C7gqsGlLowPOWSr3sZar4t1a96HAFXW4X6t8WN4L-12nPSshtLghVO5UafoqFKN12e_5xy93t68LO_J6unuYblYkYIJ3pMkSrIkF6XIeKVVmkG4pmWlqqRKWSxywRUXwEWeJxGnrMiAAucKyoTncRkpNkcXU25YbfxXLzdhqzaMlFGUZUxEaVA0R3TqKoIA73QlO2e2yu0kBTnalpNtGWzL0bbcByaaGB9621q7v-T_oR8Bj4T2</recordid><startdate>20190901</startdate><enddate>20190901</enddate><creator>Mumtaz, Rafia</creator><creator>Baig, Shahbaz</creator><creator>Kazmi, Syed Saqib Ali</creator><creator>Ahmad, Farooq</creator><creator>Fatima, Iram</creator><creator>Ghauri, Badar</creator><general>Springer London</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0002-0966-3957</orcidid></search><sort><creationdate>20190901</creationdate><title>Delineation of groundwater prospective resources by exploiting geo-spatial decision-making techniques for the Kingdom of Saudi Arabia</title><author>Mumtaz, Rafia ; Baig, Shahbaz ; Kazmi, Syed Saqib Ali ; Ahmad, Farooq ; Fatima, Iram ; Ghauri, Badar</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c374t-62696b7d794fea8906968dfaf6f8357b74a47047bb62413c901044a0d64b5d2a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Arid lands</topic><topic>Aridity</topic><topic>Artificial Intelligence</topic><topic>Computational Biology/Bioinformatics</topic><topic>Computational Science and Engineering</topic><topic>Computer Science</topic><topic>Data Mining and Knowledge Discovery</topic><topic>Decision making</topic><topic>Fresh water</topic><topic>Geographic information systems</topic><topic>Ground truth</topic><topic>Groundwater</topic><topic>Hot spots (geology)</topic><topic>Hydrology</topic><topic>Image Processing and Computer Vision</topic><topic>Land cover</topic><topic>Land use</topic><topic>Mapping</topic><topic>Original Article</topic><topic>Probability and Statistics in Computer Science</topic><topic>Rainfall</topic><topic>Remote sensing</topic><topic>Soil structure</topic><topic>Synthesis</topic><topic>Water supply</topic><topic>Water wells</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mumtaz, Rafia</creatorcontrib><creatorcontrib>Baig, Shahbaz</creatorcontrib><creatorcontrib>Kazmi, Syed Saqib Ali</creatorcontrib><creatorcontrib>Ahmad, Farooq</creatorcontrib><creatorcontrib>Fatima, Iram</creatorcontrib><creatorcontrib>Ghauri, Badar</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Neural computing & applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mumtaz, Rafia</au><au>Baig, Shahbaz</au><au>Kazmi, Syed Saqib Ali</au><au>Ahmad, Farooq</au><au>Fatima, Iram</au><au>Ghauri, Badar</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Delineation of groundwater prospective resources by exploiting geo-spatial decision-making techniques for the Kingdom of Saudi Arabia</atitle><jtitle>Neural computing & applications</jtitle><stitle>Neural Comput & Applic</stitle><date>2019-09-01</date><risdate>2019</risdate><volume>31</volume><issue>9</issue><spage>5379</spage><epage>5399</epage><pages>5379-5399</pages><issn>0941-0643</issn><eissn>1433-3058</eissn><abstract>Saudi Arabia is a water deficit and an arid land with limited fresh water supplies. Owing to the burgeoning population, ascending living standards and desert agriculture, there is a tremendous stress on the current water reserves. Towards such ends, this paper introduces a remote sensing and geographic information system (GIS)-based multi-factor decision-making system to identify the regions in Saudi Arabia with groundwater potential. The proposed model is capable of producing groundwater suitability map built on the synthesis of several hydrological factors such as rainfall, slope, lithological features, land use/land cover, geological structures, soil type, lineaments density and drainage network using an analytical hierarchical process. The synthesis of these parameters has resulted in improved rendering of groundwater probable hotspots in the study area. According to obtained results, 0.66% (587.90 km
2
), 38.97% (34162.12 km
2
), 58.55% (51328.34 km
2
), 1.8% (1588.36 km
2
) and 0.004% (3.26 km
2
) of the study area was ranked as ‘Best’, ‘Very good’, ‘Good’, ‘Fair’ and ‘Poor’, respectively. The delineated zones were validated by mapping water wells on the suitability map. The results showed that 35.88 and 62.35% of the water wells belong to ‘Very good’ and ‘Good’ regions, while 0.58 and 1.17% belong to ‘Best’ and ‘Fair’ regions. This indicates that model-generated results were in good agreement with the ground truth and majority of the existing wells belong to ‘Very good’ to ‘Good’ regions of the generated map. This demonstrated the potential of remote sensing and GIS techniques to successfully discover groundwater plausible regions which could be further exploited to find suitable locations for groundwater withdrawal.</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s00521-018-3370-z</doi><tpages>21</tpages><orcidid>https://orcid.org/0000-0002-0966-3957</orcidid></addata></record> |
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| ispartof | Neural computing & applications, 2019-09, Vol.31 (9), p.5379-5399 |
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| subjects | Arid lands Aridity Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Decision making Fresh water Geographic information systems Ground truth Groundwater Hot spots (geology) Hydrology Image Processing and Computer Vision Land cover Land use Mapping Original Article Probability and Statistics in Computer Science Rainfall Remote sensing Soil structure Synthesis Water supply Water wells |
| title | Delineation of groundwater prospective resources by exploiting geo-spatial decision-making techniques for the Kingdom of Saudi Arabia |
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