Hybrid signal processing/machine learning and PSO optimization model for conjunctive management of surface–groundwater resources
Conjunctive management of surface–groundwater resources systems by means of mathematical optimization–simulation techniques becomes an important issue for sustainable water resources development, namely in water-scarce regions. In this study, the particle swarm optimization (PSO) method has been cou...
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description | Conjunctive management of surface–groundwater resources systems by means of mathematical optimization–simulation techniques becomes an important issue for sustainable water resources development, namely in water-scarce regions. In this study, the particle swarm optimization (PSO) method has been coupled with a hybrid wavelet/ANFIS–fuzzy C-means (FCM) simulation model to determine the optimal agricultural irrigation water allocation in the Miandarband plain, western Iran. Firstly, the optimal amount of conveyed water (CW) from the Gavoshan Dam into the plain is determined by constrained PSO. The constraints are the long-term minimum monthly exceedance streamflows that are estimated for different exceedance probabilities—with a 70% value found to best reflect the average annual river inflow of 3.4 m
3
/s into the dam—using the two-parameter Weibull distribution as well as the classical Weibull nonparametric plotting position method. Then, based on the politically prioritized proportions of the dam’s allocated water for domestic, environmental and agricultural uses, as well as the share of the plain devoted to agriculture, the optimal monthly CW available for the plain (= 112 MCM/a) is obtained. However, the subsequent estimation of the irrigation water request (IWR) (= 265.8 MCM/a), calculated by the FAO-56 method and using empirical crop coefficients of the present agricultural pattern in the plain, indicates that there is an irrigation water deficit of 153.1 MCM/a that must be made up by groundwater withdrawal (GW), in a way that neither waterlogging nor severe drop conditions in groundwater levels (GL) will occur. The latter are then calculated by the hybrid wavelet/ANFIS (FCM) model, wherefore good performance indicators R
2
and RMSE, equal to 0.98 and 0.21 m and 0.94 and 0.31 m in the training and testing phases, respectively, are obtained. Finally, PSO and the hybrid model are coupled to simulate the GL fluctuations—with the above GL constraints—under conjunctive use of the optimal surface (CW) and groundwater resources (GW) in the Miandarband plain. In conclusion, the innovative coupled simulation/optimization model turns out to be a very useful tool for optimal and sustainable conjunctive management of surface–groundwater resources in an irrigation area. |
doi_str_mv | 10.1007/s00521-020-05553-8 |
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3
/s into the dam—using the two-parameter Weibull distribution as well as the classical Weibull nonparametric plotting position method. Then, based on the politically prioritized proportions of the dam’s allocated water for domestic, environmental and agricultural uses, as well as the share of the plain devoted to agriculture, the optimal monthly CW available for the plain (= 112 MCM/a) is obtained. However, the subsequent estimation of the irrigation water request (IWR) (= 265.8 MCM/a), calculated by the FAO-56 method and using empirical crop coefficients of the present agricultural pattern in the plain, indicates that there is an irrigation water deficit of 153.1 MCM/a that must be made up by groundwater withdrawal (GW), in a way that neither waterlogging nor severe drop conditions in groundwater levels (GL) will occur. The latter are then calculated by the hybrid wavelet/ANFIS (FCM) model, wherefore good performance indicators R
2
and RMSE, equal to 0.98 and 0.21 m and 0.94 and 0.31 m in the training and testing phases, respectively, are obtained. Finally, PSO and the hybrid model are coupled to simulate the GL fluctuations—with the above GL constraints—under conjunctive use of the optimal surface (CW) and groundwater resources (GW) in the Miandarband plain. In conclusion, the innovative coupled simulation/optimization model turns out to be a very useful tool for optimal and sustainable conjunctive management of surface–groundwater resources in an irrigation area.</description><identifier>ISSN: 0941-0643</identifier><identifier>EISSN: 1433-3058</identifier><identifier>DOI: 10.1007/s00521-020-05553-8</identifier><language>eng</language><publisher>London: Springer London</publisher><subject>Artificial Intelligence ; Computational Biology/Bioinformatics ; Computational Science and Engineering ; Computer Science ; Constraints ; Data Mining and Knowledge Discovery ; Groundwater ; Groundwater levels ; Image Processing and Computer Vision ; Irrigation ; Irrigation water ; Machine learning ; Optimization ; Optimization models ; Original Article ; Particle swarm optimization ; Probability and Statistics in Computer Science ; Runoff ; Signal processing ; Simulation ; Sustainable development ; Water resources development ; Weibull distribution</subject><ispartof>Neural computing & applications, 2021-07, Vol.33 (13), p.8067-8088</ispartof><rights>Springer-Verlag London Ltd., part of Springer Nature 2021</rights><rights>Springer-Verlag London Ltd., part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-814821f87fc3580a6758dd81b04c2f9c66b755288b86c040bddd8e1e416206d83</citedby><cites>FETCH-LOGICAL-c319t-814821f87fc3580a6758dd81b04c2f9c66b755288b86c040bddd8e1e416206d83</cites><orcidid>0000-0002-7147-150X</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/s00521-020-05553-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00521-020-05553-8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,778,782,27911,27912,41475,42544,51306</link.rule.ids></links><search><creatorcontrib>Zare, Mohammad</creatorcontrib><creatorcontrib>Koch, Manfred</creatorcontrib><title>Hybrid signal processing/machine learning and PSO optimization model for conjunctive management of surface–groundwater resources</title><title>Neural computing & applications</title><addtitle>Neural Comput & Applic</addtitle><description>Conjunctive management of surface–groundwater resources systems by means of mathematical optimization–simulation techniques becomes an important issue for sustainable water resources development, namely in water-scarce regions. In this study, the particle swarm optimization (PSO) method has been coupled with a hybrid wavelet/ANFIS–fuzzy C-means (FCM) simulation model to determine the optimal agricultural irrigation water allocation in the Miandarband plain, western Iran. Firstly, the optimal amount of conveyed water (CW) from the Gavoshan Dam into the plain is determined by constrained PSO. The constraints are the long-term minimum monthly exceedance streamflows that are estimated for different exceedance probabilities—with a 70% value found to best reflect the average annual river inflow of 3.4 m
3
/s into the dam—using the two-parameter Weibull distribution as well as the classical Weibull nonparametric plotting position method. Then, based on the politically prioritized proportions of the dam’s allocated water for domestic, environmental and agricultural uses, as well as the share of the plain devoted to agriculture, the optimal monthly CW available for the plain (= 112 MCM/a) is obtained. However, the subsequent estimation of the irrigation water request (IWR) (= 265.8 MCM/a), calculated by the FAO-56 method and using empirical crop coefficients of the present agricultural pattern in the plain, indicates that there is an irrigation water deficit of 153.1 MCM/a that must be made up by groundwater withdrawal (GW), in a way that neither waterlogging nor severe drop conditions in groundwater levels (GL) will occur. The latter are then calculated by the hybrid wavelet/ANFIS (FCM) model, wherefore good performance indicators R
2
and RMSE, equal to 0.98 and 0.21 m and 0.94 and 0.31 m in the training and testing phases, respectively, are obtained. Finally, PSO and the hybrid model are coupled to simulate the GL fluctuations—with the above GL constraints—under conjunctive use of the optimal surface (CW) and groundwater resources (GW) in the Miandarband plain. In conclusion, the innovative coupled simulation/optimization model turns out to be a very useful tool for optimal and sustainable conjunctive management of surface–groundwater resources in an irrigation area.</description><subject>Artificial Intelligence</subject><subject>Computational Biology/Bioinformatics</subject><subject>Computational Science and Engineering</subject><subject>Computer Science</subject><subject>Constraints</subject><subject>Data Mining and Knowledge Discovery</subject><subject>Groundwater</subject><subject>Groundwater levels</subject><subject>Image Processing and Computer Vision</subject><subject>Irrigation</subject><subject>Irrigation water</subject><subject>Machine learning</subject><subject>Optimization</subject><subject>Optimization models</subject><subject>Original Article</subject><subject>Particle swarm optimization</subject><subject>Probability and Statistics in Computer Science</subject><subject>Runoff</subject><subject>Signal processing</subject><subject>Simulation</subject><subject>Sustainable development</subject><subject>Water resources development</subject><subject>Weibull distribution</subject><issn>0941-0643</issn><issn>1433-3058</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp9UEtKBDEQDaLgOHoBVwHXrZXvZJYi_kBQUNchnU63GaaTMelWdCVewRt6EqMjuHNVUO9T9R5C-wQOCcDsKAMISiqgUIEQglVqA00IZ6xiINQmmsCcF1hyto12cl4AAJdKTND7xUudfIOz74JZ4lWK1uXsQ3fUG_vgg8NLZ1IoC2xCg29ur3FcDb73r2bwMeA-Nm6J25iwjWExBjv4J4d7E0znehcGHFucx9Qa6z7fProUx9A8m8ElnFyOYyrXdtFWa5bZ7f3OKbo_O707uaiurs8vT46vKsvIfKgU4YqSVs1ay4QCI2dCNY0iNXBL27mVsp4JQZWqlbTAoW4K6ojjRFKQjWJTdLD2LSEfR5cHvSgPlNRZU8G5lIQLWlh0zbIp5pxcq1fJ9ya9aAL6u2u97lqXrvVP1_rbmq1FuZBD59Kf9T-qLzwHhQo</recordid><startdate>20210701</startdate><enddate>20210701</enddate><creator>Zare, Mohammad</creator><creator>Koch, Manfred</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/0000-0002-7147-150X</orcidid></search><sort><creationdate>20210701</creationdate><title>Hybrid signal processing/machine learning and PSO optimization model for conjunctive management of surface–groundwater resources</title><author>Zare, Mohammad ; Koch, Manfred</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-814821f87fc3580a6758dd81b04c2f9c66b755288b86c040bddd8e1e416206d83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Artificial Intelligence</topic><topic>Computational Biology/Bioinformatics</topic><topic>Computational Science and Engineering</topic><topic>Computer Science</topic><topic>Constraints</topic><topic>Data Mining and Knowledge Discovery</topic><topic>Groundwater</topic><topic>Groundwater levels</topic><topic>Image Processing and Computer Vision</topic><topic>Irrigation</topic><topic>Irrigation water</topic><topic>Machine learning</topic><topic>Optimization</topic><topic>Optimization models</topic><topic>Original Article</topic><topic>Particle swarm optimization</topic><topic>Probability and Statistics in Computer Science</topic><topic>Runoff</topic><topic>Signal processing</topic><topic>Simulation</topic><topic>Sustainable development</topic><topic>Water resources development</topic><topic>Weibull distribution</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zare, Mohammad</creatorcontrib><creatorcontrib>Koch, Manfred</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 (ProQuest)</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>Zare, Mohammad</au><au>Koch, Manfred</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Hybrid signal processing/machine learning and PSO optimization model for conjunctive management of surface–groundwater resources</atitle><jtitle>Neural computing & applications</jtitle><stitle>Neural Comput & Applic</stitle><date>2021-07-01</date><risdate>2021</risdate><volume>33</volume><issue>13</issue><spage>8067</spage><epage>8088</epage><pages>8067-8088</pages><issn>0941-0643</issn><eissn>1433-3058</eissn><abstract>Conjunctive management of surface–groundwater resources systems by means of mathematical optimization–simulation techniques becomes an important issue for sustainable water resources development, namely in water-scarce regions. In this study, the particle swarm optimization (PSO) method has been coupled with a hybrid wavelet/ANFIS–fuzzy C-means (FCM) simulation model to determine the optimal agricultural irrigation water allocation in the Miandarband plain, western Iran. Firstly, the optimal amount of conveyed water (CW) from the Gavoshan Dam into the plain is determined by constrained PSO. The constraints are the long-term minimum monthly exceedance streamflows that are estimated for different exceedance probabilities—with a 70% value found to best reflect the average annual river inflow of 3.4 m
3
/s into the dam—using the two-parameter Weibull distribution as well as the classical Weibull nonparametric plotting position method. Then, based on the politically prioritized proportions of the dam’s allocated water for domestic, environmental and agricultural uses, as well as the share of the plain devoted to agriculture, the optimal monthly CW available for the plain (= 112 MCM/a) is obtained. However, the subsequent estimation of the irrigation water request (IWR) (= 265.8 MCM/a), calculated by the FAO-56 method and using empirical crop coefficients of the present agricultural pattern in the plain, indicates that there is an irrigation water deficit of 153.1 MCM/a that must be made up by groundwater withdrawal (GW), in a way that neither waterlogging nor severe drop conditions in groundwater levels (GL) will occur. The latter are then calculated by the hybrid wavelet/ANFIS (FCM) model, wherefore good performance indicators R
2
and RMSE, equal to 0.98 and 0.21 m and 0.94 and 0.31 m in the training and testing phases, respectively, are obtained. Finally, PSO and the hybrid model are coupled to simulate the GL fluctuations—with the above GL constraints—under conjunctive use of the optimal surface (CW) and groundwater resources (GW) in the Miandarband plain. In conclusion, the innovative coupled simulation/optimization model turns out to be a very useful tool for optimal and sustainable conjunctive management of surface–groundwater resources in an irrigation area.</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s00521-020-05553-8</doi><tpages>22</tpages><orcidid>https://orcid.org/0000-0002-7147-150X</orcidid></addata></record> |
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subjects | Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Constraints Data Mining and Knowledge Discovery Groundwater Groundwater levels Image Processing and Computer Vision Irrigation Irrigation water Machine learning Optimization Optimization models Original Article Particle swarm optimization Probability and Statistics in Computer Science Runoff Signal processing Simulation Sustainable development Water resources development Weibull distribution |
title | Hybrid signal processing/machine learning and PSO optimization model for conjunctive management of surface–groundwater resources |
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