Ensemble data-driven rainfall-runoff modeling using multi-source satellite and gauge rainfall data input fusion
Feed Forward Neural Network (FFNN), Adaptive Neuro-fuzzy Inference System (ANFIS), and Support Vector Regression (SVR) were applied for rainfall-runoff modeling of the Gilgel Abay catchment, Blue Nile basin, Ethiopia. Daily precipitations from satellite sources and rain gauge stations and outlet dis...
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| Veröffentlicht in: | Earth science informatics 2021-12, Vol.14 (4), p.1787-1808 |
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| creator | Nourani, Vahid Gökçekuş, Hüseyin Gichamo, Tagesse |
| description | Feed Forward Neural Network (FFNN), Adaptive Neuro-fuzzy Inference System (ANFIS), and Support Vector Regression (SVR) were applied for rainfall-runoff modeling of the Gilgel Abay catchment, Blue Nile basin, Ethiopia. Daily precipitations from satellite sources and rain gauge stations and outlet discharge were used. The dominant inputs were selected by non-linear sensitivity analysis. The study was conducted in two stages. First, single models for each data source with input fusion were trained. Second, ensemble runoff modeling using rainfall data fusion from only satellite products (strategy 1) and satellite and gauge (strategy 2) was conducted by Simple Average (SA), Weighted Average (WA), and Neural Network Ensemble (NNE) methods. NNE method using input fusion of strategy 2 improved performance of the best single satellite model up to 14.5% and a single gauge model up to 8% in the validation. Strategy 2 input data fusion ensemble rainfall-runoff modeling indicated substantial improvement over satellite data-based runoff modeling. This could be due to the bias correction ability of gauge rainfall over satellite rainfall products. Overall, results showed that ensemble modeling of input fusion from multiple source satellite rainfall products is a promising option for accurate modeling of the rainfall-runoff process for ungagged or sparsely gauged catchments. |
| doi_str_mv | 10.1007/s12145-021-00615-4 |
| format | Article |
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Gökçekuş, Hüseyin ; Gichamo, Tagesse</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-f07cc5b456d1f4a7d42f62aae455c108b6ad12de1cd91273d053273f44cf116f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Adaptive systems</topic><topic>Artificial neural networks</topic><topic>Catchments</topic><topic>Data integration</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Earth System Sciences</topic><topic>Fuzzy logic</topic><topic>Gauges</topic><topic>Hydrologic data</topic><topic>Hydrologic models</topic><topic>Information Systems Applications (incl.Internet)</topic><topic>Modelling</topic><topic>Neural networks</topic><topic>Ontology</topic><topic>Outlets</topic><topic>Precipitation</topic><topic>Rain gauges</topic><topic>Rainfall data</topic><topic>Rainfall runoff</topic><topic>Rainfall-runoff modeling</topic><topic>Rainfall-runoff relationships</topic><topic>Research Article</topic><topic>Runoff</topic><topic>Runoff process</topic><topic>Satellite data</topic><topic>Satellites</topic><topic>Sensitivity analysis</topic><topic>Simulation and Modeling</topic><topic>Space Exploration and Astronautics</topic><topic>Space Sciences (including Extraterrestrial Physics</topic><topic>Support vector machines</topic><toplevel>online_resources</toplevel><creatorcontrib>Nourani, Vahid</creatorcontrib><creatorcontrib>Gökçekuş, Hüseyin</creatorcontrib><creatorcontrib>Gichamo, Tagesse</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Computing Database</collection><collection>Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Earth, Atmospheric & Aquatic Science Database</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 Basic</collection><jtitle>Earth science informatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Nourani, Vahid</au><au>Gökçekuş, Hüseyin</au><au>Gichamo, Tagesse</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Ensemble data-driven rainfall-runoff modeling using multi-source satellite and gauge rainfall data input fusion</atitle><jtitle>Earth science informatics</jtitle><stitle>Earth Sci Inform</stitle><date>2021-12-01</date><risdate>2021</risdate><volume>14</volume><issue>4</issue><spage>1787</spage><epage>1808</epage><pages>1787-1808</pages><issn>1865-0473</issn><eissn>1865-0481</eissn><abstract>Feed Forward Neural Network (FFNN), Adaptive Neuro-fuzzy Inference System (ANFIS), and Support Vector Regression (SVR) were applied for rainfall-runoff modeling of the Gilgel Abay catchment, Blue Nile basin, Ethiopia. Daily precipitations from satellite sources and rain gauge stations and outlet discharge were used. The dominant inputs were selected by non-linear sensitivity analysis. The study was conducted in two stages. First, single models for each data source with input fusion were trained. Second, ensemble runoff modeling using rainfall data fusion from only satellite products (strategy 1) and satellite and gauge (strategy 2) was conducted by Simple Average (SA), Weighted Average (WA), and Neural Network Ensemble (NNE) methods. NNE method using input fusion of strategy 2 improved performance of the best single satellite model up to 14.5% and a single gauge model up to 8% in the validation. Strategy 2 input data fusion ensemble rainfall-runoff modeling indicated substantial improvement over satellite data-based runoff modeling. This could be due to the bias correction ability of gauge rainfall over satellite rainfall products. Overall, results showed that ensemble modeling of input fusion from multiple source satellite rainfall products is a promising option for accurate modeling of the rainfall-runoff process for ungagged or sparsely gauged catchments.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s12145-021-00615-4</doi><tpages>22</tpages></addata></record> |
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| subjects | Adaptive systems Artificial neural networks Catchments Data integration Earth and Environmental Science Earth Sciences Earth System Sciences Fuzzy logic Gauges Hydrologic data Hydrologic models Information Systems Applications (incl.Internet) Modelling Neural networks Ontology Outlets Precipitation Rain gauges Rainfall data Rainfall runoff Rainfall-runoff modeling Rainfall-runoff relationships Research Article Runoff Runoff process Satellite data Satellites Sensitivity analysis Simulation and Modeling Space Exploration and Astronautics Space Sciences (including Extraterrestrial Physics Support vector machines |
| title | Ensemble data-driven rainfall-runoff modeling using multi-source satellite and gauge rainfall data input fusion |
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