COMPARISON OF PROCESS‐BASED AND ARTIFICIAL NEURAL NETWORK APPROACHES FOR STREAMFLOW MODELING IN AN AGRICULTURAL WATERSHED
Identifieur interne : 000D85 ( Istex/Corpus ); précédent : 000D84; suivant : 000D86COMPARISON OF PROCESS‐BASED AND ARTIFICIAL NEURAL NETWORK APPROACHES FOR STREAMFLOW MODELING IN AN AGRICULTURAL WATERSHED
Auteurs : Puneet Srivastava ; James N. Mcnair ; Thomas E. JohnsonSource :
- JAWRA Journal of the American Water Resources Association [ 1093-474X ] ; 2006-06.
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Abstract
ABSTRACT: The performance of the Soil and Water Assessment Tool (SWAT) and artificial neural network (ANN) models in simulating hydrologic response was assessed in an agricultural watershed in southeastern Pennsylvania. All of the performance evaluation measures including Nash‐Sutcliffe coefficient of efficiency (E) and coefficient of determination (R2) suggest that the ANN monthly predictions were closer to the observed flows than the monthly predictions from the SWAT model. More specifically, monthly streamflow E and R2 were 0.54 and 0.57, respectively, for the SWAT model calibration period, and 0.71 and 0.75, respectively, for the ANN model training period. For the validation period, these values were −0.17 and 0.34 for the SWAT and 0.43 and 0.45 for the ANN model. SWAT model performance was affected by snowmelt events during winter months and by the model's inability to adequately simulate base flows. Even though this and other studies using ANN models suggest that these models provide a viable alternative approach for hydrologic and water quality modeling, ANN models in their current form are not spatially distributed watershed modeling systems. However, considering the promising performance of the simple ANN model, this study suggests that the ANN approach warrants further development to explicitly address the spatial distribution of hydrologic/water quality processes within watersheds.
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DOI: 10.1111/j.1752-1688.2006.tb04475.x
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All of the performance evaluation measures including Nash‐Sutcliffe coefficient of efficiency (E) and coefficient of determination (R2) suggest that the ANN monthly predictions were closer to the observed flows than the monthly predictions from the SWAT model. More specifically, monthly streamflow E and R2 were 0.54 and 0.57, respectively, for the SWAT model calibration period, and 0.71 and 0.75, respectively, for the ANN model training period. For the validation period, these values were −0.17 and 0.34 for the SWAT and 0.43 and 0.45 for the ANN model. SWAT model performance was affected by snowmelt events during winter months and by the model's inability to adequately simulate base flows. Even though this and other studies using ANN models suggest that these models provide a viable alternative approach for hydrologic and water quality modeling, ANN models in their current form are not spatially distributed watershed modeling systems. 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All of the performance evaluation measures including Nash‐Sutcliffe coefficient of efficiency (E) and coefficient of determination (R<sup>2</sup>) suggest that the ANN monthly predictions were closer to the observed flows than the monthly predictions from the SWAT model. More specifically, monthly streamflow E and R<sup>2</sup> were 0.54 and 0.57, respectively, for the SWAT model calibration period, and 0.71 and 0.75, respectively, for the ANN model training period. For the validation period, these values were −0.17 and 0.34 for the SWAT and 0.43 and 0.45 for the ANN model. SWAT model performance was affected by snowmelt events during winter months and by the model's inability to adequately simulate base flows. Even though this and other studies using ANN models suggest that these models provide a viable alternative approach for hydrologic and water quality modeling, ANN models in their current form are not spatially distributed watershed modeling systems. However, considering the promising performance of the simple ANN model, this study suggests that the ANN approach warrants further development to explicitly address the spatial distribution of hydrologic/water quality processes within watersheds.</p></abstract></abstractGroup></contentMeta><noteGroup><note xml:id="fn1"><label>1</label><p>Paper No. 04021 of the <i>Journal of the American Water Resources Association</i> (JAWRA)</p></note><note xml:id="fn2"><label>2</label><p>Respectively, Assistant Professor, Biosystems Engineering, Auburn University, 206 Tom E. Corley Building, Auburn, Alabama 36849–5417; and Senior Scientists, Patrick Center for Environmental Research, The Academy of Natural Sciences of Philadelphia, 1900 Benjamin Franklin Parkway, Philadelphia, Pennsylvania 19103 (E‐Mail/Srivastava: <email>srivapu@auburn.edu</email>).</p></note></noteGroup></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>COMPARISON OF PROCESS‐BASED AND ARTIFICIAL NEURAL NETWORK APPROACHES FOR STREAMFLOW MODELING IN AN AGRICULTURAL WATERSHED</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>COMPARISON OF PROCESS‐BASED AND ARTIFICIAL NEURAL NETWORK APPROACHES FOR STREAMFLOW MODELING IN AN AGRICULTURAL WATERSHED1</title></titleInfo><name type="personal"><namePart type="given">Puneet</namePart><namePart type="family">Srivastava</namePart><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">James N.</namePart><namePart type="family">McNair</namePart><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Thomas E.</namePart><namePart type="family">Johnson</namePart><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="article"></genre><originInfo><publisher>Blackwell Publishing Ltd</publisher><place><placeTerm type="text">Oxford, UK</placeTerm></place><dateIssued encoding="w3cdtf">2006-06</dateIssued><copyrightDate encoding="w3cdtf">2006</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType><extent unit="references">58</extent></physicalDescription><abstract>ABSTRACT: The performance of the Soil and Water Assessment Tool (SWAT) and artificial neural network (ANN) models in simulating hydrologic response was assessed in an agricultural watershed in southeastern Pennsylvania. All of the performance evaluation measures including Nash‐Sutcliffe coefficient of efficiency (E) and coefficient of determination (R2) suggest that the ANN monthly predictions were closer to the observed flows than the monthly predictions from the SWAT model. More specifically, monthly streamflow E and R2 were 0.54 and 0.57, respectively, for the SWAT model calibration period, and 0.71 and 0.75, respectively, for the ANN model training period. For the validation period, these values were −0.17 and 0.34 for the SWAT and 0.43 and 0.45 for the ANN model. SWAT model performance was affected by snowmelt events during winter months and by the model's inability to adequately simulate base flows. Even though this and other studies using ANN models suggest that these models provide a viable alternative approach for hydrologic and water quality modeling, ANN models in their current form are not spatially distributed watershed modeling systems. However, considering the promising performance of the simple ANN model, this study suggests that the ANN approach warrants further development to explicitly address the spatial distribution of hydrologic/water quality processes within watersheds.</abstract><note type="content">*Paper No. 04021 of the Journal of the American Water Resources Association (JAWRA)</note><subject lang="en"><genre>keywords</genre><topic>surface water hydrology</topic><topic>models</topic><topic>Soil and Water Assessment Tool (SWAT)</topic><topic>streamflow</topic><topic>runoff</topic><topic>base flow</topic></subject><relatedItem type="host"><titleInfo><title>JAWRA Journal of the American Water Resources Association</title></titleInfo><genre type="journal">journal</genre><identifier type="ISSN">1093-474X</identifier><identifier type="eISSN">1752-1688</identifier><identifier type="DOI">10.1111/(ISSN)1752-1688</identifier><identifier type="PublisherID">JAWR</identifier><part><date>2006</date><detail type="volume"><caption>vol.</caption><number>42</number></detail><detail type="issue"><caption>no.</caption><number>3</number></detail><extent unit="pages"><start>545</start><end>563</end><total>19</total></extent></part></relatedItem><identifier type="istex">8775A3F0DD767F6955B269FBD268014235FD572E</identifier><identifier type="DOI">10.1111/j.1752-1688.2006.tb04475.x</identifier><identifier type="ArticleID">JAWR545</identifier><recordInfo><recordContentSource>WILEY</recordContentSource><recordOrigin>Blackwell Publishing Ltd</recordOrigin></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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