A hybrid wavelet-Lyapunov exponent model for river water quality forecast
The use of spectral theory and chaos theory on river water quality modeling is reported in a very limited way. This study proposes a wavelet-maximum Lyapunov exponent (WMLE) hybrid model for river water quality dynamics, combining spectral theory and chaos theory. The methodology involves the follow...
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| Veröffentlicht in: | Journal of hydroinformatics 2021-07, Vol.23 (4), p.864-878 |
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| creator | Jiang, Jiping Tang, Sijie Liu, Rentao Sivakumar, Bellie Wu, Xiaoye Pang, Tianrui |
| description | The use of spectral theory and chaos theory on river water quality modeling is reported in a very limited way. This study proposes a wavelet-maximum Lyapunov exponent (WMLE) hybrid model for river water quality dynamics, combining spectral theory and chaos theory. The methodology involves the following major steps: (1) use of wavelet transformation to filter the noisy signal in the water quality time series; (2) reconstruction of phase space to embed the water quality time series and determine the trajectory of the underlying dynamics; and (3) identification of the presence/absence of chaos and prediction using the largest Lyapunov exponent value. Case studies on the Huaihe River in China and the Potomac River in the United States, as representatives of low-frequency and high-frequency forecast, show average relative errors on weekly DO, COD, and NH3-N data are 2.35%, 4.53%, and 18.85%, and on 15-minute based DO data are 1.185%. It also indicates that the hybrid model performs better to some extent when compared to the purely Lyapunov exponent model, ARMA model, and ANN model. This study is a proof that the combination of spectral theory and chaos theory is promising to describe and predict fluctuation of particular water quality indicators in rivers. |
| doi_str_mv | 10.2166/hydro.2021.023 |
| format | Article |
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This study proposes a wavelet-maximum Lyapunov exponent (WMLE) hybrid model for river water quality dynamics, combining spectral theory and chaos theory. The methodology involves the following major steps: (1) use of wavelet transformation to filter the noisy signal in the water quality time series; (2) reconstruction of phase space to embed the water quality time series and determine the trajectory of the underlying dynamics; and (3) identification of the presence/absence of chaos and prediction using the largest Lyapunov exponent value. Case studies on the Huaihe River in China and the Potomac River in the United States, as representatives of low-frequency and high-frequency forecast, show average relative errors on weekly DO, COD, and NH3-N data are 2.35%, 4.53%, and 18.85%, and on 15-minute based DO data are 1.185%. It also indicates that the hybrid model performs better to some extent when compared to the purely Lyapunov exponent model, ARMA model, and ANN model. This study is a proof that the combination of spectral theory and chaos theory is promising to describe and predict fluctuation of particular water quality indicators in rivers.</description><identifier>ISSN: 1464-7141</identifier><identifier>EISSN: 1465-1734</identifier><identifier>DOI: 10.2166/hydro.2021.023</identifier><language>eng</language><publisher>LONDON: Iwa Publishing</publisher><subject>Ammonia ; Chaos theory ; Chemical oxygen demand ; Computer Science ; Computer Science, Interdisciplinary Applications ; Dissolved oxygen ; Dynamics ; Engineering ; Engineering, Civil ; Environmental Sciences ; Environmental Sciences & Ecology ; hybrid model ; Hydrology ; Liapunov exponents ; Life Sciences & Biomedicine ; lyapunov exponent ; Machine learning ; Mathematical models ; Nitrogen ; nonlinearity ; Physical Sciences ; River water ; Rivers ; Science & Technology ; Signal quality ; Spectra ; Spectral theory ; Stream flow ; Technology ; Theories ; Time series ; Water purification ; Water quality ; Water Resources ; wavelet ; Wavelet transforms ; Weekly</subject><ispartof>Journal of hydroinformatics, 2021-07, Vol.23 (4), p.864-878</ispartof><rights>Copyright IWA Publishing Jul 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>7</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000657782500001</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c373t-e756bbc399671a0b3f3b81bb2ff160d9f1cd106962f436dea2feb6f3bc9717d33</citedby><cites>FETCH-LOGICAL-c373t-e756bbc399671a0b3f3b81bb2ff160d9f1cd106962f436dea2feb6f3bc9717d33</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,781,785,865,2103,2115,27929,27930,39263</link.rule.ids></links><search><creatorcontrib>Jiang, Jiping</creatorcontrib><creatorcontrib>Tang, Sijie</creatorcontrib><creatorcontrib>Liu, Rentao</creatorcontrib><creatorcontrib>Sivakumar, Bellie</creatorcontrib><creatorcontrib>Wu, Xiaoye</creatorcontrib><creatorcontrib>Pang, Tianrui</creatorcontrib><title>A hybrid wavelet-Lyapunov exponent model for river water quality forecast</title><title>Journal of hydroinformatics</title><addtitle>J HYDROINFORM</addtitle><description>The use of spectral theory and chaos theory on river water quality modeling is reported in a very limited way. This study proposes a wavelet-maximum Lyapunov exponent (WMLE) hybrid model for river water quality dynamics, combining spectral theory and chaos theory. The methodology involves the following major steps: (1) use of wavelet transformation to filter the noisy signal in the water quality time series; (2) reconstruction of phase space to embed the water quality time series and determine the trajectory of the underlying dynamics; and (3) identification of the presence/absence of chaos and prediction using the largest Lyapunov exponent value. Case studies on the Huaihe River in China and the Potomac River in the United States, as representatives of low-frequency and high-frequency forecast, show average relative errors on weekly DO, COD, and NH3-N data are 2.35%, 4.53%, and 18.85%, and on 15-minute based DO data are 1.185%. It also indicates that the hybrid model performs better to some extent when compared to the purely Lyapunov exponent model, ARMA model, and ANN model. This study is a proof that the combination of spectral theory and chaos theory is promising to describe and predict fluctuation of particular water quality indicators in rivers.</description><subject>Ammonia</subject><subject>Chaos theory</subject><subject>Chemical oxygen demand</subject><subject>Computer Science</subject><subject>Computer Science, Interdisciplinary Applications</subject><subject>Dissolved oxygen</subject><subject>Dynamics</subject><subject>Engineering</subject><subject>Engineering, Civil</subject><subject>Environmental Sciences</subject><subject>Environmental Sciences & Ecology</subject><subject>hybrid model</subject><subject>Hydrology</subject><subject>Liapunov exponents</subject><subject>Life Sciences & Biomedicine</subject><subject>lyapunov exponent</subject><subject>Machine learning</subject><subject>Mathematical models</subject><subject>Nitrogen</subject><subject>nonlinearity</subject><subject>Physical Sciences</subject><subject>River water</subject><subject>Rivers</subject><subject>Science & Technology</subject><subject>Signal quality</subject><subject>Spectra</subject><subject>Spectral theory</subject><subject>Stream flow</subject><subject>Technology</subject><subject>Theories</subject><subject>Time series</subject><subject>Water purification</subject><subject>Water quality</subject><subject>Water Resources</subject><subject>wavelet</subject><subject>Wavelet transforms</subject><subject>Weekly</subject><issn>1464-7141</issn><issn>1465-1734</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>HGBXW</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNkM2L2zAQxc3ShabZXns27LE41Ui2FB9D6G4DgV7as9DHqFFwrESWs-v_vkpScu5lZhh-783wiuILkAUFzr_tJhvDghIKC0LZQzGDmjcVCFZ_uM51JaCGj8WnYdiTTLElzIrNqtxNOnpbvqkzdpiq7aSOYx_OJb4fQ499Kg_BYle6EMvozxgzmXI9jarzabrs0aghPRWPTnUDfv7X58Xvl--_1j-q7c_XzXq1rQwTLFUoGq61YW3LBSiimWN6CVpT54AT2zowFghvOXU14xYVdah5hkwrQFjG5sXm5muD2stj9AcVJxmUl9dFiH-kismbDiVlQjtCaq1Q1YBOC0UxnzMEKRVos9fzzesYw2nEIcl9GGOf35e0aQQXDDhkanGjTAzDENHdrwKRl-jlNXp5iV7m6LPg603whjq4wXjsDd5FhBDeCLGkTZ7IxX75__TaJ5V86Ndh7BP7C-wrmW8</recordid><startdate>20210701</startdate><enddate>20210701</enddate><creator>Jiang, Jiping</creator><creator>Tang, Sijie</creator><creator>Liu, Rentao</creator><creator>Sivakumar, Bellie</creator><creator>Wu, Xiaoye</creator><creator>Pang, Tianrui</creator><general>Iwa Publishing</general><general>IWA Publishing</general><scope>BLEPL</scope><scope>DTL</scope><scope>HGBXW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7UA</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>H96</scope><scope>HCIFZ</scope><scope>L.G</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>DOA</scope></search><sort><creationdate>20210701</creationdate><title>A hybrid wavelet-Lyapunov exponent model for river water quality forecast</title><author>Jiang, Jiping ; Tang, Sijie ; Liu, Rentao ; Sivakumar, Bellie ; Wu, Xiaoye ; Pang, Tianrui</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c373t-e756bbc399671a0b3f3b81bb2ff160d9f1cd106962f436dea2feb6f3bc9717d33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Ammonia</topic><topic>Chaos theory</topic><topic>Chemical oxygen demand</topic><topic>Computer Science</topic><topic>Computer Science, Interdisciplinary Applications</topic><topic>Dissolved oxygen</topic><topic>Dynamics</topic><topic>Engineering</topic><topic>Engineering, Civil</topic><topic>Environmental Sciences</topic><topic>Environmental Sciences & Ecology</topic><topic>hybrid model</topic><topic>Hydrology</topic><topic>Liapunov exponents</topic><topic>Life Sciences & Biomedicine</topic><topic>lyapunov exponent</topic><topic>Machine learning</topic><topic>Mathematical models</topic><topic>Nitrogen</topic><topic>nonlinearity</topic><topic>Physical Sciences</topic><topic>River water</topic><topic>Rivers</topic><topic>Science & Technology</topic><topic>Signal quality</topic><topic>Spectra</topic><topic>Spectral theory</topic><topic>Stream flow</topic><topic>Technology</topic><topic>Theories</topic><topic>Time series</topic><topic>Water purification</topic><topic>Water quality</topic><topic>Water Resources</topic><topic>wavelet</topic><topic>Wavelet transforms</topic><topic>Weekly</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jiang, Jiping</creatorcontrib><creatorcontrib>Tang, Sijie</creatorcontrib><creatorcontrib>Liu, Rentao</creatorcontrib><creatorcontrib>Sivakumar, Bellie</creatorcontrib><creatorcontrib>Wu, Xiaoye</creatorcontrib><creatorcontrib>Pang, Tianrui</creatorcontrib><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Web of Science - Science Citation Index Expanded - 2021</collection><collection>CrossRef</collection><collection>Aqualine</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Environmental Science Database</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>Environmental Science Collection</collection><collection>Directory of Open Access Journals</collection><jtitle>Journal of hydroinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jiang, Jiping</au><au>Tang, Sijie</au><au>Liu, Rentao</au><au>Sivakumar, Bellie</au><au>Wu, Xiaoye</au><au>Pang, Tianrui</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A hybrid wavelet-Lyapunov exponent model for river water quality forecast</atitle><jtitle>Journal of hydroinformatics</jtitle><stitle>J HYDROINFORM</stitle><date>2021-07-01</date><risdate>2021</risdate><volume>23</volume><issue>4</issue><spage>864</spage><epage>878</epage><pages>864-878</pages><issn>1464-7141</issn><eissn>1465-1734</eissn><abstract>The use of spectral theory and chaos theory on river water quality modeling is reported in a very limited way. This study proposes a wavelet-maximum Lyapunov exponent (WMLE) hybrid model for river water quality dynamics, combining spectral theory and chaos theory. The methodology involves the following major steps: (1) use of wavelet transformation to filter the noisy signal in the water quality time series; (2) reconstruction of phase space to embed the water quality time series and determine the trajectory of the underlying dynamics; and (3) identification of the presence/absence of chaos and prediction using the largest Lyapunov exponent value. Case studies on the Huaihe River in China and the Potomac River in the United States, as representatives of low-frequency and high-frequency forecast, show average relative errors on weekly DO, COD, and NH3-N data are 2.35%, 4.53%, and 18.85%, and on 15-minute based DO data are 1.185%. It also indicates that the hybrid model performs better to some extent when compared to the purely Lyapunov exponent model, ARMA model, and ANN model. This study is a proof that the combination of spectral theory and chaos theory is promising to describe and predict fluctuation of particular water quality indicators in rivers.</abstract><cop>LONDON</cop><pub>Iwa Publishing</pub><doi>10.2166/hydro.2021.023</doi><tpages>15</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Ammonia Chaos theory Chemical oxygen demand Computer Science Computer Science, Interdisciplinary Applications Dissolved oxygen Dynamics Engineering Engineering, Civil Environmental Sciences Environmental Sciences & Ecology hybrid model Hydrology Liapunov exponents Life Sciences & Biomedicine lyapunov exponent Machine learning Mathematical models Nitrogen nonlinearity Physical Sciences River water Rivers Science & Technology Signal quality Spectra Spectral theory Stream flow Technology Theories Time series Water purification Water quality Water Resources wavelet Wavelet transforms Weekly |
| title | A hybrid wavelet-Lyapunov exponent model for river water quality forecast |
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