A wavelet-based autoregressive fuzzy model for forecasting algal blooms
This paper proposes fuzzy models for forecasting the complex behavior of algal blooms. The models are developed through the integration of autoregressive models, the Takagi-Sugeno fuzzy model, and discrete wavelet transform algorithms. The premise parts of the proposed models are determined using th...
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Veröffentlicht in: | Environmental modelling & software : with environment data news 2014-12, Vol.62, p.1-10 |
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creator | Kim, Yeesock Shin, Hyun Suk Plummer, Jeanine D. |
description | This paper proposes fuzzy models for forecasting the complex behavior of algal blooms. The models are developed through the integration of autoregressive models, the Takagi-Sugeno fuzzy model, and discrete wavelet transform algorithms. The premise parts of the proposed models are determined using the subtractive clustering technique and the consequent parts are optimized using weighted least squares. To train and validate the proposed fuzzy models, a large number of data sets were collected from Daecheong reservoir in Geum River in the Republic of Korea. The data include both water quality and hydrological variables. Total nitrogen, total phosphorous, dissolved oxygen, chemical oxygen demand, biochemical oxygen demand, pH, air temperature, water temperature and outflow water were evaluated as input signals while chlorophyll-a was used as an output. It is demonstrated from the simulation that the proposed fuzzy models are effective in forecasting algal blooms.
•Complex forecasting models are developed in this paper.•Three intelligent systems for predicting behavior of algal blooms are modeled.•The models are validated using experimental data. |
doi_str_mv | 10.1016/j.envsoft.2014.08.014 |
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•Complex forecasting models are developed in this paper.•Three intelligent systems for predicting behavior of algal blooms are modeled.•The models are validated using experimental data.</description><identifier>ISSN: 1364-8152</identifier><identifier>EISSN: 1873-6726</identifier><identifier>DOI: 10.1016/j.envsoft.2014.08.014</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>Algae ; Algal bloom ; Animal, plant and microbial ecology ; Applied ecology ; Autoregressive model ; Biological and medical sciences ; Blooms ; Chlorophyll-a ; Conservation, protection and management of environment and wildlife ; Forecasting ; Fundamental and applied biological sciences. Psychology ; Fuzzy ; Fuzzy logic ; Fuzzy set theory ; General aspects ; General aspects. Techniques ; Mathematical models ; Methods and techniques (sampling, tagging, trapping, modelling...) ; Water quality management ; Water temperature ; Wavelet transform</subject><ispartof>Environmental modelling & software : with environment data news, 2014-12, Vol.62, p.1-10</ispartof><rights>2014 Elsevier Ltd</rights><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c405t-81e29b9a5953dcfdc3e6c675121e73c4e66292314315f725a2b25b6fd1e343bd3</citedby><cites>FETCH-LOGICAL-c405t-81e29b9a5953dcfdc3e6c675121e73c4e66292314315f725a2b25b6fd1e343bd3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.envsoft.2014.08.014$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=29037064$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Kim, Yeesock</creatorcontrib><creatorcontrib>Shin, Hyun Suk</creatorcontrib><creatorcontrib>Plummer, Jeanine D.</creatorcontrib><title>A wavelet-based autoregressive fuzzy model for forecasting algal blooms</title><title>Environmental modelling & software : with environment data news</title><description>This paper proposes fuzzy models for forecasting the complex behavior of algal blooms. The models are developed through the integration of autoregressive models, the Takagi-Sugeno fuzzy model, and discrete wavelet transform algorithms. The premise parts of the proposed models are determined using the subtractive clustering technique and the consequent parts are optimized using weighted least squares. To train and validate the proposed fuzzy models, a large number of data sets were collected from Daecheong reservoir in Geum River in the Republic of Korea. The data include both water quality and hydrological variables. Total nitrogen, total phosphorous, dissolved oxygen, chemical oxygen demand, biochemical oxygen demand, pH, air temperature, water temperature and outflow water were evaluated as input signals while chlorophyll-a was used as an output. It is demonstrated from the simulation that the proposed fuzzy models are effective in forecasting algal blooms.
•Complex forecasting models are developed in this paper.•Three intelligent systems for predicting behavior of algal blooms are modeled.•The models are validated using experimental data.</description><subject>Algae</subject><subject>Algal bloom</subject><subject>Animal, plant and microbial ecology</subject><subject>Applied ecology</subject><subject>Autoregressive model</subject><subject>Biological and medical sciences</subject><subject>Blooms</subject><subject>Chlorophyll-a</subject><subject>Conservation, protection and management of environment and wildlife</subject><subject>Forecasting</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Fuzzy</subject><subject>Fuzzy logic</subject><subject>Fuzzy set theory</subject><subject>General aspects</subject><subject>General aspects. Techniques</subject><subject>Mathematical models</subject><subject>Methods and techniques (sampling, tagging, trapping, modelling...)</subject><subject>Water quality management</subject><subject>Water temperature</subject><subject>Wavelet transform</subject><issn>1364-8152</issn><issn>1873-6726</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNqNkMtKAzEUhgdRsF4eQZiN4GbGXCZJZyWleAPBja5DJnNSUtJJzZlW9OlNaXGri8N_Ft-58BXFFSU1JVTeLmsYthjdWDNCm5pM6xxHxYROFa-kYvI491w21ZQKdlqcIS4JIblvJsXjrPw0WwgwVp1B6EuzGWOCRQJEv4XSbb6_v8pV7CGULqZdgTU4-mFRmrAwoexCjCu8KE6cCQiXhzwv3h_u3-ZP1cvr4_N89lLZhogxfwCs7VojWsF763rLQVqpBGUUFLcNSMlaxmnDqXCKCcM6Jjrpegq84V3Pz4ub_d51ih8bwFGvPFoIwQwQN6iplIRIITj9B9ooQjlXKqNij9oUERM4vU5-ZdKXpkTvHOulPjjWO8eaTHWOPHd9OGHQmuCSGazH32HWEq6I3HF3ew6ymq2HpNF6GCz0PuscdR_9H5d-AH9NlGA</recordid><startdate>20141201</startdate><enddate>20141201</enddate><creator>Kim, Yeesock</creator><creator>Shin, Hyun Suk</creator><creator>Plummer, Jeanine D.</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7ST</scope><scope>7TG</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>KL.</scope><scope>L.G</scope><scope>M7N</scope><scope>SOI</scope><scope>7SC</scope><scope>7SP</scope><scope>7SU</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20141201</creationdate><title>A wavelet-based autoregressive fuzzy model for forecasting algal blooms</title><author>Kim, Yeesock ; Shin, Hyun Suk ; Plummer, Jeanine D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c405t-81e29b9a5953dcfdc3e6c675121e73c4e66292314315f725a2b25b6fd1e343bd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Algae</topic><topic>Algal bloom</topic><topic>Animal, plant and microbial ecology</topic><topic>Applied ecology</topic><topic>Autoregressive model</topic><topic>Biological and medical sciences</topic><topic>Blooms</topic><topic>Chlorophyll-a</topic><topic>Conservation, protection and management of environment and wildlife</topic><topic>Forecasting</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Fuzzy</topic><topic>Fuzzy logic</topic><topic>Fuzzy set theory</topic><topic>General aspects</topic><topic>General aspects. 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The models are developed through the integration of autoregressive models, the Takagi-Sugeno fuzzy model, and discrete wavelet transform algorithms. The premise parts of the proposed models are determined using the subtractive clustering technique and the consequent parts are optimized using weighted least squares. To train and validate the proposed fuzzy models, a large number of data sets were collected from Daecheong reservoir in Geum River in the Republic of Korea. The data include both water quality and hydrological variables. Total nitrogen, total phosphorous, dissolved oxygen, chemical oxygen demand, biochemical oxygen demand, pH, air temperature, water temperature and outflow water were evaluated as input signals while chlorophyll-a was used as an output. It is demonstrated from the simulation that the proposed fuzzy models are effective in forecasting algal blooms.
•Complex forecasting models are developed in this paper.•Three intelligent systems for predicting behavior of algal blooms are modeled.•The models are validated using experimental data.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.envsoft.2014.08.014</doi><tpages>10</tpages></addata></record> |
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subjects | Algae Algal bloom Animal, plant and microbial ecology Applied ecology Autoregressive model Biological and medical sciences Blooms Chlorophyll-a Conservation, protection and management of environment and wildlife Forecasting Fundamental and applied biological sciences. Psychology Fuzzy Fuzzy logic Fuzzy set theory General aspects General aspects. Techniques Mathematical models Methods and techniques (sampling, tagging, trapping, modelling...) Water quality management Water temperature Wavelet transform |
title | A wavelet-based autoregressive fuzzy model for forecasting algal blooms |
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