Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India
A new version of the fuzzy logic model, called the co-active neuro fuzzy inference system (CANFIS), is introduced for predicting standardized precipitation index (SPI). Multiple scales of drought information at six meteorological stations located in Uttarakhand State, India, are used. Different lead...
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| creator | Malik, Anurag Kumar, Anil Salih, Sinan Q Kim, Sungwon Kim, Nam Won Yaseen, Zaher Mundher Singh, Vijay P |
| description | A new version of the fuzzy logic model, called the co-active neuro fuzzy inference system (CANFIS), is introduced for predicting standardized precipitation index (SPI). Multiple scales of drought information at six meteorological stations located in Uttarakhand State, India, are used. Different lead times of SPI were computed for prediction, including 1, 3, 6, 9, 12, and 24 months, with inputs abstracted by autocorrelation function (ACF) and partial-ACF (PACF) analysis at 5% significance level. The proposed CANFIS model was validated against two models: classical artificial intelligence model (e.g., multilayer perceptron neural network (MLPNN)) and regression model (e.g., multiple linear regression (MLR)). Several performance evaluation metrices (root mean square error, Nash-Sutcliffe efficiency, coefficient of correlation, and Willmott index), and graphical visualizations (scatter plot and Taylor diagram) were computed for the evaluation of model performance. Results indicated that the CANFIS model predicted the SPI better than the other models and prediction results were different for different meteorological stations. The proposed model can build a reliable expert intelligent system for predicting meteorological drought at multi-time scales and decision making for remedial schemes to cope with meteorological drought at the study stations and can help to maintain sustainable water resources management. |
| doi_str_mv | 10.1371/journal.pone.0233280 |
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Multiple scales of drought information at six meteorological stations located in Uttarakhand State, India, are used. Different lead times of SPI were computed for prediction, including 1, 3, 6, 9, 12, and 24 months, with inputs abstracted by autocorrelation function (ACF) and partial-ACF (PACF) analysis at 5% significance level. The proposed CANFIS model was validated against two models: classical artificial intelligence model (e.g., multilayer perceptron neural network (MLPNN)) and regression model (e.g., multiple linear regression (MLR)). Several performance evaluation metrices (root mean square error, Nash-Sutcliffe efficiency, coefficient of correlation, and Willmott index), and graphical visualizations (scatter plot and Taylor diagram) were computed for the evaluation of model performance. Results indicated that the CANFIS model predicted the SPI better than the other models and prediction results were different for different meteorological stations. The proposed model can build a reliable expert intelligent system for predicting meteorological drought at multi-time scales and decision making for remedial schemes to cope with meteorological drought at the study stations and can help to maintain sustainable water resources management.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0233280</identifier><identifier>PMID: 32437386</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Agriculture ; Artificial intelligence ; Artificial neural networks ; Atmospheric models ; Autocorrelation function ; Autocorrelation functions ; Biology and Life Sciences ; Case reports ; Case studies ; Civil engineering ; Computation ; Computer and Information Sciences ; Decision making ; Drought ; Drought index ; Droughts ; Earth Sciences ; Ecology and Environmental Sciences ; Emergency preparedness ; Fuzzy logic ; Fuzzy systems ; Meteorological research ; Multilayer perceptrons ; Neural networks ; Performance evaluation ; Physical Sciences ; Precipitation ; Precipitation (Meteorology) ; Predictions ; R&D ; Regression analysis ; Regression models ; Research & development ; Research and Analysis Methods ; Resource management ; Standardized precipitation index ; Time ; Vietnam ; Visualization (Computer) ; Water management ; Water resource management ; Water resources ; Water resources management ; Water shortages ; Weather ; Weather forecasting ; Weather stations</subject><ispartof>PloS one, 2020-05, Vol.15 (5), p.e0233280-e0233280</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>2020 Malik et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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Multiple scales of drought information at six meteorological stations located in Uttarakhand State, India, are used. Different lead times of SPI were computed for prediction, including 1, 3, 6, 9, 12, and 24 months, with inputs abstracted by autocorrelation function (ACF) and partial-ACF (PACF) analysis at 5% significance level. The proposed CANFIS model was validated against two models: classical artificial intelligence model (e.g., multilayer perceptron neural network (MLPNN)) and regression model (e.g., multiple linear regression (MLR)). Several performance evaluation metrices (root mean square error, Nash-Sutcliffe efficiency, coefficient of correlation, and Willmott index), and graphical visualizations (scatter plot and Taylor diagram) were computed for the evaluation of model performance. Results indicated that the CANFIS model predicted the SPI better than the other models and prediction results were different for different meteorological stations. The proposed model can build a reliable expert intelligent system for predicting meteorological drought at multi-time scales and decision making for remedial schemes to cope with meteorological drought at the study stations and can help to maintain sustainable water resources management.</description><subject>Agriculture</subject><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Atmospheric models</subject><subject>Autocorrelation function</subject><subject>Autocorrelation functions</subject><subject>Biology and Life Sciences</subject><subject>Case reports</subject><subject>Case studies</subject><subject>Civil engineering</subject><subject>Computation</subject><subject>Computer and Information Sciences</subject><subject>Decision making</subject><subject>Drought</subject><subject>Drought index</subject><subject>Droughts</subject><subject>Earth Sciences</subject><subject>Ecology and Environmental Sciences</subject><subject>Emergency 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one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Malik, Anurag</au><au>Kumar, Anil</au><au>Salih, Sinan Q</au><au>Kim, Sungwon</au><au>Kim, Nam Won</au><au>Yaseen, Zaher Mundher</au><au>Singh, Vijay P</au><au>Ab Wahab, Mohd Nadhir</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2020-05-21</date><risdate>2020</risdate><volume>15</volume><issue>5</issue><spage>e0233280</spage><epage>e0233280</epage><pages>e0233280-e0233280</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>A new version of the fuzzy logic model, called the co-active neuro fuzzy inference system (CANFIS), is introduced for predicting standardized precipitation index (SPI). Multiple scales of drought information at six meteorological stations located in Uttarakhand State, India, are used. Different lead times of SPI were computed for prediction, including 1, 3, 6, 9, 12, and 24 months, with inputs abstracted by autocorrelation function (ACF) and partial-ACF (PACF) analysis at 5% significance level. The proposed CANFIS model was validated against two models: classical artificial intelligence model (e.g., multilayer perceptron neural network (MLPNN)) and regression model (e.g., multiple linear regression (MLR)). Several performance evaluation metrices (root mean square error, Nash-Sutcliffe efficiency, coefficient of correlation, and Willmott index), and graphical visualizations (scatter plot and Taylor diagram) were computed for the evaluation of model performance. Results indicated that the CANFIS model predicted the SPI better than the other models and prediction results were different for different meteorological stations. The proposed model can build a reliable expert intelligent system for predicting meteorological drought at multi-time scales and decision making for remedial schemes to cope with meteorological drought at the study stations and can help to maintain sustainable water resources management.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>32437386</pmid><doi>10.1371/journal.pone.0233280</doi><tpages>e0233280</tpages><orcidid>https://orcid.org/0000-0002-0298-5777</orcidid><orcidid>https://orcid.org/0000-0003-3647-7137</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Agriculture Artificial intelligence Artificial neural networks Atmospheric models Autocorrelation function Autocorrelation functions Biology and Life Sciences Case reports Case studies Civil engineering Computation Computer and Information Sciences Decision making Drought Drought index Droughts Earth Sciences Ecology and Environmental Sciences Emergency preparedness Fuzzy logic Fuzzy systems Meteorological research Multilayer perceptrons Neural networks Performance evaluation Physical Sciences Precipitation Precipitation (Meteorology) Predictions R&D Regression analysis Regression models Research & development Research and Analysis Methods Resource management Standardized precipitation index Time Vietnam Visualization (Computer) Water management Water resource management Water resources Water resources management Water shortages Weather Weather forecasting Weather stations |
| title | Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India |
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