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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doaj-652211a7a6794aeb962ca1bbe278055a2021-03-03T21:51:23ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-01155e023328010.1371/journal.pone.0233280Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India.Anurag MalikAnil KumarSinan Q SalihSungwon KimNam Won KimZaher Mundher YaseenVijay P SinghA 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.https://doi.org/10.1371/journal.pone.0233280 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Anurag Malik Anil Kumar Sinan Q Salih Sungwon Kim Nam Won Kim Zaher Mundher Yaseen Vijay P Singh |
spellingShingle |
Anurag Malik Anil Kumar Sinan Q Salih Sungwon Kim Nam Won Kim Zaher Mundher Yaseen Vijay P Singh Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India. PLoS ONE |
author_facet |
Anurag Malik Anil Kumar Sinan Q Salih Sungwon Kim Nam Won Kim Zaher Mundher Yaseen Vijay P Singh |
author_sort |
Anurag Malik |
title |
Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India. |
title_short |
Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India. |
title_full |
Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India. |
title_fullStr |
Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India. |
title_full_unstemmed |
Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India. |
title_sort |
drought index prediction using advanced fuzzy logic model: regional case study over kumaon in india. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2020-01-01 |
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. |
url |
https://doi.org/10.1371/journal.pone.0233280 |
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