Journal Article

Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India

Yaseen, Zaher Mundher, Malik, Anurag, Singh, Vijay P., Kim, Sungwon, Kumar, Anil, Salih, Sinan Q., Kim, Nam Won

2020

Overview

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.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• 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