Identification of drought in Dhalai river watershed using MCDM and ANN models
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An innovative approach for drought identification is developed using Multi-Criteria Decision Making (MCDM) and Artificial Neural Network (ANN) models from surveyed drought parameter data around the Dhalai river watershed in Tripura hinterlands, India. Total eight drought parameters, i.e., precipitation, soil moisture, evapotranspiration, vegetation canopy, cropping pattern, temperature, cultivated land, and groundwater level were obtained from expert, literature and cultivator survey. Then, the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) were used for weighting of parameters and Drought Index Identification (DII). Field data of weighted parameters in the meso scale Dhalai River watershed were collected and used to train the ANN model. The developed ANN model was used in the same watershed for identification of drought. Results indicate that the Limited-Memory Quasi-Newton algorithm was better than the commonly used training method. Results obtained from the ANN model shows the drought index developed from the study area ranges from 0.32 to 0.72. Overall analysis revealed that, with appropriate training, the ANN model can be used in the areas where the model is calibrated, or other areas where the range of input parameters is similar to the calibrated region for drought identification.
KeywordsMulti-criteria decision making artificial neural network drought identification.
Sainath Aher is sincerely thankful to the School of Hydro-Informatics Engineering, National Institute of Technology, Agartala (Tripura) for providing access to their research facilities during Summer Internship (May–June 2014). Further, authors wish to thank the anonymous reviewer for critically reviewing an earlier version of this manuscript and for providing helpful suggestions and comments that significantly improved its content.
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