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      https://www.ias.ac.in/article/fulltext/jess/126/02/0021

    • Keywords

       

      Multi-criteria decision making; artificial neural network; drought identification.

    • Abstract

       

      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 analysisrevealed 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.

    • Author Affiliations

       

      Sainath Aher1 2 Sambhaji Shinde1 Shantamoy Guha3 Mrinmoy Majumder4

      1. Department of Geography, Shivaji University, Kolhapur 416 004, India.
      2. Department of Geography, S.N. Arts, D.J.M. Commerce & B.N.S. Science College, Sangamner 422 605, India.
      3. Discipline of Earth Sciences, Indian Institute of Technology, Gandhinagar 382 424, India.
      4. School of Hydro-Informatics Engineering, National Institute of Technology, Agartala 799 046, India.
    • Dates

       
  • Journal of Earth System Science | News

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