Identification of drought in Dhalai river watershed using MCDM and ANN Models

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dc.contributor.author Guha, Shantamoy
dc.contributor.author Aher, Sainath
dc.contributor.author Shinde, Sambhaji
dc.contributor.author Majumder, Mrinmoy
dc.date.accessioned 2016-12-26T08:26:10Z
dc.date.available 2016-12-26T08:26:10Z
dc.date.issued 2017-02
dc.identifier.citation Aher, Sainath; Shinde, Sambhaji; Guha, Shantamoy and Majumder, Mrinmoy, “Identification of drought in Dhalai river watershed using MCDM and ANN models”, Journal of Earth System Science, DOI: 10.1007/s12040-017-0795-1, vol. 126, no. 2, Feb. 2017. en_US
dc.identifier.issn 0253-4126
dc.identifier.issn 0973-774X
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/2584
dc.identifier.uri http://dx.doi.org/10.1007/s12040-017-0795-1
dc.description.abstract An innovative approach for drought identification is developed using Multi-Criteria Decision-Making (MCDM) and Artificial Neural Network (ANN) model from surveyed drought parameters 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 experts, literature and cultivators 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 trained ANN model has been tested in the same watershed for its calibration. Results indicate that the Limited Memory - Quasi Newton algorithm was better than the commonly used training method. Based on obtained results from ANN model drought index 0.30 to 0.75 were generated for present study area. Overall analysis revealed that, with appropriate training, the ANN model could be used in the areas where the model is calibrated, or other areas where range of input parameters is similar to the calibrated region. en_US
dc.description.statementofresponsibility by Sainath Aher, Sambhaji Shinde, Shantamoy Guha and Mrinmoy Majumder
dc.format.extent vol. 126, no. 2
dc.language.iso en en_US
dc.publisher Springer Verlag en_US
dc.subject Multi-criteria decision making en_US
dc.subject Artificial neural network en_US
dc.subject Drought identification en_US
dc.title Identification of drought in Dhalai river watershed using MCDM and ANN Models en_US
dc.type Article en_US
dc.relation.journal Journal of Earth System Science


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