• Register
  • Login
  • Persian

Journal of Range and Watershed Managment

  1. Home
  2. Comparing the performance of Artificial Neural Network (ANN) to predict the long term Meteorological Drought using Climatic Parameters and teleconnection (case study: South of Qazvin Province)

Current Issue

By Issue

By Author

Author Index

Keyword Index

About Journal

Aims and Scope

Editorial Board

Publication Ethics

Indexing and Abstracting

Related Links

FAQ

Peer Review Process

News

Journal Metric

Superior reviewers

Reviewers

Guide for Reviewers

Comparing the performance of Artificial Neural Network (ANN) to predict the long term Meteorological Drought using Climatic Parameters and teleconnection (case study: South of Qazvin Province)

    Authors

    • Fatemeh Maghsoud 1
    • Mohammad Reza Yazdani 2
    • Mohammad Rahimi
    • Arash Malekian
    • ali asghar zolfaghari 3

    1 tarbiat modarres university

    2 Associate Professor

    3 assistant professor

,

Document Type : Research Paper

10.22059/jrwm.2018.122721.862
  • Article Information
  • Download
  • How to cite
  • Statistics
  • Share

Abstract

Overview, drought is effected an unusual dry period which is enough continued and causes imbalance in the hydrologic status, as depletion of surface water and groundwater resources. The purpose of this research is modeling meteorological drought prediction using Neural Network- Multi layer Perceptron, parameters and climatic signals in three time scales include short, middle and long term in a rain-gauge station located at south plain of Qazvin Province. Three different scenarios were tested as inputs model. Optimal combination of variables was determinate by Gamma-Test after identification of input variables using cross-correlation. Results showed, influence of climatic signals increased and against the influence of meteorological parameters decreased when time scale were increased from short-term to long-term. MEI (Multivariate ENSO Index) and rainfall were introduced as the most effective climatic signals and meteorological parameter for each scale, respectively. Neural Network modeling which has hidden layer with enough neurons, Sigmoid Function in middle layer and linear function at output layer was used. The most appropriate of the number neurons was determined in each scenario and wasn’t observed significant correlation between increasing or decreasing the error and number of neurons. Finally, the most appropriate network structure was determined based on evaluation indexes in three scenarios and each time scale.

Keywords

  • Drought
  • Gamma-Test
  • prediction
  • Artificial Network
  • Climatic Signal
  • XML
  • PDF 1.37 M
  • RIS
  • EndNote
  • Mendeley
  • BibTeX
  • APA
  • MLA
  • HARVARD
  • VANCOUVER
    • Article View: 230
    • PDF Download: 249
Journal of Range and Watershed Managment
Volume 70, Issue 4
January 2018
Pages 1015-1030
Files
  • XML
  • PDF 1.37 M
History
  • Receive Date: 27 April 2015
  • Revise Date: 14 February 2018
  • Accept Date: 28 May 2016
  • First Publish Date: 22 December 2017
Share
How to cite
  • RIS
  • EndNote
  • Mendeley
  • BibTeX
  • APA
  • MLA
  • HARVARD
  • VANCOUVER
Statistics
  • Article View: 230
  • PDF Download: 249

APA

Maghsoud, F., Yazdani, M. R., Rahimi, M., Malekian, A., & zolfaghari, A. A. (2017). Comparing the performance of Artificial Neural Network (ANN) to predict the long term Meteorological Drought using Climatic Parameters and teleconnection (case study: South of Qazvin Province). Journal of Range and Watershed Managment, 70(4), 1015-1030. doi: 10.22059/jrwm.2018.122721.862

MLA

Fatemeh Maghsoud; Mohammad Reza Yazdani; Mohammad Rahimi; Arash Malekian; ali asghar zolfaghari. "Comparing the performance of Artificial Neural Network (ANN) to predict the long term Meteorological Drought using Climatic Parameters and teleconnection (case study: South of Qazvin Province)". Journal of Range and Watershed Managment, 70, 4, 2017, 1015-1030. doi: 10.22059/jrwm.2018.122721.862

HARVARD

Maghsoud, F., Yazdani, M. R., Rahimi, M., Malekian, A., zolfaghari, A. A. (2017). 'Comparing the performance of Artificial Neural Network (ANN) to predict the long term Meteorological Drought using Climatic Parameters and teleconnection (case study: South of Qazvin Province)', Journal of Range and Watershed Managment, 70(4), pp. 1015-1030. doi: 10.22059/jrwm.2018.122721.862

VANCOUVER

Maghsoud, F., Yazdani, M. R., Rahimi, M., Malekian, A., zolfaghari, A. A. Comparing the performance of Artificial Neural Network (ANN) to predict the long term Meteorological Drought using Climatic Parameters and teleconnection (case study: South of Qazvin Province). Journal of Range and Watershed Managment, 2017; 70(4): 1015-1030. doi: 10.22059/jrwm.2018.122721.862

  • Home
  • About Journal
  • Editorial Board
  • Submit Manuscript
  • Contact Us
  • Glossary
  • Sitemap

News

  • Publication fee 2021-09-23

Newsletter Subscription

Subscribe to the journal newsletter and receive the latest news and updates

© Journal Management System. Powered by Sinaweb