Statistical downscaling of global climate model outputs to monthly precipitation via extreme learning machine: A case study. Issue 5 (22nd January 2018)

• Record Type: Journal Article
• Authors: Alizamir, Meysam; Azhdary Moghadam, Mehdi; Hashemi Monfared, Arman; Shamsipour, Aliakbar
• Is Part Of: Environmental progress & sustainable energy. Volume 37:Issue 5(2018)
• Page Start: 1853
• Page End: 1862
• Publication Date: 2018-01-22
• Subjects: climate change -- extreme learning machine -- artificial neural network -- genetic programming -- general circulation model -- statistical downscaling; Environmental engineering -- Periodicals; Sustainable engineering -- Periodicals; Environmental chemistry -- Periodicals; 333.7
• DOI: 10.1002/ep.12856
• Languages: English
• ISSNs: 1944-7442

Abstract

Abstract : The present article explores the impacts of climate change on precipitation at station scale in the Minab basin, Iran. The data used for evaluation were large‐scale input (predictor) parameters extracted from the reanalysis data set of the National Center for Environmental Prediction and National Center for Atmospheric Research to downscale monthly precipitation. In this research, four approaches were applied to downscale precipitation, including an implementation on extreme learning machine (ELM) for single‐hidden layer feedforward neural network, artificial neural network, genetic programming, and quantile mapping. The results indicated that the ELM approach outperformed all other approaches in downscaling the large‐scale global climate model atmospheric variables to monthly precipitation at station scale. © 2018 American Institute of Chemical Engineers Environ Prog, 37: 1853–1862, 2018