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Application of Intelligent Models and LARS-WG in Prediction of River Discharge
Authors: Bayat-Varkeshi, mayam , Najib, Reza , Mohammadi, Kourosh , Golmohammadi, Golmar
Identifier: CSBE18170
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Published in: CSBE-SCGAB Technical Conferences » AGM Guelph 2018
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Description: Water scarcity is a common problem in arid and semi-arid regions around the world, these days. Therefore, the need for an informed decision making system has become more essential than before. In this study, the effects of climate change on hydrology parameters of Khoramabad River located in the southwest of Iran was evaluated. For this purpose, at first, the weather information was predicted using LARS-WG software under three scenarios A1B, A2 and B1 from 2011 until 2113. The results indicated that the minimum and maximum temperatures and evaporation during future years will increase. In the scenario A2, the precipitation will have decreasing trend and solar radiation will have increasing trend. The flow rate of river under different scenarios were estimated using the relationship between weather parameters and recorded flow rates in different artificial intelligent models. The predicted flow rates using intelligent models showed that the accuracy of the co-active neuro fuzzy inference system (CANFIS) model was higher than that in the artificial neural network (ANN) and ANN-GA (genetic algorithm) models. The results of the flow rate predictions by the optimized structure of CANFIS showed that the minimum and maximum flow will occur in scenario A1B and in scenario B1, respectively. According to the results, in summers of 2080 to 2113, the river has minimum flow and since the highest demand in the region is in summer, the water shortage is a major problem in that season.
Keywords: Climate change, LARS-WG, Artificial Neural Network, River flow
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Date: 2018-07-22
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Conference name: CSBE/SCGAB 2018 Annual Conference, School of Engineering, University of Guelph, Guelph, Ontario, July 22-25 2018.
Session name: Soil and Water Management III
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Type: Text.Article
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Publication type: Technical conference
Source:
Relation:
Coverage: Canada
Language 1: en
Language 2:
Rights: Canadian Society for Bioengineering
Notes:
Other files:
DOI:
Description: Water scarcity is a common problem in arid and semi-arid regions around the world, these days. Therefore, the need for an informed decision making system has become more essential than before. In this study, the effects of climate change on hydrology parameters of Khoramabad River located in the southwest of Iran was evaluated. For this purpose, at first, the weather information was predicted using LARS-WG software under three scenarios A1B, A2 and B1 from 2011 until 2113. The results indicated that the minimum and maximum temperatures and evaporation during future years will increase. In the scenario A2, the precipitation will have decreasing trend and solar radiation will have increasing trend. The flow rate of river under different scenarios were estimated using the relationship between weather parameters and recorded flow rates in different artificial intelligent models. The predicted flow rates using intelligent models showed that the accuracy of the co-active neuro fuzzy inference system (CANFIS) model was higher than that in the artificial neural network (ANN) and ANN-GA (genetic algorithm) models. The results of the flow rate predictions by the optimized structure of CANFIS showed that the minimum and maximum flow will occur in scenario A1B and in scenario B1, respectively. According to the results, in summers of 2080 to 2113, the river has minimum flow and since the highest demand in the region is in summer, the water shortage is a major problem in that season.
Keywords: Climate change, LARS-WG, Artificial Neural Network, River flow
Résumé:
Mots-clés:
Citation:
Volume:
Issue:
Pages -
Contributor:
Date: 2018-07-22
Technical field:
Conference name: CSBE/SCGAB 2018 Annual Conference, School of Engineering, University of Guelph, Guelph, Ontario, July 22-25 2018.
Session name: Soil and Water Management III
Other information:
Type: Text.Article
Format:
Publication type: Technical conference
Source:
Relation:
Coverage: Canada
Language 1: en
Language 2:
Rights: Canadian Society for Bioengineering
Notes:
Other files:
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