Incorporating large-scale atmospheric variables in long-term seasonal rainfall forecasting using artificial neural networks: An application to the Ping Basin in Thailand

Hydrology Research

Volumn 48, Issue 3, 2017, Pages 867-882

  Babel, M S ^a   Sirisena, T A J G ^a   Singhrattna, N ^b  

^a ASIAN INSTITUTE OF TECHNOLOGY   (Thailand)

^b Thai Royal Military Academy   (Thailand)

Author keywords

Artificial neural network; Large scale atmospheric variables; Representative concentration pathways; Seasonal rainfall forecasting

Indexed keywords

ATMOSPHERIC HUMIDITY; CLIMATE CHANGE; CLIMATE MODELS; FORECASTING; NEURAL NETWORKS; RAIN; WATER RESOURCES;

ATMOSPHERIC VARIABLES; GENERAL CIRCULATION MODEL; MAPPING BIAS; PRECIPITABLE WATER; RAINFALL FORECASTING; REPRESENTATIVE CONCENTRATION PATHWAYS; SEASONAL RAINFALL; WATER RESOURCE PLANNING;

WEATHER FORECASTING;

ABIOTIC FACTOR; ARTIFICIAL NEURAL NETWORK; ATMOSPHERIC FORCING; CLIMATE CHANGE; ERROR CORRECTION; FORECASTING METHOD; LONG-TERM CHANGE; RAINFALL; SEASONAL VARIATION; SPATIOTEMPORAL ANALYSIS; WATER MANAGEMENT; WATER PLANNING; WATER RESOURCE;

NORTHERN REGION [THAILAND]; PING BASIN; THAILAND;

EID: 85020534978     PISSN: 19989563     EISSN: 22247955     Source Type: Journal    
DOI: 10.2166/nh.2016.212     Document Type: Conference Paper

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