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Please use this identifier to cite or link to this item: http://repository.li.mahidol.ac.th/dspace/handle/123456789/14204
Title: Estimation of crop coefficient of corn (Kc<inf>corn</inf>) under climate change scenarios using data mining technique
Authors: Kampanad Bhaktikul
Rommanee Anujit
Jongdee To-im
Mahidol University. Faculty of Environment and Resource Studies
Keywords: Environmental Science;Pharmacology, Toxicology and Pharmaceutics
Issue Date: 1-Jan-2012
Citation: EnvironmentAsia. Vol.5, No.1 (2012), 56-62
Abstract: The main objectives of this study are to determine the crop coefficient of corn (Kc corn ) using data mining technique under climate change scenarios, and to develop the guidelines for future water management based on climate change scenarios. Variables including date, maximum temperature, minimum temperature, precipitation, humidity, wind speed, and solar radiation from seven meteorological stations during 1991 to 2000 were used. Cross-Industry Standard Process for Data Mining (CRISP-DM) was applied for data collection and analyses. The procedures compose of investigation of input data, model set up using Artificial Neural Networks (ANNs), model evaluation, and finally estimation of the Kc corn . Three climate change scenarios of carbon dioxide (CO 2 ) concentration level: 360 ppm, 540 ppm, and 720 ppm were set. The results indicated that the best number of node of input layer - hidden layer - output layer was 7-13-1. The correlation coefficient of model was 0.99. The predicted Kc corn revealed that evapotranspiration (ET corn ) pattern will be changed significantly upon CO2 concentration level. From the model predictions, ETcorn will be decreased 3.34% when CO 2 increased from 360 ppm to 540 ppm. For the double CO2 concentration from 360 ppm to 720 ppm, ET corn will be increased 16.13%. The future water management guidelines to cope with the climate change are suggested.
URI: http://repository.li.mahidol.ac.th/dspace/handle/123456789/14204
metadata.dc.identifier.url: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=82755162360&origin=inward
ISSN: 19061714
Appears in Collections:Scopus 2011-2015

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