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SELANGOR, Malaysia, July 21, 2016 - (ACN Newswire) - A computer model that can "learn" similarly to the human brain could help water resource managers mitigate damage in cases of extreme flooding, according to research published in the Pertanika Journal of Science & Technology.
 | | "The obtained results could help the water resource managers to operate the reservoir properly in the case of extreme events such as flooding and drought," write the researchers in their study. (Copyright: 123rf.com) |
Artificial neural networks (ANNs) are a biologically-inspired method of computing that can receive large amounts of data, find patterns, learn from them and then develop predictions for future events. They have been proposed as a useful tool to process the complex relationships between large amounts of data related to the transformation of rainfall into runoff. This relationship is one of the most difficult hydrological problems faced by water resource managers.
Researchers at Universiti Putra Malaysia "taught" an ANN to predict daily runoff for the Bertam River into the Ringlet Reservoir 200 km north of Kuala Lumpur. They collected daily rainfall and stream flow data from the Bertam River catchment area over a ten-year period, from 2003 to 2012, and estimated daily water evaporation using temperature data collected from the nearest station to the reservoir. Seventy percent of this data was input into the model to "train" it while the remaining 30% of the data was used to test the model's accuracy using statistical evaluation measurements. The ANN was developed to map the relationship between rainfall and runoff. The more factors used, the more accurate the results. The ANN was able to predict river stream flow into the reservoir with 76% accuracy.
"The results indicate that the artificial neural network is a powerful tool in modelling rainfall-runoff," write the researchers in their study. "The obtained results could help the water resource managers to operate the reservoir properly in the case of extreme events such as flooding and drought," they add.
The ANN's predictive power could be improved by including additional inputs such as deforestation, agricultural activities and land use, the researchers say.
For more information about this research, please contact:
Aida Tayebiyan
Department of Civil Engineering
Faculty of Engineering
Universiti Putra Malaysia
43400 Serdang, Selangor, Malaysia
Email: ida_tayebiyan@yahoo.com; aaiiddaa1114@gmail.com
Mobile: +6012 342 6010
About Pertanika Journal of Science & Technology (JST)
Pertanika Journal of Science & Technology (JST) is published by Universiti Putra Malaysia in English and is open to authors around the world regardless of nationality. Currently, it is published twice a year in January and July. Other Pertanika series include Pertanika Journal of Tropical Agricultural Science (JTAS), and Pertanika Journal of Social Sciences & Humanities (JSSH).
Pertanika Journal of Science & Technology aims to provide a forum for high quality research related to science and engineering research. Areas relevant to the scope of the journal include: bioinformatics, bioscience, biotechnology and bio-molecular sciences, chemistry, computer science, ecology, engineering, engineering design, environmental control and management, mathematics and statistics, medicine and health sciences, nanotechnology, physics, safety and emergency management, and related fields of study.
Website: http://www.pertanika.upm.edu.my/
The paper is available from this link: http://bit.ly/29YHT7g
For more information about the journal, contact:
The Chief Executive Editor (UPM Journals)
Office of the Deputy Vice Chancellor (R&I)
IDEA Tower 2, UPM-MDTC Technology Centre
Universiti Putra Malaysia
43400 Serdang, Selangor
Malaysia
Phone: +603 8947 1622 | +6016 217 4050
Email: nayan@upm.my
Press release distributed by ResearchSEA for Pertanika Journal.
Topic: Research and development
Source: Pertanika Journal
Sectors: Science & Research, Daily News
https://www.acnnewswire.com
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