This paper proposes a practical approach of a neuro-genetic algorithm to enhance its capability of predicting water levels of rivers. Its practicality has three attributes: (1) to easily develop a model with a neuro-genetic algorithm; (2) to verify the model at various predicting points with different conditions; and (3) to provide information for making urgent decisions on the operation of river infrastructure. The authors build an artificial neural network model coupled with the genetic algorithm (often called a hybrid neuro-genetic algorithm), and then apply the model to predict water levels at 15 points of four major rivers in Korea. This case study demonstrates that the approach can be highly compatible with the real river situations, such as hydrological disturbances and water infrastructure under emergencies. Therefore, proper adoption of this approach into a river management system certainly improves the adaptive capacity of the system.
Skip Nav Destination
Article navigation
Research Article|
July 10 2013
Improving applicability of neuro-genetic algorithm to predict short-term water level: a case study
Gooyong Lee;
Gooyong Lee
1Korea Advanced Institute of Science and Technology (KAIST), 335 Gwahangno, Yuseong-gu, Daejeon 305-701, Republic of Korea
Search for other works by this author on:
Sangeun Lee;
Sangeun Lee
2International Centre for Water Hazard and Risk Management under the Auspices of UNESCO (ICHARM) 1-6 Minamihara, Tsukuba-shi, Ibaraki-ken, 305-8516, Japan
Search for other works by this author on:
Heekyung Park
1Korea Advanced Institute of Science and Technology (KAIST), 335 Gwahangno, Yuseong-gu, Daejeon 305-701, Republic of Korea
E-mail: [email protected]
Search for other works by this author on:
Journal of Hydroinformatics (2014) 16 (1): 218–230.
Article history
Received:
January 18 2013
Accepted:
May 20 2013
Citation
Gooyong Lee, Sangeun Lee, Heekyung Park; Improving applicability of neuro-genetic algorithm to predict short-term water level: a case study. Journal of Hydroinformatics 1 January 2014; 16 (1): 218–230. doi: https://doi.org/10.2166/hydro.2013.011
Download citation file: