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A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer
Authors:Heesung Yoon  Seong-Chun Jun  Yunjung Hyun  Gwang-Ok Bae  Kang-Kun Lee
Institution:1. School of Earth and Environmental Sciences, Seoul National University, Seoul 151-747, Republic of Korea;2. GeoGreen21 Co., Ltd., EnC Venture Dream Tower 2nd 901, Seoul 152-719, Republic of Korea;1. Department of Irrigation & Reclamation Engineering, Faculty of Agricultural Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Daneshkadeh St., Karaj, Tehran, Iran;2. Department of Land, Air & Water Resources, Department of Civil & Environmental Engineering, and Department of Biological & Agricultural Engineering, University of California, 139 Veihmeyer Hall, Davis, CA 95616-8628, USA;1. Department of Civil Engineering, Graduate University of Advanced Technology-Kerman, P.O. Box 76315-116, Kerman, Iran;2. Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka, Surathkal, Mangalore 575025, India;1. Department of Range and Watershed Management, Faculty of Natural Resources, University of Guilan, Iran;2. Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China;3. Department of Forestry, Faculty of Natural Resources, University of Guilan, Iran;4. Tarahane Alborz Sabz Company, Rasht, Iran;1. Directorate of Water Management, Bhubaneswar 751 023, Odisha, India;2. AgFE Department, Indian Institute of Technology, Kharagpur 721 302, West Bengal, India;1. Department of Civil Engineering, Gayatri Vidya Parishad College of Engineering, Visakhapatnam, A.P., India;2. Department of Civil Engineering, Indian Institute of Technology, Hauzkhas, New Delhi, India;3. Department of Civil Engineering, Andhra University College of Engineering, Visakhapatnam, A.P., India;4. Department of Electrical Engineering, Indian Institute of Technology, Hauzkhas, New Delhi, India
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