Aquifer Water Level Prediction Using Support Vector Machines Method
Publish place: 4th National Congress on Civil Engineering
Publish Year: 1387
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
NCCE04_130
تاریخ نمایه سازی: 19 مهر 1386
Abstract:
In this research, a new data-driven model called Support Vector Machine (SVMs) uses the initial water level measurements, production well extractions, and climate conditions to forecast the final water level elevation in multi-time scale (i.e. daily, weekly, bi-weekly, monthly and bi-monthly) at a specific monitoring well. Due to the fact that SVMs approach does not require the explicit characterization of the physical onditions and input parameters, simulation is made based on the easily quantifiable and measurable variables. This study will demonstrate the prediction capability of SVMs compared to that of ANNs in forecasting the aquifer Water Level Elevation (WLE).
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Authors
Mohsen Behzad
Graduate Student Isfahan University of Technology, Civil Engineering Department
Keyvan Asghari
Assistant Professor Isfahan University of Technology, Civil Engineering Department