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Information Journal Paper

Title

TECHNICAL NOTE: FORECASTING GROUNDWATER LEVEL IN SAADAT-SHAHR PLAIN, IRAN, USING ARTIFICIAL NEURAL NETWORKS

Pages

  82-86

Abstract

 A proper architectural design of the ARTIFICIAL NEURAL NETWORK (ANN) models can provide a robust tool in water resources modeling and forecasting. The performance of different neural networks in a GROUNDWATER LEVEL FORECASTING was examined by researchers in order to identify an optimal ANN architecture that can provide accurate predictions up to 24 months ahead. In this study the SAADAT-SHAHR PLAIN in Fars Province in central Iran was chosen as the study area. All networks were trained for an 8-year period of data and calibrated for a 24-month period. Experimental results showed that the most accurate forecast (for up to 24 months ahead) is achieved with an FNN trained with the LM algorithm.

Cites

References

Cite

APA: Copy

NIKMANESH, M.R., & RAKHSHANDEHROO, G.R.. (2011). TECHNICAL NOTE: FORECASTING GROUNDWATER LEVEL IN SAADAT-SHAHR PLAIN, IRAN, USING ARTIFICIAL NEURAL NETWORKS. IRAN-WATER RESOURCES RESEARCH, 7(1 (19)), 82-86. SID. https://sid.ir/paper/100032/en

Vancouver: Copy

NIKMANESH M.R., RAKHSHANDEHROO G.R.. TECHNICAL NOTE: FORECASTING GROUNDWATER LEVEL IN SAADAT-SHAHR PLAIN, IRAN, USING ARTIFICIAL NEURAL NETWORKS. IRAN-WATER RESOURCES RESEARCH[Internet]. 2011;7(1 (19)):82-86. Available from: https://sid.ir/paper/100032/en

IEEE: Copy

M.R. NIKMANESH, and G.R. RAKHSHANDEHROO, “TECHNICAL NOTE: FORECASTING GROUNDWATER LEVEL IN SAADAT-SHAHR PLAIN, IRAN, USING ARTIFICIAL NEURAL NETWORKS,” IRAN-WATER RESOURCES RESEARCH, vol. 7, no. 1 (19), pp. 82–86, 2011, [Online]. Available: https://sid.ir/paper/100032/en

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