ARTIFICIAL NEURAL NETWORK TECHNIQUE FOR RAINFALL TEMPORAL DISTRIBU-TION SIMULATION (CASE STUDY: KECHIK REGION)

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GHOLAMI V.; DARVARI Z.; MOHSENI SARAVI M.

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Paper Information

Journal: CASPIAN JOURNAL OF ENVIRONMENTAL SCIENCES (CJES) Year:2015 | Volume:13 | Issue:1 Page(s): 53-60

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Title

ARTIFICIAL NEURAL NETWORK TECHNIQUE FOR RAINFALL TEMPORAL DISTRIBU-TION SIMULATION (CASE STUDY: KECHIK REGION)

Author(s)

GHOLAMI V. | DARVARI Z. | MOHSENI SARAVI M. | Issue Writer Certificate

Keywords

INTENSITYQ3

RAINFALLQ3

ALGORITHMQ3

ANNQ3

KECHIK STATIONQ3

Abstract

Artificial neural networks (ANNs) have become one of the most promising tools for RAINFALL simulation since a few years ago. However, most of the researchers have focused on RAINFALL INTENSITY records as well as on watersheds, which generally are utilized as input records of other hydro-meteorological variables. The present study was conducted in KECHIK STATION, Golestan Province (northern Iran). The normal multi-layer perceptron form of ANN (MLP-ANN) was selected as the baseline ANN model. The efficiency of GDX, CG and L-M training ALGORITHMs were compared to improve computed performances. The inputs of ANN included temperature, evaporation, air pressure, humidity and wind velocity in a 10 minute increment The results revealed that the L-M ALGORITHM was more efficient than the CG and GDX ALGORITHM, so it was used for training six ANN models for RAINFALL INTENSITY forecasting. The results showed that all of the parameters were proper in-puts for simulating RAINFALL, but temperature, evaporation and moisture were the most important factors in RAINFALL occurrence.

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APA: Copy

GHOLAMI, V., DARVARI, Z., & MOHSENI SARAVI, M.. (2015). ARTIFICIAL NEURAL NETWORK TECHNIQUE FOR RAINFALL TEMPORAL DISTRIBU-TION SIMULATION (CASE STUDY: KECHIK REGION). CASPIAN JOURNAL OF ENVIRONMENTAL SCIENCES (CJES), 13(1 ), 53-60. SID. https://sid.ir/paper/240763/en

Vancouver: Copy

GHOLAMI V., DARVARI Z., MOHSENI SARAVI M.. ARTIFICIAL NEURAL NETWORK TECHNIQUE FOR RAINFALL TEMPORAL DISTRIBU-TION SIMULATION (CASE STUDY: KECHIK REGION). CASPIAN JOURNAL OF ENVIRONMENTAL SCIENCES (CJES)[Internet]. 2015;13(1 ):53-60. Available from: https://sid.ir/paper/240763/en

IEEE: Copy

V. GHOLAMI, Z. DARVARI, and M. MOHSENI SARAVI, “ARTIFICIAL NEURAL NETWORK TECHNIQUE FOR RAINFALL TEMPORAL DISTRIBU-TION SIMULATION (CASE STUDY: KECHIK REGION),” CASPIAN JOURNAL OF ENVIRONMENTAL SCIENCES (CJES), vol. 13, no. 1 , pp. 53–60, 2015, [Online]. Available: https://sid.ir/paper/240763/en

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