SPRING RAINFALL ESTIMATION OF KHORASAN RAZAVI PROVINCE BASED ON TELE-CONNECTION SYNOPTICALLY PATTERNS USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM

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FALAH GHALHARI GH.A.; HABIBI NOUKHANDAN M.; KHOUASHHAL J.

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Journal: Journal of Range and Watershed Managment Year:2010 | Volume:63 | Issue:1 Page(s): 55-74

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Title

SPRING RAINFALL ESTIMATION OF KHORASAN RAZAVI PROVINCE BASED ON TELE-CONNECTION SYNOPTICALLY PATTERNS USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM

Author(s)

FALAH GHALHARI GH.A. | HABIBI NOUKHANDAN M. | KHOUASHHAL J. | Issue Writer Certificate

Keywords

RAINFALL ESTIMATIONQ2

SYNOPTIC PATTERNSQ3

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMQ3

Abstract

The aim of this research is the assessment of the relation between rainfall and large scale synoptically patterns at Khorasan Razavi province. In this study, using adaptive neuro fuzzy inference system, the RAINFALL ESTIMATION has been done from April to June in the Area under study. Spring rainfall data including the information of 38 synoptic, Climatologic and rain gauge stations from 1970 to 2007 has been selected from Iranian Meteorological Organization and Ministry of Energy. In this paper, we are analyzed 38 years of rainfall data at Khorasan Razavi province located in northeastern part of Iran at latitude-longitude pairs (34o-38oN, 56o-62oE). The ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM based on synoptically patterns with 38 years of rainfall data was trained. For performance evaluation, network predicted outputs were compared with the actual rainfall data. In this Study, at the first step, the relationship Between synoptically pattern variations including Sea Level Pressure (SLP), Sea Surface Temperature (SST), Sea Surface Pressure Difference (SLP), Sea Surface Temperature Difference (SST), air temperature at 700 hpa, thickness between 500 and 1000 hpa level, relative humidity at 300 hpa and precipitable water were investigated .As the second step, the model was calibrated from 1970 to 1997. Finally, rainfall prediction is performed from 1998 to 2007. The model that used in this research has an input layer, one hidden layer and an output layer. The number of neuron for input layer, hidden layer and output layer was 13-28-1, respectively. The results of simulation reveal that adaptive neuro fuzzy inference systems are promising and efficient.

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APA: Copy

FALAH GHALHARI, GH.A., HABIBI NOUKHANDAN, M., & KHOUASHHAL, J.. (2010). SPRING RAINFALL ESTIMATION OF KHORASAN RAZAVI PROVINCE BASED ON TELE-CONNECTION SYNOPTICALLY PATTERNS USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM. JOURNAL OF RANGE AND WATERSHED MANAGEMENT (IRANIAN JOURNAL OF NATURAL RESOURCES), 63(1), 55-74. SID. https://sid.ir/paper/162344/en

Vancouver: Copy

FALAH GHALHARI GH.A., HABIBI NOUKHANDAN M., KHOUASHHAL J.. SPRING RAINFALL ESTIMATION OF KHORASAN RAZAVI PROVINCE BASED ON TELE-CONNECTION SYNOPTICALLY PATTERNS USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM. JOURNAL OF RANGE AND WATERSHED MANAGEMENT (IRANIAN JOURNAL OF NATURAL RESOURCES)[Internet]. 2010;63(1):55-74. Available from: https://sid.ir/paper/162344/en

IEEE: Copy

GH.A. FALAH GHALHARI, M. HABIBI NOUKHANDAN, and J. KHOUASHHAL, “SPRING RAINFALL ESTIMATION OF KHORASAN RAZAVI PROVINCE BASED ON TELE-CONNECTION SYNOPTICALLY PATTERNS USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM,” JOURNAL OF RANGE AND WATERSHED MANAGEMENT (IRANIAN JOURNAL OF NATURAL RESOURCES), vol. 63, no. 1, pp. 55–74, 2010, [Online]. Available: https://sid.ir/paper/162344/en

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