MONTHLY RAINFALL PREDICTION USING ARTIFICIAL NEURAL NETWORKS AND M5 MODEL TREE (CASE STUDY: STATIONS OF AHAR AND JOLFA)

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SATTARI M. TAGHI; NAHREIN FARNAZ

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Journal: Journal of Irrigation and Water Engineering Year:2014 | Volume:4 | Issue:14 Page(s): 83-98

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Title

MONTHLY RAINFALL PREDICTION USING ARTIFICIAL NEURAL NETWORKS AND M5 MODEL TREE (CASE STUDY: STATIONS OF AHAR AND JOLFA)

Author(s)

SATTARI M. TAGHI | NAHREIN FARNAZ | Issue Writer Certificate

Keywords

PREDICTION OF MONTH SCALE RAINFALLQ2

ARTIFICIAL NEURAL NETWORKSQ2

GENETIC PROGRAMINGQ2

M5 TREE MODELQ2

AHAR AND JOLFAQ2

Abstract

Rainfall has been one of the most important agents in water cycle which has an effective rule in each region characters measurement. PREDICTION OF MONTH SCALE RAINFALL is important for main goals as torrent estimating, drought, run-off, sediment, irrigation programming and also manage the drainage basins. Rainfall measure prediction in each area mediated by punctual data measured of humidity, temperature, barograph manometers, wind speed and etc. the limitations such as unavailable enough data about rainfall measure on a scale of time and location and also complicated boundaries among meteorology agents related to rainfall, caused to inexact and non trustable amount based on unusual manners. In this research, firstly the description of different concepts of meteorology parameters on month scale in AHAR AND JOLFA regions, EAST AZARBAIJAN, have been explained in which the entrance ARTIFICIAL NEURAL NETWORKS, Genetic Programming and M5 TREE MODEL have been defined too. Then, the best concept has been chosen for each model according to both R and RMSE statistics. In Ahar station Genetic Programming approach with (R=0.88) and (RMSE=3.32), also in Jolfa station Genetic Programming approach with (R=0.87) and (RMSE=3.79) presented the best results. The conclusion determined that each mentioned approaches presents the comparatively exact result for rainfall prediction in region but due to having simple liner models and understandable with M5 TREE MODEL, this approach would be considerate as an efficient application and substitutes for rainfall measurement.

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APA: Copy

SATTARI, M. TAGHI, & NAHREIN, FARNAZ. (2014). MONTHLY RAINFALL PREDICTION USING ARTIFICIAL NEURAL NETWORKS AND M5 MODEL TREE (CASE STUDY: STATIONS OF AHAR AND JOLFA). IRANIAN OF IRRIGATION & WATER ENGINEERING, 4(14), 83-98. SID. https://sid.ir/paper/247287/en

Vancouver: Copy

SATTARI M. TAGHI, NAHREIN FARNAZ. MONTHLY RAINFALL PREDICTION USING ARTIFICIAL NEURAL NETWORKS AND M5 MODEL TREE (CASE STUDY: STATIONS OF AHAR AND JOLFA). IRANIAN OF IRRIGATION & WATER ENGINEERING[Internet]. 2014;4(14):83-98. Available from: https://sid.ir/paper/247287/en

IEEE: Copy

M. TAGHI SATTARI, and FARNAZ NAHREIN, “MONTHLY RAINFALL PREDICTION USING ARTIFICIAL NEURAL NETWORKS AND M5 MODEL TREE (CASE STUDY: STATIONS OF AHAR AND JOLFA),” IRANIAN OF IRRIGATION & WATER ENGINEERING, vol. 4, no. 14, pp. 83–98, 2014, [Online]. Available: https://sid.ir/paper/247287/en

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