EVALUATE THE PERFORMANCE REGRESSION DECISION TREE MODEL IN PREDICTING DROUGHT (CASE STUDY: SYNOPTIC STATION IN SANANDAJ)

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MOZAFARI GHOLAMREZA; SHAFIE SHAHAB; TAGHIZADE ZAHRA

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Journal: JOURNAL OF NATURAL ENVIRONMENT HAZARDS Year:2016 | Volume:4 | Issue:6 Page(s): 1-19

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Title

EVALUATE THE PERFORMANCE REGRESSION DECISION TREE MODEL IN PREDICTING DROUGHT (CASE STUDY: SYNOPTIC STATION IN SANANDAJ)

Author(s)

MOZAFARI GHOLAMREZA | SHAFIE SHAHAB | TAGHIZADE ZAHRA | Issue Writer Certificate

Keywords

DECISION TREEQ2

FORECAST TRAIN FALLQ2

CART ALGORITHMQ1

SANANDAJQ2

Abstract

There are several ways to study drought. Method of analysis rainfall data, Public Sector analysis methods is drought. Therefore, accurate prediction and before the outbreak precipitation could provide the conditions for assessing the drought situation. The purpose of this study is investigating the effect of data preprocessing on the performance of the DECISION TREE model to predict drought in synoptic station in SANANDAJ. In this study, CART ALGORITHMs (Classification and regression tree) has been used as variety of DECISION TREE regression in order to predict precipitation forecast of12months. The data used in this study are the monthly precipitation, relative humidity, the maximum temperature, the average temperature, wind direction and wind speed in a specific statistical period (1970-2010). To assess the created trees in this study, different statistical measures have been sed which in the end results show that in synoptic station in SANANDAJ, DECISION TREE regression model is a relatively efficient model to predict drought in which using a moving averages compared to other states led to Increasing the efficiency of DECISION TREE mode land providing thread just mint in the range of changes, the input data with a high reliability is able to estimate the amount ofprecipitation12months before it occurs which in the simulation carried outing this study, when the five-year moving average of the data has been used to implement the model ,combination of previous rainfall, maximum temperature has been identified as the most appropriate states. The findings show that applying moving average to the original data, dramatically improves the performance of the model. In these circumstances, the DECISION TREE method regression in SANANDAJ station with high reliability level estimate the occurrence of precipitation in 12 months ago.

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APA: Copy

MOZAFARI, GHOLAMREZA, SHAFIE, SHAHAB, & TAGHIZADE, ZAHRA. (2016). EVALUATE THE PERFORMANCE REGRESSION DECISION TREE MODEL IN PREDICTING DROUGHT (CASE STUDY: SYNOPTIC STATION IN SANANDAJ). JOURNAL OF NATURAL ENVIRONMENT HAZARDS, 4(6), 1-19. SID. https://sid.ir/paper/259183/en

Vancouver: Copy

MOZAFARI GHOLAMREZA, SHAFIE SHAHAB, TAGHIZADE ZAHRA. EVALUATE THE PERFORMANCE REGRESSION DECISION TREE MODEL IN PREDICTING DROUGHT (CASE STUDY: SYNOPTIC STATION IN SANANDAJ). JOURNAL OF NATURAL ENVIRONMENT HAZARDS[Internet]. 2016;4(6):1-19. Available from: https://sid.ir/paper/259183/en

IEEE: Copy

GHOLAMREZA MOZAFARI, SHAHAB SHAFIE, and ZAHRA TAGHIZADE, “EVALUATE THE PERFORMANCE REGRESSION DECISION TREE MODEL IN PREDICTING DROUGHT (CASE STUDY: SYNOPTIC STATION IN SANANDAJ),” JOURNAL OF NATURAL ENVIRONMENT HAZARDS, vol. 4, no. 6, pp. 1–19, 2016, [Online]. Available: https://sid.ir/paper/259183/en

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