SPRING FROST PREDICTION USING NEURAL NETWORK MULTILAYER PERCEPTRON (MLP) AND IT'S IMPACT ON THE PUBLIC TRANSPORTATION IN KHORRAMABAD CITY

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TAGHAVI SAEIDEH; OMIDZADEH HANIYEH

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Journal: ENVIRONMENTAL BASED TERRITORIAL PLANNING (AMAYESH) Year:2015 | Volume:8 | Issue:28 Page(s): 111-124

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Information Journal Paper

Title

SPRING FROST PREDICTION USING NEURAL NETWORK MULTILAYER PERCEPTRON (MLP) AND IT'S IMPACT ON THE PUBLIC TRANSPORTATION IN KHORRAMABAD CITY

Author(s)

TAGHAVI SAEIDEH | OMIDZADEH HANIYEH | Issue Writer Certificate

Keywords

FORECASTINGQ2

ARTIFICIAL NEURAL NETWORKSQ2

FROSTQ2

URBAN TRANSPORTATIONQ2

KHORRAMABADQ2

Abstract

Transportation system within the city as an important tool and an engine has come of age in urban development as well as the local economy. It is claimed that if urban communities today has convenient transport facilities urban Infrastructure will not face serious problems. In this regard, efforts and attempts that climatologists induce seem to be justified. In this research we have followed the same heritage and have looked at the influential role of transportation by analyzing the panel data and predicting the serious problems conceived for future. The panel data provided us with the model and made it possible to predict the future across a Network of Artificial Neural System. In this study, we tried to predict Spring FROST for KHORRAMABAD station using Multilayer Perceptron (MLP) and investigate its impact on urban transport. Attempts were.made to detect the occurrence of an event rather than mitigation of the events and its disorders possible to happen. In order to achieve these important variables of monthly minimum means and maximum temperature means, along with the minimum and maximum means of the relative humidity, sunshine hours and the total monthly mean of precipitation during the past 28 years (2009-1981) for the prediction of the minimum temperatures in April and May months years 2010 to 2012 were analyzed and compared with the actual data. In so doing, the features and functions available in the MATLAB software were used to evaluate the network performance parameters such as the relative percent of error and coefficient of correlation.Results indicate a 80% probability of occurrence of spring FROSTs and 1.49 year return period of 187 days in April 7. The maximum error of the model with real data was 0.10oC which was lower than the remarkable ability of artificial neural network prediction models. Therefore, according to the climatic phenomenon the results could be used to provide help for the management and the development of URBAN TRANSPORTATION priority.

Cites

ANALYSIS OF CHANGES USING THE METHOD OF MANN-KENDALL (CASE STUDY OF FOUR TOWNSHIPS OF CHAHARMAHAL AND BAKHTIARI PROVINCE)

References

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APA: Copy

TAGHAVI, SAEIDEH, & OMIDZADEH, HANIYEH. (2015). SPRING FROST PREDICTION USING NEURAL NETWORK MULTILAYER PERCEPTRON (MLP) AND IT'S IMPACT ON THE PUBLIC TRANSPORTATION IN KHORRAMABAD CITY. ENVIRONMENTAL BASED TERRITORIAL PLANNING (AMAYESH), 8(28), 111-124. SID. https://sid.ir/paper/130723/en

Vancouver: Copy

TAGHAVI SAEIDEH, OMIDZADEH HANIYEH. SPRING FROST PREDICTION USING NEURAL NETWORK MULTILAYER PERCEPTRON (MLP) AND IT'S IMPACT ON THE PUBLIC TRANSPORTATION IN KHORRAMABAD CITY. ENVIRONMENTAL BASED TERRITORIAL PLANNING (AMAYESH)[Internet]. 2015;8(28):111-124. Available from: https://sid.ir/paper/130723/en

IEEE: Copy

SAEIDEH TAGHAVI, and HANIYEH OMIDZADEH, “SPRING FROST PREDICTION USING NEURAL NETWORK MULTILAYER PERCEPTRON (MLP) AND IT'S IMPACT ON THE PUBLIC TRANSPORTATION IN KHORRAMABAD CITY,” ENVIRONMENTAL BASED TERRITORIAL PLANNING (AMAYESH), vol. 8, no. 28, pp. 111–124, 2015, [Online]. Available: https://sid.ir/paper/130723/en

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