TIME SERIES RECONSTRUCTION OF DAILY MAXIMUM AND MINIMUM TEMPERATURE USING NEAREST NEIGHBORHOOD AND ARTIFICIAL NEURAL NETWORK TECHNIQUES (CASE STUDY: WEST OF TEHRAN PROVINCE)

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

KHORSHIDDOUST ALI MOHAMMAD; NASSAJI ZAVVAREH MOJTABA; GHERMEZ CHESHMEH BAGHER

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: GEOGRAPHIC SPACE Year:2012 | Volume:12 | Issue:38 Page(s): 197-214

Download Full-Text

Persian Verion

View:

1,001

Download:

Cites:

Information Journal Paper

Title

TIME SERIES RECONSTRUCTION OF DAILY MAXIMUM AND MINIMUM TEMPERATURE USING NEAREST NEIGHBORHOOD AND ARTIFICIAL NEURAL NETWORK TECHNIQUES (CASE STUDY: WEST OF TEHRAN PROVINCE)

Author(s)

KHORSHIDDOUST ALI MOHAMMAD | NASSAJI ZAVVAREH MOJTABA | GHERMEZ CHESHMEH BAGHER | Issue Writer Certificate

Keywords

TIME SERIESQ2

DATA RECONSTRUCTIONQ2

DAILY MAXIMUM AND MINIMUM TEMPERATUREQ2

ARTIFICIAL NEURAL NETWORK METHODQ2

NEAREST NEIGHBOR METHODQ2

Abstract

Long TIME SERIES are needed for analysis of time variation, trend of extreme events, risk estimation and possible events. One of the most important TIME SERIES in geographical and climatic science is DAILY MAXIMUM AND MINIMUM TEMPERATURE. These two parameters use daily evapotranspiration estimation, determination of water balance and climate change study. Maximum and minimum temperature are measured in meteorological stations. However, different statistical years, deficiency in statistical data and error of measurement cause variation in TIME SERIES. Therefore, reconstruction of TIME SERIES is very important. This research evaluates reconstruction of daily extreme temperatures to nearest neighbor and ARTIFICIAL NEURAL NETWORK METHODs for five stations in the west of Tehran Province. In the nearest neighborhood method correlation between respective maximum or minimum temperature is used. Whilst in the artificial neural network using meteorological stations network the minimum and maximum daily temperature are reconstituted. Neural network used in this research is a multilayer feed forward network with back propagation algorithm and hidden layer.Results show that ARTIFICIAL NEURAL NETWORK METHOD had least mean absolute error for all stations compared to the NEAREST NEIGHBOR METHOD. With increasing distance of the station the estimated error increases in the NEAREST NEIGHBOR METHOD. Accuracy of the two methods in estimating daily maximum is more than the daily minimum temperature.

Cites

No record.

References

Cite

APA: Copy

KHORSHIDDOUST, ALI MOHAMMAD, NASSAJI ZAVVAREH, MOJTABA, & GHERMEZ CHESHMEH, BAGHER. (2012). TIME SERIES RECONSTRUCTION OF DAILY MAXIMUM AND MINIMUM TEMPERATURE USING NEAREST NEIGHBORHOOD AND ARTIFICIAL NEURAL NETWORK TECHNIQUES (CASE STUDY: WEST OF TEHRAN PROVINCE). GEOGRAPHIC SPACE, 12(38), 197-214. SID. https://sid.ir/paper/91532/en

Vancouver: Copy

KHORSHIDDOUST ALI MOHAMMAD, NASSAJI ZAVVAREH MOJTABA, GHERMEZ CHESHMEH BAGHER. TIME SERIES RECONSTRUCTION OF DAILY MAXIMUM AND MINIMUM TEMPERATURE USING NEAREST NEIGHBORHOOD AND ARTIFICIAL NEURAL NETWORK TECHNIQUES (CASE STUDY: WEST OF TEHRAN PROVINCE). GEOGRAPHIC SPACE[Internet]. 2012;12(38):197-214. Available from: https://sid.ir/paper/91532/en

IEEE: Copy

ALI MOHAMMAD KHORSHIDDOUST, MOJTABA NASSAJI ZAVVAREH, and BAGHER GHERMEZ CHESHMEH, “TIME SERIES RECONSTRUCTION OF DAILY MAXIMUM AND MINIMUM TEMPERATURE USING NEAREST NEIGHBORHOOD AND ARTIFICIAL NEURAL NETWORK TECHNIQUES (CASE STUDY: WEST OF TEHRAN PROVINCE),” GEOGRAPHIC SPACE, vol. 12, no. 38, pp. 197–214, 2012, [Online]. Available: https://sid.ir/paper/91532/en

Related Journal Papers