Combining Neural Network and Wavelet Transform to Predict Drought in Iran Using MODIS and TRMM Satellite Data

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.

Mokhtari Dehkordi Ramin; Akhoondzadeh Mahdi

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

Journal Paper

Paper Information

Journal: ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY Year:2020 | Volume:7 | Issue:4 Page(s): 175-191

Download Full-Text

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

Persian Verion

View:

330

Download:

Cites:

Information Journal Paper

Title

Combining Neural Network and Wavelet Transform to Predict Drought in Iran Using MODIS and TRMM Satellite Data

Author(s)

Mokhtari Dehkordi Ramin | Akhoondzadeh Mahdi | Issue Writer Certificate

Keywords

DroughtQ1

Wavelet Artifical Neural NetworksQ2

Time SeriesQ1

NDVIQ2

Abstract

The Drought can be described as a natural disaster in each region. In this study, one of the important factors in Drought, vegetation, has been considered. For this purpose, monthly vegetation cover images and snow cover data of MODIS and TRMM satellite precipitation product from 2009 to 2018 were used for the study area of Iran. After initial preprocessing, we have used artificial neural network method and hybrid neural network and wavelet transform method to predict the normalized difference vegetation index (NDVI). After training the two algorithms using the Time Series of (NDVI) index as well as the Time Series of snow cover and precipitation from 2009 to 2017, the (NDVI) index is predicted for twelve months from 2018, which is finally estimated with real values. The results and prediction accuracy for these two algorithms are different and in general the combined neural network and wavelet transform method has higher accuracy compare to the neural network method so that the twelve average of 2018 is equal to the root mean square error of 0. 055 and coefficient of determination was 0. 804. The results also show that in both methods the accuracy of the index in the early months of 2018 is better than the end months. Therefore, this method can be used to predict this index, as one of the Drought parameters.

Cites

No record.

References

No record.

Cite

APA: Copy

Mokhtari Dehkordi, Ramin, & Akhoondzadeh, Mahdi. (2020). Combining Neural Network and Wavelet Transform to Predict Drought in Iran Using MODIS and TRMM Satellite Data. ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY, 7(4 ), 175-191. SID. https://sid.ir/paper/230153/en

Vancouver: Copy

Mokhtari Dehkordi Ramin, Akhoondzadeh Mahdi. Combining Neural Network and Wavelet Transform to Predict Drought in Iran Using MODIS and TRMM Satellite Data. ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY[Internet]. 2020;7(4 ):175-191. Available from: https://sid.ir/paper/230153/en

IEEE: Copy

Ramin Mokhtari Dehkordi, and Mahdi Akhoondzadeh, “Combining Neural Network and Wavelet Transform to Predict Drought in Iran Using MODIS and TRMM Satellite Data,” ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY, vol. 7, no. 4 , pp. 175–191, 2020, [Online]. Available: https://sid.ir/paper/230153/en

Related Journal Papers