AGRICULTURAL DROUGHT RISK ASSESSMENT MODEL FOR KERMANSHAH PROVINCE, USING STATISTICAL AND INTELLIGENT METHODS

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.

ARSHAD S.; MORID S.; MOBASHERI M.R.; AGHA ALIKHANI M.

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

Journal Paper

Paper Information

Journal: JOURNAL OF AGRICULTURAL ENGINEERING RESEARCH Year:2008 | Volume:9 | Issue:3 Page(s): 1-22

Download Full-Text

Persian Verion

View:

814

Download:

Cites:

Information Journal Paper

Title

AGRICULTURAL DROUGHT RISK ASSESSMENT MODEL FOR KERMANSHAH PROVINCE, USING STATISTICAL AND INTELLIGENT METHODS

Author(s)

ARSHAD S. | MORID S. | MOBASHERI M.R. | AGHA ALIKHANI M. | Issue Writer Certificate

Keywords

AGRICULTURAL DROUGHTQ3

GENETIC ALGORITHMQ3

KERMANSHAH PROVINCEQ3

PRINCIPLE COMPONENT ANALYSISQ3

VULNERABILITY ASSESSMENTQ3

Abstract

The agriculture sector has been affected by severe drought in recent years, making development of a drought warning system for agriculture crucial. Such a system can be a useful tool for policy makers and investors. This research develops a model for AGRICULTURAL DROUGHT risk assessment using statistical and intelligent methods. KERMANSHAH PROVINCE, a major rain-fed region of Iran, was selected as the study area. The model is specific to rain-fed wheat and was updated during the different phonological stages of the growing season. The inputs are a combination of the PDSI, Z-index, CMI, SPI and EDI drought indices which were selected using GENETIC ALGORITHM and artificial neural networking techniques. The results show that the Z-index better predicts possible losses. The general performance of the model increased toward the end of the growing season, especially after the third stage, when the significance level of the relation reaches 1% and the results become more reliable. Furthermore, linkage of the model to the geographical information system makes it more capable of spatial analysis and more suitable for presentation of the final results.

Cites

No record.

References

No record.

Cite

APA: Copy

ARSHAD, S., MORID, S., MOBASHERI, M.R., & AGHA ALIKHANI, M.. (2008). AGRICULTURAL DROUGHT RISK ASSESSMENT MODEL FOR KERMANSHAH PROVINCE, USING STATISTICAL AND INTELLIGENT METHODS. JOURNAL OF AGRICULTURAL ENGINEERING RESEARCH, 9(3), 1-22. SID. https://sid.ir/paper/28088/en

Vancouver: Copy

ARSHAD S., MORID S., MOBASHERI M.R., AGHA ALIKHANI M.. AGRICULTURAL DROUGHT RISK ASSESSMENT MODEL FOR KERMANSHAH PROVINCE, USING STATISTICAL AND INTELLIGENT METHODS. JOURNAL OF AGRICULTURAL ENGINEERING RESEARCH[Internet]. 2008;9(3):1-22. Available from: https://sid.ir/paper/28088/en

IEEE: Copy

S. ARSHAD, S. MORID, M.R. MOBASHERI, and M. AGHA ALIKHANI, “AGRICULTURAL DROUGHT RISK ASSESSMENT MODEL FOR KERMANSHAH PROVINCE, USING STATISTICAL AND INTELLIGENT METHODS,” JOURNAL OF AGRICULTURAL ENGINEERING RESEARCH, vol. 9, no. 3, pp. 1–22, 2008, [Online]. Available: https://sid.ir/paper/28088/en

Related Journal Papers