Applied Mathematics and Nonlinear Sciences
Journal license

Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 6, Issue 2


Published
on

November 22, 2021


Pages

275-284


DOI

Article

Fractional Linear Regression Equation in Agricultural Disaster Assessment Model Based on Geographic Information System Analysis Technology

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Authors

Lihua Wen Affiliation:
Department of Geography, Handan University, Handan 056001, China
, Hongyao Liu Affiliation:
Handan Institute of Agricultural Science, Handan 056001, China
, Jihong Chen Affiliation:
HeBei Gao Xiang Geographic Information Technology Service Co. Ltd, Handan 056001, China
, Bahjat Fakieh Affiliation:
Information System Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
and Samer M. Shorman Affiliation:
Department of Accounting and Finace, Faculty of Administrative Sciences, Applied Science University, Al Eker, Kingdom of Bahrain


Abstract

This article combines geographic information system (GIS) technology and database technology to analyse agricultural, natural disasters. The article uses a fractional linear regression equation to define the comprehensive intensity grading standard of the disaster-causing factors of torrential rain. At the same time, we use GIS to superimpose the agricultural vulnerability index into the storm disaster risk zoning to obtain the degree of agricultural impact under different levels of risk. At the end of the thesis, the model is applied to actual case analysis to verify the effectiveness of the algorithm model.


Keywords

Heavy rain, hazard factors, geographic information system, disaster risk, fractional linear regression equation, quantitative evaluation, 62J05


Citation

Wen, L., Liu, H., Chen, J., Fakieh, B., & Shorman, S. M. (2021). Fractional linear regression equation in agricultural disaster assessment model based on geographic information system analysis technology. Applied Mathematics and Nonlinear Sciences, 6(2), 275–284. https://doi.org/10.2478/amns.2021.2.00096

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