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370402

PREDICTING STANDARDIZED PRECIPITATION EVAPOTRANSPIRATION INDEX (SPEI) USING SVM AND ARIMA MODELS: A COMPARATIVE STUDY

Article

Last updated: 26 Dec 2024

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Tags

Agricultural Irrigation and Drainage Engineering

Abstract

This study evaluates the predictive performance of Support Vector Machines (SVM) and Autoregressive Integrated Moving Average (ARIMA) models in forecasting the Standardized Precipitation Evapotranspiration Index (SPEI) for three critical agricultural regions in Egypt: Nubariyah, Wadi Al-Natrun, and Al-Boseli. Accurate SPEI forecasting is necessary for effective water management and agricultural planning, especially in arid regions. Through comprehensive analysis involving time series decomposition and model evaluation using Mean Squared Error (MSE) and Mean Absolute Error (MAE), ARIMA models consistently outperformed SVM models across all locations. The ARIMA (1,1,1) model showed superior predictive accuracy, with MSE reductions ranging from 1.4% to 14% over the SVM models. In Nubariyah, the ARIMA model achieved an MSE of 1.7499 compared to 1.7746 for the SVM model. In Wadi Al Natrun, the ARIMA model's MSE was 2.0735, significantly lower than the SVM model's 2.4113. In contrast, in Al Boseli, the ARIMA model recorded an MSE of 1.8033 versus 2.0844 for the SVM model. The decomposition of SPEI values into trend, seasonal, and residual components revealed a long-term trend towards increasing dryness over the 30 years, alongside regular annual fluctuations. These insights are essential for understanding climatic behavior and informing water management strategies. The ARIMA model's superior performance underscores its effectiveness in anticipating drought conditions and optimizing water usage. Research should explore advanced models like Recurrent Neural Networks (RNN) to enhance forecasting accuracy further and expand the analysis to additional regions with more recent data to validate these findings, thereby improving drought prediction and water resource management.

DOI

10.21608/mjae.2024.301439.1139

Keywords

Drought Prediction, Time Series Forecasting, Agricultural Water Management

Authors

First Name

Amr

Last Name

Mossad

MiddleName

-

Affiliation

Assoc. Prof., Ag. Eng. Dept., Fac. of Ag., Ain Shams U., Cairo, Egypt.

Email

amr_abdelbari@agr.asu.edu.eg

City

cairo

Orcid

-

First Name

Salwa

Last Name

Abdou

MiddleName

Hassan

Affiliation

Assist. Prof., Ag. Eng. Dept., Fac. of Ag., Ain Shams U., Cairo, Egypt

Email

salwahassan107@agr.asu.edu.eg

City

-

Orcid

-

Volume

41

Article Issue

4

Related Issue

50608

Issue Date

2024-10-01

Receive Date

2024-07-05

Publish Date

2024-10-01

Page Start

263

Page End

278

Print ISSN

1687-384X

Online ISSN

2636-3062

Article File

MJAE_Volume 41_Issue 4_Pages 263-278.pdf

PDF . 665.3kB

Link

https://mjae.journals.ekb.eg/article_370402.html

Detail API

https://mjae.journals.ekb.eg/service?article_code=370402

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Type

Original Article

Type Code

1,326

Publication Type

Journal

Publication Title

Misr Journal of Agricultural Engineering

Publication Link

https://mjae.journals.ekb.eg/

MainTitle

PREDICTING STANDARDIZED PRECIPITATION EVAPOTRANSPIRATION INDEX (SPEI) USING SVM AND ARIMA MODELS: A COMPARATIVE STUDY

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Type

Article

Created At

26 Dec 2024