Modeling of Climatic Variables Using Stochastic Approaches in Sudan

Authors : Mawadda Abdallah, Bilal Cemek

Pages : 53-68

Doi:10.7161/omuanajas.1145094

View : 13 | Download : 8

Publication Date : 2023-02-28

Article Type : Research

Abstract :The climatic variables play a significant role in agricultural process and irrigation management because we need to know all changes related to the climate, which will absolutely affect agricultural yield.. For this purpose, the ARIMA models were suggested in this study for modeling daily average temperature, solar radiation, and relative humidity factors related to five main meteorological stations (Wad Madani, Khartoum, Al Gadaref, Al Damazin, and Dongola) in Sudan. The daily variables were obtained from the period 2013 to 2020. Time series analysis methods are used for estimating and modeling the climatic variables using Autoregressive Integrated Moving Average methods, which are called Box Jenkins models. For modeling purposes, linear stochastic models were used to estimate the future values of daily variables. The Augmented Dickey-Fuller test (ADF) was used to check the stationarity of the data at 1%, 5%, and 10% confidence levels. The time series of variables showed stationarity and no trend. The best models were selected from the autocorrelation (ACF) and partial autocorrelation (PACF) function graphs employing diagnostic testing. The adjusted R 2 , Standard error (S.E), Akaike information criterion (AIC), and Bayesian information criterion (BIC) values were used to assess which models were the best. The appropriate findings were observed in ARIMA (1,0,1) and (1,0,2) which can be effective for predicting future values. The ARIMA models obtained satisfactory results for temperature, relative humidity, and solar radiation variables. So, this study might be extremely helpful for agricultural engineers to achieve all the processes related to agricultural practices.
Keywords : Değişkenler, ARIMA modelleri, ADF testi, stokastik modelleri