Modeling of Climatic Variables Using Stochastic Approaches in Sudan

Mawadda Abdallah; Bilal Cemek

Makale detayları

Modeling of Climatic Variables Using Stochastic Approaches in Sudan

Yazarlar:

Mawadda Abdallah | Bilal Cemek

Yayıncı:

Ondokuz Mayıs University

Dergi:

Anadolu Tarım Bilimleri Dergisi Vol.38 Issue 1, pp.53-68

Belge Tipi:

Makale

Yayın Tarihi:

2/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001283737

pISSN:

1308-8750

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 İklimsel değişkenleri, tarımsal süreç ve sulama yönetiminde önemli bir rol oynamaktadır çünkü iklimle ilgili tarımsal verimden kesinlikle tüm etkilenecek değişiklikleri bilmemiz gerekmektedir. Bu amaçla, bu çalışmada Sudandaki beş ana meteoroloji istasyonu Wad Madani, Hartum, Al Gadaref, Al Damazin ve Dongola ile ilgili günlük ortalama sıcaklık, bağıl nem ve güneş radyasyonu değişkenlerinin modellenmesi için ARIMA modelleri önerilmiştir. 2013-2020 yılları arasındaki günlük iklim verileri kullanılmıştır. Box Jenkins modelleri olarak adlandırılan ARIMA Otoregresif Entegre Hareketli Ortalama Modelleri kullanılarak iklimsel değişkenlerin tahmin ve modellemesini için zaman serisi analiz yöntemlerini kullanılmıştır. Modelleme amacıyla, günlük değişkenlerin gelecekteki değerlerini tahmin etmek için doğrusal stokastik modeller kullanılmıştır. Durağanlığı 1, 5 ve 10 güven düzeylerinde kontrol etmek için zaman serilerine artırılmış Dickey-Fuller testi ADF uygulanmıştır. Değişkenlerin zaman serileri durağanlı ve eğilimsiz olduğunu tespit edilmiştir. Modelleri kontrol etmek ve otokorelasyon ACF ve kısmi otokorelasyon PACF fonksiyon grafiklerinden en iyi modelleri seçmek için diyagnostik kontrolleri kullanılmıştır. En iyi modelleri, düzeltilmiş R 2 , Standart hata S.E, Akaike bilgi kriteri AIC ve Bayesian bilgi kriteri BIC değerlerine göre seçilmiştir. En iyi sonuçları, gelecekteki değerleri tahmin etmek için etkili olabilecek ARIMA 1,0,1 ve 1,0,2 modellerinde gözlenmiştir. ARIMA modelleri sıcaklık, bağıl nem ve güneş radyasyonu değişkenleri için tatmin edici sonuçları elde etmiştir. Bu nedenle, bu çalışma ziraat mühendislerinin tarımsal uygulamalarla ilgili tüm süreçleri gerçekleştirmeleri için çok yardımcı olabilmektedir.

Makale detayları

Modeling of Climatic Variables Using Stochastic Approaches in Sudan

Yazarlar:

Mawadda Abdallah | Bilal Cemek

Yayıncı:

Ondokuz Mayıs University

Dergi:

Anadolu Tarım Bilimleri Dergisi Vol.38 Issue 1, pp.53-68

Belge Tipi:

Makale

Yayın Tarihi:

2/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001283737

pISSN:

1308-8750

variables

ARIMA models

ADF test

stochastic models

Değişkenler

ARIMA modelleri

ADF testi

stokastik modelleri

Sayı

Makale detayları

Yazarlar: Mawadda Abdallah | Bilal Cemek

Yayıncı: Ondokuz Mayıs University

Dergi: Anadolu Tarım Bilimleri Dergisi Vol.38 Issue 1, pp.53-68

Belge Tipi: Makale

Yayın Tarihi: 2/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001283737

pISSN: 1308-8750

Sayı

Yazarlar:

Mawadda Abdallah | Bilal Cemek

Yayıncı:

Ondokuz Mayıs University

Dergi:

Anadolu Tarım Bilimleri Dergisi Vol.38 Issue 1, pp.53-68

Belge Tipi:

Makale

Yayın Tarihi:

2/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001283737

pISSN:

1308-8750