Weather-based maize yield forecast in Saudi Arabia using statistical analysis and machine learning

• Autorzy: Jayagopal Prabhu , Muthukumaran Venkatesan , Koti Manjula Sanjay , Kumar Sampath Satheesh , Rajendran Sukumar , Mathivanan Sandeep Kumar

Identyfikatory

DOI

10.1007/s11600-022-00854-z

• Języki publikacji: EN

Abstrakty

EN

Crop yield is completely vulnerable to extreme weather events. Growing research investigation to establish climate change, implications in the sectors are influencing the connection. Forecasting maize output with some lead time can help producers to prepare for requirement and, in many cases, limited human resources, as well as support in strategic business decisions. The major purpose is to illustrate the relationship between various climatic characteristics and maize production, as well as to predict forecasts using ARIMA and machine learning approaches. When compared to ARIMA, the proposed method performs better in forecasting maize yields. Consequently, the neural network provides the majority of the prospective talents for forecasting maize production. Seasonal growth is susceptible of forecasting crop yields with tolerable competencies, and efforts are essential to quantify the proposed methodology that forecasts overall crop yield in diverse neighbourhoods in Saudi Arabia’s regions. The proposed combined ARIMA-LSTM model requires less training, with parameter adjustment having less effect on data prediction without bias. To monitor progress, the model may be trained repeatedly using roll back. The correlations between estimated yield and measured yield at irrigation and rain-fed sites were analysed to further validate the robustness of the optimal ARIMA-LSTM method, and the results demonstrated that the proposed model can serve as an effective approach for different types of sampling sites and has better adaptability to inter-annual fluctuations in climate with findings indicating a dependable and viable method for enhancing yield estimates.

Słowa kluczowe

EN

Saudi Arabia; agriculture; prediction; maize; time series

PL

Arabia Saudyjska; rolnictwo; prognoza; kukurydza; szereg czasowy

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2022
• Tom: Vol. 70, no 6
• Strony: 2901--2916
• Opis fizyczny: Bibliogr. 34 poz.

Twórcy

autor

Jayagopal Prabhu

• School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

autor

Muthukumaran Venkatesan

• Department of Mathematics, School of Applied Sciences, REVA University, Bengaluru, Karnataka 560064, India

autor

Koti Manjula Sanjay

• Department of MCA, Dayananda sagar Academy of Technology and Management, Bangalore 560082, India

autor

Kumar Sampath Satheesh

• Department of Computer Science, School of Applied Sciences, REVA University, Bangalore 560064, India

autor

Rajendran Sukumar

• School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

autor

Mathivanan Sandeep Kumar

• School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).