Weather-Based Rice Crop Yield Forecasting using Different Regression Techniques & Neural Network Approach for Prayagraj Region
Nilesh Kumar Singh *
Department of Environmental Science and Natural Resource Management, Sam Higginbottom University of Agriculture Technology and Sciences, Allahabad, Uttar Pradesh, India.
Shraddha Rawat
Department of Environmental Science and Natural Resource Management, Sam Higginbottom University of Agriculture Technology and Sciences, Allahabad, Uttar Pradesh, India.
Shweta Gautam
Department of Environmental Science and Natural Resource Management, Sam Higginbottom University of Agriculture Technology and Sciences, Allahabad, Uttar Pradesh, India.
*Author to whom correspondence should be addressed.
Abstract
Rice crop yield data and weather data were considered in this study, covering the past twenty-nine years (1991-2019) in Prayagraj District, Uttar Pradesh. The data was sourced from DACNET and the College of Forestry, SHUATS Prayagraj. The analysis comprised a calibration period of 26 years (90% of the dataset) and a validation period using the remaining data (10%). In this study, 75.9% of the data were utilized for training the Artificial Neural Network (ANN) model, while the remaining 24.1% were allocated for testing and validation, ensuring comprehensive model assessment. The primary evaluation metric employed for model efficiency was the Normalized Root Mean Squared Error (nRMSE), with a focus on achieving the lowest values. Both a Stepwise Linear Regression technique and a Neural Network were employed for rice yield prediction. Notably, the regression-based model exhibited superior performance compared to the ANN model, as indicated by the nRMSE values. This conclusion was drawn from the observation that the regression-based model yielded the best-fitting results. The study's findings highlight the significance of Bright Sunshine Hours in relation to nRMSE and the coefficient of determination, which were recorded at 0.00025 and 0.94, respectively. This underlines the importance of this meteorological factor in accurately predicting rice crop yield.
Keywords: Regression, yield, model, parameter, artificial neural networks, coefficient of determination
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