Precipitation forecast with logistics regression methods for harvest optimization
Abstract
This paper proposes a model that forecasts the weather and then, based on that forecast, uses an income-oriented linear programming method to optimize the harvesting process. Data representing a total yearly output capacity of 472,878 tons from 214 different field locations were used to test the model for sugar beet production. Prior to optimization, long-term one-year weather rainfall forecasting was done using 10 years of actual weather data for the field locations. Weather precipitation was forecasted using logistic regression with an accuracy of 84.16%. The outcome of the weather precipitation prediction model was a parameter in the optimization model. The weather forecast for precipitation led to the 120-day harvest planning being optimized. Comparative analysis was done on the outcomes of the developed model and the current scenario. Comparing the current situation to the proposed one, revenue would have increased by 16.7%. Given that it incorporates weather forecasts into the harvest optimization process, the methodology presented in this paper is more practical than other harvest optimization models.
Keywords
Precipitation forecast, Machine learning, Logistic regression, Harvest efficiency, Optimization
References
- Abhishek, K., Singh, M.P., Ghosh, S., Anand, A. (2012). Weather forecasting model using Artificial Neural Network, Pocedia Technology, 311-318
- Aktaş, C., Erkuş, O. (2009). Investigation of fog forecastıng of eskişehir using logistic regression analysis, İstanbul Commerce University Journal of Science, 47-59
- Anwar, M.T., Nugrohadi, S., Tantriyati, V., Windarni, V.A. (2020). Rain prediction using rule-based machine learning approach, advance sustainable science, Engineering and Technology, 200104
- Budak, B., Erpolat S. (2012). Comparison of artificial neural networks and logistic regression analysis in the credit risk prediction, Online Academic Journal of Information Technology, 3(9), 23
- Chappell, D. (2015). Introducing azure machine learning, DavidChappell & Associates,
- Choi, C., Kim, J., Kim, J., Kim, D., Bae, Y., Kim, H.S. (2018). Development of heavy rain damage prediction model using machine learning based on big data, Hindawi Advances in Meteorology, 11
- Çokluk, Ö. (2010). Lojistik regresyon analizi: kavram ve uygulama, In: Educational Sciences: heory & Practice, 1357-1407, (in Turkish)
- DeBuse, C., Lopez, G., DeJong, T. (2010). Using spring weather data to predict harvest date for ‘Improved French’ prune, In: Proc. 9th IS on Plum & Prune Genetics, 107-112
- Dhekale, B.S., Sawant, P.k., Upadhye, T.P. (2014). Weather based pre-harvest forecasting of rice at Kolhapur (Maharashtra), Trends in Biosciences, 925-927
- Fente, D.N., Singh, D.K. (2018). Weather forecasting using artificial neural network, In: Proceedings of the 2nd International Conference on Inventive Communication and Computational Technologies, 1757-1761
