Precipitation forecast with logistics regression methods for harvest optimization

Year 2023, , 213 - 222, 27.03.2023

Mesut Samastı , Tarık Küçükdeniz

https://doi.org/10.31015/jaefs.2023.1.26

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
• Gianey, H.K., Choudhary, R. (2017). Comprehensive review on supervised machine learning algorithms, In: , 37-43
• Helm, J.M., Swiergosz, A.M., Haeberle, H.S., Karnuta, J.M., Schaffer, J.L., Krebs, V.E., Spitzer A.I., Ramkumar, P.N. (2020). Machine learning and artificial ıntelligence: definitions, applications, and future directions, Current Reviews in Musculoskeletal Medicine, 69-76
• Hill, R.W., Keller, J. (1980). Irrigation system selection for maximum crop profit, American Society of Agricultural Engineers, 366-372
• Holmstrom, M., Liu, D., Vo, C. (2016). Machine learning applied to weather forecasting, Stanford University, 1-5
• Hung, N.Q., Babel, M.S., Weesakul, S., Tripathi, N. K. (2009). An artificial neural network model for rainfall forecasting in Bangkok, Thailand, Hydrol. Earth Syst. Sci, 1413-1425
• Karevan, Z., Suykens, J.A.K. (2018). Spatio-temporal stacked LSTM for temperature prediction in weather forecasting, 1-5
• Kaygın, C.Y., Tazegül, A., Yazarkan H. (2016). Estimation capability of financial failures and successes of enterprises using data mining and logistic regression analysis, Ege Academic Review, 147-159
• Khalilzadeh, Z., Wang, L. (2021). An MILP model for corn planting and harvest scheduling considering storage capacity and growing degree units, Biorxiv, 2021-02
• LaValley, M.P. (2008). Logistic Regression, Circulation, 2395–2399
• Lee, J., Hong, S., Lee, J.H. (2014). An efficient prediction for heavy rain from big weather data using genetic algorithm, In: IMCOM (ICUIMC)’14, 1-7
• Lin, X., Negenborn, R.R., Lodewijks, G. (2018). Predictive quality-aware control for scheduling of potato starch production, Computers and Electronics in Agriculture, 266-278
• Luk, K.C., Ball, J.E., Sharma, A. (2001). An application of artificial neural networks for rainfall forecasting, Mathematical and Computer Modelling, 683-693
• Mat, İ., Kassim, M.R.M., Harun, A.N., (2016). IoT in precision agriculture applications using wireless moisture sensor network, In: IEEE Conference on Open Systems, 24-29
• Maulud, D.H., Abdulazeez, A.M. (2020). A review on linear regression comprehensive in machine learning, Journal of Applied Science and Technology Trends, 140-147
• Medvediev, I., Lebid, I., Bragin, M. (2017). Assessment of the weather and climate conditions impact on the organization and planning of transport support for wheat harvesting, ТЕKA. Commission of the motorization and energetics in agriculture, 45-54
• Mutlubaş, C., Soybalı, H.H. (2017). The ivestigation of effect of customer satisfaction factors on customer loyalty with logistics regression analysis, Journal of Turkissh Tourism Research, 1-15
• Priya, S.R.K., Suresh, K.K. (2009). A study on pre-harvest forecast of sugarcane yield using climatic variables, Statistics and Applications, 1-8
• Ranka, N.M., Sharma, L. (2012). Design of experiments: a powerful tool for agriculture analysis, Statistics, 11356-11358
• Safar, N.Z.M., Ramli, A.A., Mahdin, H., Ndzi, D., Khalif, M.N.K. (2019). Rain prediction using fuzzy rule based system in North-West Malaysia, Indonesian Journal of Electrical Engineering and Computer Science, 1572-1581
• Salman, M.G., Kanigoro, B., Heryadi, Y. (2015). Weather forecasting using deep learning techniques, In: ICACSIS, 281-285
• Sulaiman, J., Wahab, S.H. (2017). Heavy rainfall forecasting model using artificial neural network for flood prone area, IT Convergence and Security, 69-76
• Şaşmaz, M.Ü., Özel, Ö. (2019). Effect of agricultural ıncentives on the development of agricultural sector: example of Turkey, Dumlupinar University Journal of Social Sciences, 61, 50-65
• TÜSSİDE (2019). National suger beet logistics optimization in Turkey, In: Turkish Management Sciences Institutes’ Research,
• Wang, G., Pu, P., Shen, T. (2020). An efficient gene bigdata analysis using machine learning algorithms, Multimedia Tools and Applications, 9847-9870