Long-Term Forecasting of Crop Water Requirement with BP-RVM Algorithm for Food Security and Harvest Risk Reduction

Abstract

Cropping pattern planning is important to avoid crop failure. Meanwhile, cropping patterns are affected by climate change, which is constantly shifting and erratic. Mistakes in determining the planting schedule will affect the risk of crop failure. Hence, climate forecast using long-term hydro-climatological data must be conducted as cropping patterns are mapped for a multi-year period. Data was collected from the Meteorology, Climatology, and Geophysics Agency in Lombok Island. This paper discusses the combination of backpropagation and relevance vector machine with RBF kernel. We utilized BP-RVM architecture with three hidden layers to improve the performance of the network. This combination is utilized because of the BP algorithm's ability to simplify data pattern recognition and RVM to speed up and reduce the number of iterations for each data training-testing process. The evapotranspiration of each crop was then calculated using the FAO24 Blaney-Criddle method. Based on the forecasting, the average MAPE was below 20%, which indicates "good forecasting". The evapotranspiration values of CGPRT and horticultural crops were almost the same with an average of 2.79 mm/day and 2.78 mm/day. These values are lower than the evapotranspiration values of tobacco and rice. Finally, based on the calculation of each crop's water requirement throughout the year, it was recommended to start the first planting season at the end of October. The results of this study can be recommended to the government to apply the BP-RVM algorithm in forecasting hydro-climatological data and optimizing cropping patterns to avoid crop failure and maintain the stability of national food security.