AniCast: A Machine Learning-Based Nowcasting System for Rice Production in Response to Climate Parameters

Abstract

Rice is an important crop in the Philippines, and its production is one of the most vulnerable to climate change. Various works studied the relationship between those climate parameters and rice yield, highlighting their relevance to each other. This led to the development of different models and architectures to analyze the multivariate time series data. However, those are purely statistical. Moreover, the Philippines have limited accessible tools for nowcasting rice production. This study aims to develop a machine learning-based nowcasting tool (web application) for rice production with climate parameters as the factors. The analysis used Bukidnon’s quarterly climate and rice yield dataset from 2000 to 2019. The ARDL model identifies the significant lags and variables for the hybrid model. The study compared seven models: GPR, BVAR, XGBoost, RF, RNN, LSTM, and CNN; additional models include the ARDL and the ARDL-BVAR. These are evaluated using APE, MSE, RMSE, and MAPE. In the initial evaluation among the machine learning models, BVAR has the lowest MAPE and was used in the hybrid model, ARDL-BVAR. Using the significant variables as factors, the model improved by gathering much lower error metrics. The best-performing models were then adapted into the website application called AniCast, which displays visualizations, nowcasts, and evaluation metrics in table form and can be downloaded into a PDF file. This study addresses the gap by utilizing machine learning models and provides a valuable, accessible tool for informed decision-making regarding rice production management in the Philippines.