Salt Fields Productivity Forecasting Based On Sunlight Duration, Wind Speed and Temperature Data

Abstract

Once a major salt producer, Indonesia has imported million tons of salt in recent years to meet domestic demands of chemical industries. Indonesia’s salt-producing potential has been hindered by lack of competitiveness and unsynchronized production data. The salt supply chain process is typically finished on a monthly basis, yet the uncertainty of weather conditions often leads to erratic production yields. Since heavy reliance on the weather can bring negative consequences for salt farmers around the country, accurate salt field productivity forecasting is of great importance. This study aims at examining sunlight duration, wind speed and temperature data to predict salt field productivity in Kalianget Sumenep Madura. The predictive model is developed using Artificial Neural Network (ANN) method because it has a low risk of fault to solve nonlinear relationships. The effects of different learning rate and momentum values are analyzed by full factorial design of experiment and evaluated based on the lowest root mean square error (RMSE). Then, the optimal model is used to test and compare the forecasting performance based on ANN and Holt-Winters predictors. The result demonstrates that the proposed model is accurate and efficient to represent a good solution to predict salt field productivity in the region