Long-term forecasting for climate classification using hybrid backpropagation relevance vector machine algorithm

Climate forecasting and classification are essential in planning cropping patterns. Therefore, the purpose of this study is to evaluate the condition of hydro-climatological data for the last 10 years and determine the trend of the data and determine the climate classification. The forecasting process utilizes a long-term forecasting method based on a hybrid backpropagation-relevance vector machine (BP-RVM) algorithm. The architecture parameters consist of three hidden layers (36-73-38-19-1), learning rate 0.1, momentum 0.9, and RBF gamma of 0.01. The forecasting results show that the BP-RVM algorithm has an average accuracy level with a category of "high accurate forecasting". The climate classification in the Central Lombok region, Indonesia is D4 climate (slightly dry category). The increase in rainfall volume and temperature from year to year has a strong correlation with a decrease in air humidity, wind speed, and length of sunshine. The results of this study can be utilized as a basis for planning cropping patterns and handling the water needs of crops grown in the dry season. Therefore, farmers can determine the amount of water needed from dams or watersheds due to evaporation that occurs during the growing season.