A Deep Learning-Based Vector Autoregressive-Gated Recurrent Unit Hybrid Model for Long-Term Forecasting of Weather Parameters for Smart Farms

Abstract

Agriculture is inextricably linked to the environment. Climate change has an effect directly on agricultural activities. India gets severely impacted if there is a loss of yields, which affects human lives. Hence, monitoring climate and its impact on the agricultural field is essential for a country like India. This chapter proposes a novel deep learning-based hybrid vector autoregressive–gated recurrent unit model (VAR-GRU model) for weather forecasting involving the four important weather parameters such as temperature, pressure, humidity, and wind speed for the cities of Bengaluru and temperature, pressure, dew point, and wind speed for the cities of Dongsi. The effectiveness of the proposed VAR-GRU model is proven by comparing its performance metrics (MAE, MSE, RMSE, and R2 Score) with that of other baseline models such as LSTM, VAR, GRU, and another hybrid VAR-LSTM model. The outcomes of this research work can help in increasing crop yields by utilizing the weather forecasting results in smart farming applications.