DWFH: An improved data-driven deep weather forecasting hybrid model using Transductive Long Short Term Memory (T-LSTM).

• The Transductive Long Short Term Memory model is proposed. • The proposed result indicates that the T -LSTM is better than previous studies. • The T -LSTM method produces 98.2% accuracy in forecasting the weather. • This proposed hybrid T -LSTM method provides a robust solution. Forecasting climate and the development of the environment have been essential in recent days since there has been a drastic change in nature. Weather forecasting plays a significant role in decision-making in traffic management, tourism planning, crop cultivation in agriculture, and warning the people nearby the seaside about the climate situation. It is used to reduce accidents and congestion, mainly based on climate conditions such as rainfall, air condition, and other environmental factors. Accurate weather prediction models are required by meteorological scientists. The previous studies have shown complexity in terms of model building, and computation, and based on theory-driven and rely on time and space. This drawback can be easily solved using the machine learning technique with the time series data. This paper proposes the state-of-art deep learning model Long Short-Term Memory (LSTM) and the Transductive Long Short-Term Memory (T -LSTM) model. The model is evaluated using the evaluation metrics root mean squared error, loss, and mean absolute error. The experiments are carried out on HHWD and Jena Climate datasets. The dataset comprises 14 weather forecasting features including humidity, temperature, etc. The T -LSTM method performs better than other methodologies, producing 98.2% accuracy in forecasting the weather. This proposed hybrid T -LSTM method provides a robust solution for the hydrological variables. [ABSTRACT FROM AUTHOR]