Deep Learning Based Effective Weather Prediction Model for Tunceli City

Abstract

Making accurate and effective weather forecasts has been one of the important issues in human history. Its importance is further increased by its direct impact on economic areas such as agriculture and tourism, and its effects on saving people’s lives by predicting high-level air disasters. Especially today, when global warming has increased its impact on weather events, sudden weather changes and subsequent disasters have made it very important to develop accurate weather forecast methods and models. In this study, a deep learning based prediction model has been developed for weather forecast. The developed LSTM (Long Short-Term Memory) based deep learning model has been compared implementally with GRU (Gated Recurrent Unit), one of the recurrent neural network models, and traditional machine learning algorithms such as RF (Random Forest) and SVR (Support Vector Regression). For each model and algorithm, the experimental results obtained according to MSE (Mean Squared Error), RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), Pearsons correlation coefficient and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> metrics have been compared. Experimental results show that the developed LSTM-based deep learning model gives more successful results than other compared models and algorithms.