This paper compares data mining approaches for weather forecasting from one-dimensional and multidimensional meteorological weather data. Linear and nonlinear methods are applied and more successful results are obtained from nonlinear methods. The best result is obtained with LSTM(Long short-term memory). RFE(Recursive Feature Elimination) is used for subset feature selection and it increases one-dimensional MLP(Multi Layer Perceptron) model accuracy. In addition, Grid Search is used for hyperparameter tuning and early stopping is used to avoid overfitting and underfitting.