چکیده مقاله
Accurate short term temperature forecasting is important for agriculture, energy management, water resource planning, and public health This need is even greater in arid and semi arid regions such as central Iran This paper presents a data driven approach for forecasting daily air temperature in the city of Kashan The approach uses an Artificial Neural Network ANN based on the Multilayer Perceptron MLP architecture A meteorological dataset that covers the period from 2006 to 2025 was collected, preprocessed, and normalized This dataset was then used to train and validate the proposed model The network was trained with the Levenberg Marquardt back propagation algorithm Several input configurations and hidden layer sizes were examined in order to obtain the optimal structure The performance of the model was evaluated using three standard statistical metrics These metrics are the Root Mean Square Error RMSE , the Mean Absolute Error MAE , and the coefficient of determination R² The experimental results show that the proposed MLP network can capture the nonlinear behavior of the regional climate The model also provides accurate temperature predictions These findings confirm that neural network models are suitable for meteorological forecasting in arid regions
کلیدواژهها
نویسندگان
Ali Hosseini Laqa
Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
Ali Hosseini Laqa
Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
شیوه ارجاع
Hosseini Laqa, Ali,1405,Multilayer Perceptron Neural Network Based Daily Temperature Forecasting for Kashan Using Meteorological Data from 2006 to 2025,30th National Conference on Electrical, Computer and Mechanical Engineering,Shirvan
ارائهشده در
مجموعه مقالات سی امین کنفرانس ملی مهندسی برق، کامپیوتر و مکانیک30 اردیبهشت 1405 · شیروان