Prediction of Temperature, Precipitation, and Pressure Trends for Beijing's 12 Months in 2009 Using MATLAB's Built-in Toolbox Functions

Using MATLAB's built-in toolbox functions, I implemented trend prediction for temperature, precipitation, and atmospheric pressure across 12 months in Beijing during 2009. The implementation employs a Backpropagation Neural Network algorithm that demonstrated excellent performance metrics. The project utilizes several key files and programs: BP neural network main script (Bp.m) containing the core algorithm implementation with layered activation functions, MATLAB data handler (bp_ds.xls) for preprocessing time-series data, training set input (bp_nds.xls) featuring normalized meteorological parameters, training set target output (bp_td.xls), test set input (bp_ntd.xls) for validation, test set output documentation (BP_weather_prediction.doc), and comprehensive thesis paper detailing the methodological approach. Through these files and programs, the BP neural network algorithm successfully predicts temperature, precipitation, and pressure variations for Beijing's 2009 monthly data. The prediction model exhibits outstanding accuracy in forecasting results, utilizing MATLAB's neural network toolbox functions that provide convenient built-in methods for data normalization, network training with Levenberg-Marquardt optimization, and performance evaluation through mean squared error calculations. Research results confirm the feasibility of using BP neural networks for meteorological parameter prediction. The algorithm employs a multi-layer perceptron structure with sigmoid activation functions and gradient descent weight adjustment, enabling better understanding and forecasting of climate patterns. The accompanying thesis elaborates on the algorithm's theoretical foundation, implementation specifics including hidden layer configuration and epoch settings, along with experimental results and comparative analysis. In summary, utilizing MATLAB's BP neural network algorithm for predicting temperature, precipitation, and atmospheric pressure represents a promising research direction in meteorological forecasting. This study contributes to enhanced understanding of weather pattern dynamics and supports advancements in climatological research through machine learning applications, featuring robust data preprocessing techniques and cross-validation methodologies.