Modeling of neural networks for weather forecasting in debre markos, Ethiopia

The use of artificial neural networks (ANNs) to make daily weather predictions based on historical data is proposed in this study. The proposed models are designed using four learning algorithms: feedforward backpropagation, cascade forward backpropagation, feedforward distributed time delay, and layer recurrence. Moreover, the four learning algorithms use TRAINLM, TRAINRP, TRAINSCG, TRAINGDX, and TRAINOSS training functions independently. Weather data of 5500 days were used for training, validating, and testing using the designed networks. By measuring the atmospheric minimum and maximum temperatures, relative humidity, precipitation, sunshine hours, and wind speed on each day, the weather data of the next day, that is, the maximum temperature of the atmosphere and sunshine hours, were forecast. The network architecture is composed of 1 hidden layer and 12 neurons. The feedforward backpropagation neural network achieved a better result than the other neural networks. The numerical predictions of the feedforward backpropagation neural network were nearly equal to the actual values, and its forecast was responsible for a mean square error of less than 2.11 for the overall training. During the training, validation and testing phase the recorded regression were 0.99109, 0.98298 and 0.99184, respectively. The aggregated regression value considering training, validation, and testing was 0.98955.