Application of neural networks modeling to environmentally global climate change at San Joaquin Old River Station

摘要

The present study focuses on temperature variations during the past 21 years (1990-2010) using data obtained from San Joaquin River (Old River Station), to calculate the rate of temperature variation. The rate of temperature change (R) is calculated by adding up the difference between each year's mean temperature and that of the previous years. According to our calculation R equals to 0.0354 °C/year, which means that if the local conditions would exist, we will have 3.54 °C temperature rise withinthe next 100 years. Using the resource we calculated mean temperature for the past 21 years, which was equal to 17.12 °C, meaning that the mean temperature of the year 2100 will be around 20.5 °C, which will be incredibly high. We also made an ANN model (and ran it using MATLAB) to regenerate the missing data. The model is a feed-forward network with back propagation neurons trained by the Levenberg-Marquardt algorithm, with 4 layers containing 25 neurons. After making the model and before using it, we tested the model with existing data and compared the results that showed unexpected high correlation of 99 %.