Forecasting the weather of Nevada: A deep learning approach

This paper compares two approaches for predicting air temperature from historical pressure, humidity, and temperature data gathered from meteorological sensors in Northwestern Nevada. We describe our data and our representation and compare a standard neural network against a deep learning network. Our empirical results indicate that a deep neural network with Stacked Denoising Auto-Encoders (SDAE) outperforms a standard multilayer feed forward network on this noisy time series prediction task. In addition, predicting air temperature from historical air temperature data alone can be improved by employing related weather variables like barometric pressure, humidity and wind speed data in the training process.

[1]  William W. Hsieh,et al.  Applying Neural Network Models to Prediction and Data Analysis in Meteorology and Oceanography. , 1998 .

[2]  Ajith Abraham,et al.  Intelligent weather monitoring systems using connectionist models , 2002, Neural Parallel Sci. Comput..

[3]  Pak Wai Chan,et al.  Deep neural network based feature representation for weather forecasting , 2014 .

[4]  Xiaodong Li,et al.  Time series forecasting by evolving artificial neural networks with genetic algorithms, differential evolution and estimation of distribution algorithm , 2011, Neural Computing and Applications.

[5]  James N. K. Liu,et al.  An Artificial Neural Network with Chaotic Oscillator for Wind Shear Alerting , 2012 .

[6]  A. Barros,et al.  Localized Precipitation Forecasts from a Numerical Weather Prediction Model Using Artificial Neural Networks , 1998 .

[7]  Ajith Abraham,et al.  An ensemble of neural networks for weather forecasting , 2004, Neural Computing & Applications.

[8]  Brian A. Smith,et al.  Air temperature prediction using artificial neural networks , 2006 .

[9]  Ravi Sankar,et al.  Time Series Prediction Using Support Vector Machines: A Survey , 2009, IEEE Computational Intelligence Magazine.

[10]  Aoife Foley,et al.  Current methods and advances in forecasting of wind power generation , 2012 .

[11]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[12]  Munindar P. Singh,et al.  Weather Forecasting Model using Artificial Neural Network , 2012 .

[13]  Wei-Chiang Hong,et al.  Rainfall forecasting by technological machine learning models , 2008, Appl. Math. Comput..

[14]  Bita Shadgar,et al.  Artificial Neural Network Ensemble Approach for Creating a Negotiation Model with Ethical Artificial Agents , 2012, ICAISC.

[15]  Shyi-Ming Chen,et al.  Temperature prediction using fuzzy time series , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[16]  Y. Radhika,et al.  Atmospheric Temperature Prediction using Support Vector Machines , 2009 .

[17]  Y.-Y. Hsu,et al.  Short term load forecasting using a multilayer neural network with an adaptive learning algorithm , 1992 .

[18]  Yi-Qing Ni,et al.  Correlating modal properties with temperature using long-term monitoring data and support vector machine technique , 2005 .