Deep Reinforcement Learning Based Weather Monitoring Systemusing Arduino for Smart Environment
R.Sathya Vignesh1, A.Sivakumar2, M.Shyam3, J. Yogapriya4
1Mr. R. Sathya Vignesh, Assistant Professor, Department of Electronics and Communication Engineering, R.M.K. Engineering College, Kavaraipettai, Tamil Nadu, India.
2Mr. A. Sivakumar, Assistant Professor, Department of Electronics and Communication Engineering, R.M.K. Engineering College, Kavaraipettai, Tamil Nadu, India.
3Mr. M. Shyam, Assistant Professor, Department of Electronics and Communication Engineering, R.M.K. Engineering College, Kavaraipettai, Tamil Nadu, India.
4Mrs. J. Yogapriya, Programmer Analyst, Department of Electronics and Communication Engineering, Dr. N.G.P. Institute of Technology, Coimbatore, Chennai, India.
Manuscript received on November 11, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on 30 November, 2019. | PP: 4346-4350 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8261118419/2019©BEIESP | DOI: 10.35940/ijrte.D8261.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Weather forecasting is an essential predictive challenge that has depended primarily on model-based methods. Collection of data about the different weather parameters is needed a smart environment. Recent developments in machine learning (ML) made possible to collect the data. The data from input sensors is then read by Arduino, which acts as server. The sensors collect the data of various environmental parameters and provide it to Arduino, which act as a base station. It then transmits the data using WIFI and the processed data will be displayed on laptop through accessing the server that is on the receiver side. In this paper, new directions are explored with forecasting weather as a data intensive challenge that involves inferences across space and time. Machine Learningmakes predictions through a unique hybrid approach that combines discriminatively trained predictive models and a deep neural network. The Deep Learning algorithm utilized here is Value-Based – Temporal Difference Algorithm. This in turnmodels the joint statistics of a set of weather-related variables. It is shown that the base model can be enhanced with spatial interpolation that uses learned long-range spatial dependencies. An efficient learning and inference procedure is also devised, that allows for large scale optimization of the model parameters. The methods are evaluated with experiments on real-world meteorological data that highlight the promise of the approach.
Keywords: Machine Learning, Arduino, WIFI, DRL (Deep Reinforcement Learning) Algorithms, Temporal difference (TD) Algorithm.
Scope of the Article: Machine Learning.