Development of a Small Intelligent Weather Station for Agricultural Applications
It is known that climate change causes a decrease in the profit gained from agricultural production. This work designs and establishes weather boxes equipped with functions of rainfall prediction, frosting forecast, and lightning detection. With the wireless connection and the build-in decision mode, weather boxes can deliver early-warning by sending texting messages to the users and actuating the corresponding action to response the extreme climate. To implement rainfall and frosting prognostication, two different datasets are analyzed by the technology of data mining. One of the datasets is acquired from the Central Weather Bureau, and the other is from the proposed weather box monitoring the agricultural environment. From the experimental results, the prediction model constructed from the data which is collected by the proposed weather box exhibits a higher accuracy in rainfall forecasting than those based on the Central Weather Bureau.
M. A. Cooper and R. L. Holle, Current Global Estimates of Lightning Fatalities and Injuries, Reducing Lightning Injuries Worldwide, Springer, 2019.
R. L. Holle, “Annual Rates of Lightning Fatalities by Country,” 20th International Lightning Detection Conference, April 2008.
R. S. Cerveny, P. Bessemoulin, C. C. Burt, M. A. Cooper, Z. Cunjie, A. Dewan, et al. “WMO Assessment of Weather and Climate Mortality Extremes: Lightning, Tropical Cyclones, Tornadoes, and Hail,” Weather, Climate, and Society, vol. 9, pp. 487-497, 2017.
S. Pattar, R. Buyya, K. R. Venugopal, S. Iyengar, and L. Patnaik, “Searching for the IoT Resources: Fundamentals, Requirements, Comprehensive Review, and Future Directions,” IEEE Communications Surveys & Tutorials, vol. 20, pp. 2101-2132, April 2018.
P. Sethi and S. R. Sarangi, “Internet of Things: Architectures, Protocols, and Applications,” Journal of Electrical and Computer Engineering, vol. 2017, January 2017.
S. Aftab, M. Ahmad, N. Hameed, M. S. Bashir, I. Ali, and Z. Nawaz, “Rainfall Prediction Using Data Mining Techniques: A Systematic Literature Review,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 5, pp. 143-150, 2018.
D. J. Hand and N. M. Adams, “Data Mining,” Wiley StatsRef: Statistics Reference Online, pp. 1-7, 2014.
J. Han, J. Pei, and M. Kamber, Data Mining: concepts and Techniques, Elsevier, 2011.
A. Iqbal and S. Aftab, “A Feed-Forward and Pattern Recognition ANN Model for Network Intrusion Detection,” International Journal of Computer Network & Information Security, vol. 11, no. 4, pp. 19-25, April 2019.
Z. Chao, F. Pu, Y. Yin, B. Han, and X. Chen, “Research on Real-Time Local Rainfall Prediction Based on MEMS Sensors,” Journal of Sensors, vol. 2018, 2018.
S. Zainudin, D. S. Jasim, and A. A. Bakar, “Comparative Analysis of Data Mining Techniques for Malaysian Rainfall Prediction,” International Journal on Advanced Science, Engineering and Information Technology, vol. 6, pp. 1148-1153, 2016.
C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
R. C. Brito, F. Favarim, G. Calin, and E. Todt, “Development of a Low Cost Weather Station Using Free Hardware and Software,” Latin American Robotics Symposium (LARS) and 2017 Brazilian Symposium on Robotics (SBR), November 2017, pp. 1-6.
A. Geetha and G. Nasira, “Data Mining for Meteorological Applications: Decision Trees for Modeling Rainfall Prediction,” IEEE International Conference on Computational Intelligence and Computing Research, 2014, pp. 1-4.
P. S. Yu, T. C. Yang, S. Y. Chen, C. M. Kuo, and H. W. Tseng, “Comparison of Random Forests and Support Vector Machine for Real-Time Radar-Derived Rainfall Forecasting,” Journal of Hydrology, vol. 552, pp. 92-104, September 2017.
C. Cortes and V. Vapnik, “Support-Vector Networks,” Machine Learning, vol. 20, no. 3, pp. 273-297, September 1995.
L. C. Liang and L. T. Chen, “Improved SVM Classifier Incorporating Adaptive Condensed Instances Based on Hybrid Continuous-Discrete Particle Swarm Optimization,” Advances in Technology Innovation, vol. 1, no. 2, pp. 53-57, September 2016.
T. Cover and P. Hart, “Nearest Neighbor Pattern Classification,” IEEE Transactions on Information Theory, vol. 13, no. 1, pp. 21-27, January 1967.
L. Breiman, “Bagging Predictors,” Machine Learning, vol. 24, no. 2 pp. 123-140, 1996.
S. Rana and R. Garg, “Slow Learner Prediction Using Multi-Variate Naïve Bayes Classification Algorithm,” International Journal of Engineering and Technology Innovation, vol. 7, no. 1, pp. 11, January 2017.
J. Lai, Y. Liu, J. Du, and Q. Li, “Lightning Detection Technology and Application,” International Conference on Meteorology Observations (ICMO), December 2019, pp. 1-5.
Z. Yang and S. Jiang, “Design of Lightning Detection System Based on ARM,” International Conference on Lightning Protection (ICLP), October 2014, pp. 346-350.
AS3935: Franklin Lightning Sensor IC, https://www.mouser.tw/new/ams/ams-AS3935/
S. Boughorbel, F. Jarray, and M. El-Anbari, “Optimal Classifier for Imbalanced Data Using Matthews Correlation Coefficient metric,” PloS One, vol. 12, no. 6, pp. e0177678, June 2017.
Welcome to Collective.Ifttt! https://collectiveifttt.readthedocs.io/en/latest/
Y. Shibai, L. F. Tsai, and Y. Q. Lee, “Analysis of the characteristics of each lightning detection system in Taiwan,” 2018, http://photino.cwb.gov.tw/conf/history/108/A3/A3_10_L_%E7%99%BD%E6%84%8F%E8%A9%A9_%E5%90%84%E9%96%83%E9%9B%BB%E5%81%B5%E6%B8%AC.pdf (Chinese)
Copyright (c) 2021 Yi-Hua Chung, Jun-Fu Huang, Yuan-Chen Hu, Chen-Kang Huang
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Submission of a manuscript implies: that the work described has not been published before that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication. Authors can retain copyright in their articles with no restrictions. is accepted for publication. Authors can retain copyright of their article with no restrictions.
Since Jan. 01, 2019, AITI will publish new articles with Creative Commons Attribution Non-Commercial License, under The Creative Commons Attribution Non-Commercial 4.0 International (CC BY-NC 4.0) License.
The Creative Commons Attribution Non-Commercial (CC-BY-NC) License permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.