Intelligent Methods Used for Obtaining Weather Derivatives: A Review

Gujanatti Rudrappa; Nataraj Vijapur

doi:doi:10.11648/j.eas.20190406.12

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Intelligent Methods Used for Obtaining Weather Derivatives: A Review

Gujanatti Rudrappa, Nataraj Vijapur

Published in Engineering and Applied Sciences (Volume 4, Issue 6)

Received: 24 July 2019 Accepted: 31 October 2019 Published: 6 December 2019

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Abstract

Weather is the condition of the atmosphere and forecasting is predicting the condition of the atmosphere in near future. Weather forecasting is a formidable challenge as weather is a multi- dimensional, continuous and chaotic process. The distinct nature of the model forecasting in all situations accurately is challenging. Presently weather conditions are being obtained from satellites, Doppler radar, radio sounds, observations from aircraft and ground. Collected data is subjected to various statistical and machine learning techniques. These techniques can incorporate relatively simple observation of the sky to highly complex computerized mathematical models. Weather forecasting still remains a challenging issue, due to unpredictable and chaotic nature of weather. Even with present methods the weather forecasting system may still fail to predict weather attribute, therefore there is still scope left to improve these systems. The objective of carrying out the survey is to look forward on how machine learning can help us to improve weather parameter estimation. In this paper we report the different methods carried out by leading researchers, formidable challenges and present our views on development of efficient weather forecasting system. Also, we propose a method which makes use image processing and neural networks to achieve the weather parameter estimation.

Published in	Engineering and Applied Sciences (Volume 4, Issue 6)
DOI	10.11648/j.eas.20190406.12
Page(s)	144-148
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Weather, Weather Forecasting, Weather Forecasting Systems, Artificial Intelligence, Machine Learning, Image Processing

References

[1]	Garima Jain and Bhawna Mallick, “A Review on Weather Forecasting Techniques,” in International Journal of Advanced Research in Computer and Communication Engineering, Vol 5, Issue 12, pp.177–180, December 2016.
[2]	H. Saima, J. Jaafar, S. Belhaouari and T. A. Jillani, "Intelligent methods for weather forecasting: A review,” 2011 National Postgraduate Conference, Kuala Lumpur, pp. 1-6, 2011. Available: https://www.researchgate.net/publication/239764598_Intelligent_methods_for_weather_forecasting_A_review
[3]	Vijay Kumar Didal, Brijbhooshan, Anita Todawat and Kamlesh Choudhary, "Weather Forecasting in India: A Review", Int. J. Curr. Microbiol. App. Sci, 6 (11): 577-590, 2017.
[4]	W. Chu, K. Ho and A. Borji, "Visual Weather Temperature Prediction,” 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, pp. 234-241, 2018. Available: https://arxiv.org/abs/1801.08267
[5]	Wei-Ta Chu, Xiang-You Zheng, Ding-Shiuan Ding, “Camera as weather sensor Estimating weather information from single images,” Journal of Visual Communication and Image Representation, Volume 46, Pages 233-249, July 2017.
[6]	C. Lu, D. Lin, J. Jia and C. Tang, "Two-Class Weather Classification,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 12, pp. 2510-2524, 1 Dec. 2017.
[7]	C. I. Christodoulou, S. C. Michaelides and C. S. Pattichis, "Multifeature texture analysis for the classification of clouds in satellite imagery,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 41, no. 11, pp. 2662-2668, Nov. 2003.
[8]	Bautu, E. Bautu, September,” Meteorological Data Analysis and Prediction by Means of Genetic Programming,” Proceedings of the Fifth Workshop on Mathematical Modeling of Environmental and Life Sciences Problems Constant¸a, Romania, pp. 35–42, 2006.
[9]	Esfandeh, S. and Sedighizadeh, M., "Meteorological data study and forecasting using particle swarm optimization algorithm,” World Academy of Science, Engineering and Technology, vol.59, pp.2117-2119, 2011, 2011.
[10]	S. K. Mohapatra, A. Upadhyay and C. Gola, "Rainfall prediction based on 100 years of meteorological data,” 2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN), Gurgaon, pp. 162-166, 2017. Available: https://www.researchgate.net/publication/323063793_Rainfall_prediction_based_on_100_years_of_meteorological_data
[11]	Volokitin, R. Timofte and L. Van Gool, "Deep Features or Not: Temperature and Time Prediction in Outdoor Scenes,” 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Las Vegas, NV, pp. 1136-1144, 2016. Available: https://homes.esat.kuleuven.be/~konijn/publications/2016/Volokitin-CVPRW-2016.pdf
[12]	Jiwan Lee; Bonghee Hong; Sunghoon Jung; Victor Chang, “Clustering learning model of CCTV image pattern for producing road hazard meteorological information,” Future Generation Computer Systems, ISSN: 0167-739X, Vol: 86, Page: 1338-1350, 2018.
[13]	S. Liu, L. Zhang, Z. Zhang, C. Wang and B. Xiao, "Automatic Cloud Detection for All-Sky Images Using Superpixel Segmentation,” in IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 2, pp. 354-358, Feb. 2015.
[14]	C. Papin, P. Bouthemy and G. Rochard, "Unsupervised segmentation of low clouds from infrared METEOSAT images based on a contextual spatio-temporal labeling approach,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 40, no. 1, pp. 104-114, Jan. 2002.
[15]	M. Mahrooghy, N. H. Younan, V. G. Anantharaj, J. Aanstoos and S. Yarahmadian, "On the Use of a Cluster Ensemble Cloud Classification Technique in Satellite Precipitation Estimation,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 5, no. 5, pp.1356-1363, Oct. 2012.
[16]	S. Dev, Y. H. Lee and S. Winkler, "Color-Based Segmentation of Sky/Cloud Images From Ground-Based Cameras,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, no. 1, pp. 231-242, Jan. 2017.
[17]	T. Shiraishi, T. Motohka, R. B. Thapa, M. Watanabe and M. Shimada, April 2014, "Comparative Assessment of Supervised Classifiers for Land Use–Land Cover Classification in a Tropical Region Using Time-Series PALSAR Mosaic Data,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 4, pp. 1186-1199.
[18]	Lee and M. T. Mahmood, "Robust Registration of Cloudy Satellite Images Using Two-Step Segmentation,” in IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 5, pp. 1121-1125, May 2015.
[19]	Q. Zhang and C. Xiao, "Cloud Detection of RGB Color Aerial Photographs by Progressive Refinement Scheme,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 11, pp. 7264-7275, Nov. 2014.
[20]	Y. Chen, L. Cheng, M. Li, J. Wang, L. Tong and K. Yang, "Multiscale Grid Method for Detection and Reconstruction of Building Roofs from Airborne LiDAR Data,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 10, pp. 4081-4094, Oct. 2014.
[21]	J. M. Weiss, S. A. Christopher and R. M. Welch, "Automatic contrail detection and segmentation,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 36, no. 5, pp. 1609-1619, Sept. 1998.
[22]	R. Venkata Rao, "Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems,” in International Journal of Industrial Engineering Computations, vol.7, pp. 19–34, 2016.
[23]	D. Binu and B. S. Kariyappa, "RideNN: A New Rider Optimization Algorithm-Based Neural Network for Fault Diagnosis in Analog Circuits,” in IEEE Transactions on Instrumentation and Measurement. doi: 10.1109/TIM.2018.2836058.
[24]	Mihail RP., Workman S., Bessinger Z., Jacobs N. 2016, “Sky Segmentation in the Wild: An Empirical Study,” in IEEE Winter Conference on Applications of Computer Vision (WACV), pp-1–6. Available: http://cs.uky.edu/~jacobs/papers/mihail2016skyfinder.pdf
[25]	Wei-Ta Chu, Kai-Chia Ho, and Ali Borji, "Visual Weather Temperature Prediction,” Proceedings of IEEE Winter Conference on Applications of Computer Vision, pp. 234-241, 2018.
[26]	SUN Database: Large-scale Scene Recognition from Abbey to Zoo". J. Xiao, J. Hays, K. Ehinger, A. Oliva, and A. Torralba. IEEE Conference on Computer Vision and Pattern Recognition, 2010. Available: https://www.cc.gatech.edu/~hays/papers/sun.pdf
[27]	LabelMe: a database and web-based tool for image annotation. B. Russell, A. Torralba, K. Murphy, W. T. Freeman, International Journal of Computer Vision, 2007.
[28]	SWIMSEG Database https://sites.wustl.edu/clouddetection/introduction/data-sets/ (Website accesed on 30th May, 2019).
[29]	Jacobs N., Burgin W., Fridrich N., Abrams A., Miskell K., Braswell BH., Richardson AD., Pless R. 2009, “The Global Network of Outdoor Webcams: Properties and Applications,” in ACM International Conference on Advances in Geographic Information Systems (ACM/SIGSPATIAL GIS), pp-111–120. https://dl.acm.org/citation.cfm?id=1653789
[30]	Nataraj A Vijapur, R. Srinivasa Rao Kunte, “Sensitized Glaucoma Detection by Using a Unique Template Based Correlation Filter and Undecimated Isotropic Wavelet Transform,” Journal of Medical and Biological Engineering (JMBE), Springer, an SCI and EI journal, Volume 37, Issue 3, pp 365–373, June 2017.
[31]	Nataraj A Vijapur, R. Srinivasa Rao Kunte, “Improved Efficiency of Glaucoma Detection by using Wavelet Filters, Prediction and Segmentation Method,” International Journal of Electronics, Electrical and Computational System, IJEECS, Academic Science, Volume 3, Issue 8, pp. 1-13, Oct 2014.
[32]	Nataraj A Vijapur, R. Srinivasa Rao Kunte, “Glaucoma Detection by Using Pearson-R Correlation Filter,” Proc., 4th IEEE International Conference on communication and Signal Processing, Melmaruvathur, Tamilnadu, India, IEEE Xplore®, pp.1194-1198, April 2015. https://ieeexplore.ieee.org/document/7322695.

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APA Style

Gujanatti Rudrappa, Nataraj Vijapur. (2019). Intelligent Methods Used for Obtaining Weather Derivatives: A Review. Engineering and Applied Sciences, 4(6), 144-148. https://doi.org/10.11648/j.eas.20190406.12

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ACS Style

Gujanatti Rudrappa; Nataraj Vijapur. Intelligent Methods Used for Obtaining Weather Derivatives: A Review. Eng. Appl. Sci. 2019, 4(6), 144-148. doi: 10.11648/j.eas.20190406.12

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AMA Style

Gujanatti Rudrappa, Nataraj Vijapur. Intelligent Methods Used for Obtaining Weather Derivatives: A Review. Eng Appl Sci. 2019;4(6):144-148. doi: 10.11648/j.eas.20190406.12

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@article{10.11648/j.eas.20190406.12,
  author = {Gujanatti Rudrappa and Nataraj Vijapur},
  title = {Intelligent Methods Used for Obtaining Weather Derivatives: A Review},
  journal = {Engineering and Applied Sciences},
  volume = {4},
  number = {6},
  pages = {144-148},
  doi = {10.11648/j.eas.20190406.12},
  url = {https://doi.org/10.11648/j.eas.20190406.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.eas.20190406.12},
  abstract = {Weather is the condition of the atmosphere and forecasting is predicting the condition of the atmosphere in near future. Weather forecasting is a formidable challenge as weather is a multi- dimensional, continuous and chaotic process. The distinct nature of the model forecasting in all situations accurately is challenging. Presently weather conditions are being obtained from satellites, Doppler radar, radio sounds, observations from aircraft and ground. Collected data is subjected to various statistical and machine learning techniques. These techniques can incorporate relatively simple observation of the sky to highly complex computerized mathematical models. Weather forecasting still remains a challenging issue, due to unpredictable and chaotic nature of weather. Even with present methods the weather forecasting system may still fail to predict weather attribute, therefore there is still scope left to improve these systems. The objective of carrying out the survey is to look forward on how machine learning can help us to improve weather parameter estimation. In this paper we report the different methods carried out by leading researchers, formidable challenges and present our views on development of efficient weather forecasting system. Also, we propose a method which makes use image processing and neural networks to achieve the weather parameter estimation.},
 year = {2019}
}

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TY - JOUR
T1 - Intelligent Methods Used for Obtaining Weather Derivatives: A Review
AU - Gujanatti Rudrappa
AU - Nataraj Vijapur
Y1 - 2019/12/06
PY - 2019
N1 - https://doi.org/10.11648/j.eas.20190406.12
DO - 10.11648/j.eas.20190406.12
T2 - Engineering and Applied Sciences
JF - Engineering and Applied Sciences
JO - Engineering and Applied Sciences
SP - 144
EP - 148
PB - Science Publishing Group
SN - 2575-1468
UR - https://doi.org/10.11648/j.eas.20190406.12
AB - Weather is the condition of the atmosphere and forecasting is predicting the condition of the atmosphere in near future. Weather forecasting is a formidable challenge as weather is a multi- dimensional, continuous and chaotic process. The distinct nature of the model forecasting in all situations accurately is challenging. Presently weather conditions are being obtained from satellites, Doppler radar, radio sounds, observations from aircraft and ground. Collected data is subjected to various statistical and machine learning techniques. These techniques can incorporate relatively simple observation of the sky to highly complex computerized mathematical models. Weather forecasting still remains a challenging issue, due to unpredictable and chaotic nature of weather. Even with present methods the weather forecasting system may still fail to predict weather attribute, therefore there is still scope left to improve these systems. The objective of carrying out the survey is to look forward on how machine learning can help us to improve weather parameter estimation. In this paper we report the different methods carried out by leading researchers, formidable challenges and present our views on development of efficient weather forecasting system. Also, we propose a method which makes use image processing and neural networks to achieve the weather parameter estimation.
VL - 4
IS - 6
ER -

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Author Information

Gujanatti Rudrappa

Department of Electronics and Communication, KLE Dr. M. S. Sheshgiri College of Engineering and Technology, VTU, Belgaum, India
Nataraj Vijapur

Department of Electronics and Communication, KLE Dr. M. S. Sheshgiri College of Engineering and Technology, VTU, Belgaum, India

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Gujanatti Rudrappa, Nataraj Vijapur. (2019). Intelligent Methods Used for Obtaining Weather Derivatives: A Review. Engineering and Applied Sciences, 4(6), 144-148. https://doi.org/10.11648/j.eas.20190406.12

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ACS Style

Gujanatti Rudrappa; Nataraj Vijapur. Intelligent Methods Used for Obtaining Weather Derivatives: A Review. Eng. Appl. Sci. 2019, 4(6), 144-148. doi: 10.11648/j.eas.20190406.12

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AMA Style

Gujanatti Rudrappa, Nataraj Vijapur. Intelligent Methods Used for Obtaining Weather Derivatives: A Review. Eng Appl Sci. 2019;4(6):144-148. doi: 10.11648/j.eas.20190406.12

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@article{10.11648/j.eas.20190406.12,
  author = {Gujanatti Rudrappa and Nataraj Vijapur},
  title = {Intelligent Methods Used for Obtaining Weather Derivatives: A Review},
  journal = {Engineering and Applied Sciences},
  volume = {4},
  number = {6},
  pages = {144-148},
  doi = {10.11648/j.eas.20190406.12},
  url = {https://doi.org/10.11648/j.eas.20190406.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.eas.20190406.12},
  abstract = {Weather is the condition of the atmosphere and forecasting is predicting the condition of the atmosphere in near future. Weather forecasting is a formidable challenge as weather is a multi- dimensional, continuous and chaotic process. The distinct nature of the model forecasting in all situations accurately is challenging. Presently weather conditions are being obtained from satellites, Doppler radar, radio sounds, observations from aircraft and ground. Collected data is subjected to various statistical and machine learning techniques. These techniques can incorporate relatively simple observation of the sky to highly complex computerized mathematical models. Weather forecasting still remains a challenging issue, due to unpredictable and chaotic nature of weather. Even with present methods the weather forecasting system may still fail to predict weather attribute, therefore there is still scope left to improve these systems. The objective of carrying out the survey is to look forward on how machine learning can help us to improve weather parameter estimation. In this paper we report the different methods carried out by leading researchers, formidable challenges and present our views on development of efficient weather forecasting system. Also, we propose a method which makes use image processing and neural networks to achieve the weather parameter estimation.},
 year = {2019}
}

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