Implementation of Anticipating Rainfall Using Machine Learning

Year : 2023 | Volume :01 | Issue : 01 | Page : 1-8
By

    Shaik Shafeeq Ahmed

  1. Shivaraj

  2. Suneet Lionel Dsouza

  3. Simran Pal R

  1. Student, Department of Computer Science and Engineering, H.K.B.K. College of Engineering, Nagawara, Bangalore, Karnataka, India
  2. Student, Department of Computer Science and Engineering, H.K.B.K. College of Engineering, Nagawara, Bangalore, Karnataka, India
  3. Student, Department of Computer Science and Engineering, H.K.B.K. College of Engineering, Nagawara, Bangalore, Karnataka, India
  4. Assistant Professor, Department of Computer Science and Engineering, H.K.B.K. College of Engineering, Nagawara, Bangalore, Karnataka, India

Abstract

Rainfall forecasting is crucial for many aspects of our national economy and should help prevent major seasonal droughts. Since agriculture is a beloved profession in many states, some Asian countries are economically hooked to decline. Previous precipitation info is beneficial. Farmers are cancerous in managing their crops, resulting in economic progress for the country. downfall prediction is hard for earth science scientists because of unordering time and unordered quantity of downfall. This enables the development of fresh herb designs and the efficient use of aquatic resources for the crops. Linear and non-linear style square measures are widely used to forecast seasonal decline. Several algorithms that are computer-aided rule-based techniques include the support vector machine (SVM), genetic algorithm (GA), and CART. Regression, Naive Bayes, Random Forest, and other analytical techniques are frequently used in this study. Overall, we tend to look at how the algorithmic rule that can be applied qualitatively is anticipating failure. The task appears challenging and sophisticated since it requires a vast array of specialist workers, and every person’s choice is enthralled with no assurance of success

Keywords: Rainfall detection, Anticipation, Prediction, Machine Learning, Genetic Algorithm

[This article belongs to International Journal of Satellite Remote Sensing(ijsrs)]

How to cite this article: Shaik Shafeeq Ahmed, Shivaraj, Suneet Lionel Dsouza, Simran Pal R , Implementation of Anticipating Rainfall Using Machine Learning ijsrs 2023; 01:1-8
How to cite this URL: Shaik Shafeeq Ahmed, Shivaraj, Suneet Lionel Dsouza, Simran Pal R , Implementation of Anticipating Rainfall Using Machine Learning ijsrs 2023 {cited 2023 Dec 22};01:1-8. Available from: https://journals.stmjournals.com/ijsrs/article=2023/view=129918


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References

  1. M. Sumi, M.F. Zaman and H. Hirose a rainfall forecasting method using machine learning models and its application to Fukuoka city case”. Article in International Journal of Applied Mathematics and Computer Science December 2012(,1-14), amcs.
  2. Rahman, M. M., Bhattacharya, P., & Desai, B. C. (2007)” A framework for medical image retrieval using machine learning and statistical similarity matching techniques with relevance feedback”, IEEE Transactions on Information Technology in Biomedicine, 11(1),58- 69, https://www.semanticscholar.org/paper/A-Framework-for-Medical-Image-Retrieval-Using-and-Rahman-Bhattacharya/cdcf84a6bb5581ae7fc25680be1083344871029e
  3. Morales, L. Tapia, R. Pearce, S. Rodriguez & M. M. Amat o. (2004), “A machine learning approach for feature- sensitive motion planning. In Algorithmic Foundations of Robotics “VI, star-17, (pp. 361-376). Springer, Berlin, Heidelberg,https://link.springer.com/chapter/10.1007/10991541_25.
  4. Ireland, G., Volpi, M., & Petropoulos, G. P. (2015),” Examining the capability of supervised machine learning classifiers in extracting flooded areas from Landsat TM imagery: a case study from a Mediterranean flood. Remote sensing, Volume: 7, Issue-(3), 3372-3399.
  5. Huang, C., Davis, L. S., & Townshend, J. R. G. (2002). An assessment of support vector machines for land cover classification. International Journal of remote sensing, Volume-23. Issue-(4), 725-749.
  6. Chantasut, C. Charoenjit, and C. Tanprasert, “Predictive mining of rainfall predictions using artificial neural networks for the Chao Phraya River,” 4th Int Conf. of the Asian Federation of Inform. Technology in Agriculture and the 2nd World Congr. on Comput. in Agriculture and Natural Resources, pp. 117-122, Bangkok, Thailand, 2004.
  7. Kotsiantis, S. B., Zaharakis, I., &Pintelas, P. (2007). Supervised machine learning: A review of classification techniques. Emerging artificial intelligence applications in computer engineering, 160,3-24. Cheriyadat, A. M. (2014). Unsupervised feature learning for aerial scene classification. IEEE Transactions on Geoscience and Remote Sensing, Volume-52, issue-(1),439-451.
  8. Bauer, S., Nolte, LP., Reyes, M. (2011). Fully Automatic Segmentation of Brain Tumor Images Using Support Vector Machine Classification in Combination with Hierarchical Conditional Random Field Regularization. In: Fichtinger, G., Martel, A., Peters, T. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. MICCAI 2011. Lecture Notes in Computer Science, vol 6893. Springer, Berlin, Heidelberg.
  9. Chattopadhyay and M. Chattopadhyay, “A Soft Computing Technique in Rainfall Forecasting,” International Conference on Information Technology, HIT, pp. 19-21, 2007, https://arxiv.org/ftp/nlin/papers/0703/0703042.pdf.
  10. Chattopadhyay and G. Chattopadhyay, “Comparative study between different neural net learning algorithms applied to rainfall time series,” Meteorological Applications, vol. 15, no. 2, pp. 273- 280, 2008.
  11. Guhathakurta, “Long-lead monsoon rainfall prediction using a deterministic Artificial Neural Network model,” Meteorology and Atmospheric Physics 101, pp. 93-108, 2008.
  12. L. Wu, K. W. Chau, and C. Fan, “Prediction of rainfall time series using Modular Artificial Neural Networks coupled with data preprocessing techniques,” Journal of Hydrology, Volume-389, issue- 1, pp. 146-167, 2010,https://www.sciencedirect.com/science/article/abs/pii/S0022169410003252.
  13. K. Htike and O. O. Khalifa, “Rainfall Forecasting Models Using Focused Time-Delay Neural Networks,” Int. Conf. on IEEE, Comput. and Commun. Eng. (ICCCE), 2010, IEEE Xplore.
  14. Subimal Ghosh and S. Kannan, “Prediction of daily rainfall state in a river basin using statistical downscaling from GCM output,” Springer-Verlag, July 2010.
  15. Kannan, S.Prabhakaran, and P.Ramachandran, “Rainfall Forecasting Using Data Mining Technique,” International Journal of Engineering and Technology, Vol.2, issue- (6), pp. 397-401, 2010.
  16. Geetha and R. S. Selvaraj, “Prediction of monthly rainfall in Chennai using Back Propagation Neural Network model,” International Journal of Engineering Sciences and Technology, vol. 3, issue- 1, pp. 211-213, 2011.
  17. A. Sharma and J. B. Singh, “Comparative Study of Rainfall Forecasting Models,” New York Science Journal, vol. 115, no. 1, 2011, pp. 115-120.
  18. Abbot and J. Marohasy, “Application of Artificial Neural Networks to Rainfall Forecasting in Queensland, Australia,” Advances in Atmospheric Sciences, vol. 29, no. 4, 2012, pp. 717-730.
  19. Kumar, A. Kumar, R. Ranjan, and S. Kumar, “A rainfall prediction model using artificial neural network,” Control and Syst. Graduate Research Colloq. (ICSGRC), 2012, pp. 82-87.
  20. R. Deshpande, “On the Prediction of Rainfall Time Series Using Multilayer Perceptron Artificial Neural Network,” International Journal of Emerging Technology and Advanced Engineering, vol. 2, no. 1, pp. 148-153, 2012.
  21. “Designing a Rule-Based Hourly Rainfall Prediction Model,” Soo-Yeon Ji, Sharad Sharma, Byunggu Yu, and Dong Hyun Jeong, IEEE IRI 2012, August – 2012,https://ieeexplore.ieee.org/document/6303024.
  22. Shrivastava, S. Karmakar, and M. K. Kowar, “BPN model for long-term monsoon rainfall forecasting over a very small geographical region and its validation for 2012,” Geofizika, vol. 30, issue- 2, pp. 155-172, 2013.
  23. L. Wu and K. W. Chau, “Prediction of rainfall time series using modular soft computing methods,” Engineering Applications of Artificial Intelligence, vol. 26, issue-3, pp. 997-1007, 2013.
  24. “Prediction Of Rainfall Using Data Mining Technique Over Assam,” Pinky Saikia Dutta and Hitesh Tahbilder, Indian Journal of Computer Science and Engineering (IJCSE), Vol. 5 issue-2 Apr May 2014.
  25. Pankratz, Concepts and Cases for Forecasting with Univariate Box-Jenkins Models. 414 pages, John Wiley & Sons, Inc., New York.
  26. T. Hagan, H. B. Demuth, and M. Beale, Neural Network Design, Thomson Asia Pte. Ltd, Singapore, 2002, https://hagan.okstate.edu/NNDesign.pdf
Regular Issue Subscription Original Research
Volume 01
Issue 01
Received July 27, 2023
Accepted September 20, 2023
Published December 22, 2023
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