A rainfall prediction model using artificial neural network
Abstract
The multilayered artificial neural network with learning by back-propagation algorithm configuration is the most common in use, due to of its ease in training. It is estimated that over 80% of all the neural network projects in development use back-propagation. In back-propagation algorithm, there are two phases in its learning cycle, one to propagate the input patterns through the network and other to adapt the output by changing the weights in the network. The back-propagation-feed forward neural network can be used in many applications such as character recognition, weather and financial prediction, face detection etc. The paper implements one of these applications by building training and testing data sets and finding the number of hidden neurons in these layers for the best performance. In the present research, possibility of predicting average rainfall over Udupi district of Karnataka has been analyzed through artificial neural network models. In formulating artificial neural network based predictive models three layered network has been constructed. The models under study are different in the number of hidden neurons.
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Two machine learning techniques (SVM and CNN) were developed to extract human settlements from Very High Resolution satellite images of 3 provinces in Afghanistan.
“In [162], two ML techniques (SVM and CNN) were developed to extract human settlements from Very High Resolution (VHR) satellite images of 3 provinces in Afghanistan.”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [162]
By comparing with analyst-verified reference data, the CNN technique yielded more accurate results overall in terms of overall pixel cells, while SVM performed more accurately in omission based on derived statistics.
“By comparing the results with analyst-verified reference data information (LandScan Settlement Layer), the authors demonstrated that in terms of overall pixel cells the CNN technique yielded more accurate results overall while based on derived statistics against the reference data the SVM technique performed more accurately in omission.”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [162]
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Applications and Challenges of Artificial Intelligence in Space Missions
Oche, P. A., Ewa, G. A., & Ibekwe, N. (2024) · IEEE Access · doi:10.1109/access.2021.3132500 · Cited by 68 · see citing works
“3) INEFFICIENCY A fundamental challenge with statistical models is that they are inefficient in prediction due to the irregularly varying patterns of voluminous data [161].”
BackgroundAn Overview of Technological Revolution in Satellite Image Analysis
Department of Computer Science & Engineering, K. A. U., Jenice, A. R., Raimond, K., & Department of Computer Science & Engineering, K. A. U. (2016) · Journal of Engineering Science and Technology Review · doi:10.25103/jestr.094.01 · Cited by 11 · see citing works
- Authors
- Abhishek, K., Kumar, A., Ranjan, R., & Kumar, S.
- Affiliations
- National Institute of Technology Patna (IN)
- Deolitte India, Pune, India
- Venue
- 2012 IEEE Control and System Graduate Research Colloquium
- Publisher
- IEEE
- Published
- 2012-07
- Pages
- 82-87
- OpenAlex
- W2094978308
- Cited by
- 234
- References
- 0
- Citations by year
- 2026: 2 · 2025: 14 · 2024: 25 · 2023: 35 · 2022: 20 · 2021: 28 · 2020: 21 · 2019: 20 · 2018: 15 · 2017: 9 · 2016: 13 · 2015: 14 · 2014: 11 · 2013: 7
- FWCI
- 46.06
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Control and System Graduate Research Colloquium (ICSGRC) (publishedVersion)
- Topics
- Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- precipitation forecasting, artificial neural networks, backpropagation, feedforward neural networks, Udupi district, weather forecasting, training and testing datasets
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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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