1. Cloud classification: principles and applications
- Author
-
Seema Mahajan and Bhavin Fataniya
- Subjects
Environmental Engineering ,Computer science ,business.industry ,media_common.quotation_subject ,Weather forecasting ,Cloud computing ,computer.software_genre ,Hybrid approach ,Sky ,Classifier (linguistics) ,Earth and Planetary Sciences (miscellaneous) ,Satellite ,Data mining ,Literature survey ,Cluster analysis ,business ,computer ,Waste Management and Disposal ,Astrophysics::Galaxy Astrophysics ,media_common ,Water Science and Technology - Abstract
Clouds classification is essentially required in weather forecasting and climate related study. Detection, removal and classification of cloud are the major challenges to deal with in satellite-based images. In this paper, literature survey on cloud classification techniques published during 2000 to 2018 is presented. In recent years, various approaches are applied for cloud classification such as threshold-based, machine learning, clustering, K-means, k-nearest-neighbour (K-NN) algorithm and hybrid approach. Threshold-based is the easiest approach but it fails to classify cloud in complex sky conditions. It is also unable to classify cloud at night time. Machine learning-based approach gives highest accuracy but it depends on various parameters like day/night time, weather season, types of satellite and geographical region of the cloud. It is recommended to have hybrid model with the use of machine learning, threshold values and physical parameters.
- Published
- 2021
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