Recognizing and estimating rainfall using cloud images

Mano, S.; Ananth, J. P.; Kavitha, V.; SahayaAru, J.

doi:10.1109/rstscc.2010.5712875

Cited by 1 publication

(2 citation statements)

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“…7 As a result, early rainfall prediction has been one of the most difficult undertakings globally in recent years. 8 Rainfall seems to be a complex atmospheric phe-nomenon impacted by several climatic factors. Rainfall predictions that are accurate and timely can be valuable in a variety of ways, including water resource management planning, issuing early flood warnings, controlling aircraft operations, and limiting transportation and construction activities.…”

Section: Introductionmentioning

confidence: 99%

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A novel fuzzy based deep neural network for rain fall prediction using cloud images

Manoj¹,

Harshavardhanan

Victor

et al. 2022

Concurrency and Computation

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Summary The most challenging aspect of weather forecasting is predicting rainfall. In contrast, timely and precise rainfall forecasting is immensely useful in developing effective security measures for active infrastructure projects, logistics, agricultural activities, air transport operations, and flood scenarios. Artificial intelligence has been established to predict rain precipitation, but very little research has been conducted on images than time‐series data to predict rainfall. However, the accuracy of the prediction model utilizing pictures is lower when compared to time series data. Thus a novel fuzzy based deep neural network (NFDNN) model was created to increase rainfall prediction performance. Initially, our research utilized input cloud images, in which the fuzzy wavelet incorporated with neural networks is utilized to identify clouds in the sky. Second, the types of clouds are determined by a fuzzy deep neural networks cloud classifier in which the adaptive fuzzy sparse representation is used. Then, the density‐based spatial clustering for applications containing noise clustering method was used to detect rainy clouds by evaluating the cloud's color and density from the fuzzy deep neural network cloud classifier. Finally, rainfall was predicted using information such as cloud categorization, appearance, and height obtained from cloud position and cloud types. As a result, the proposed NFDNN model gives a better prediction result than the existing methods.

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Section: Introductionmentioning

confidence: 99%

“…Each year, many persons die or are displaced due to heavy rain and flooding 7 . As a result, early rainfall prediction has been one of the most difficult undertakings globally in recent years 8 . Rainfall seems to be a complex atmospheric phenomenon impacted by several climatic factors.…”

Section: Introductionmentioning

confidence: 99%