Rainfall Forecasting by using Residual Network with Cloud Image and Humidity

Tsukahara, Jun; Fujimoto, Yasutaka; Fudeyasu, Hironori

doi:10.1109/indin41052.2019.8972197

Cited by 5 publications

(1 citation statement)

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“…People used to predict the weather by looking at the sky and observing the clouds, behavior of animals, smell of soil, etc., by using their sense organs. Nowadays technology has reached a state where machines can predict things like humans once trained [1]. It will be a boon to them if technology can give prior knowledge of such weather changes to make appropriate decisions to protect their crops.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning Approach - An Efficient Method to Predict Rainfall Based on Ground-Based Cloud Images

Ambildhuke¹,

Banik²

2021

ISI

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Clouds play a vital role in climate prediction. Rainfall prediction also majorly depends on the status and types of clouds present in the sky. Therefore, cloud identification is the most exciting and vital topic in meteorology and attracts most researchers from other areas. This paper presents the transfer learning technique to predict the Rainfall based on ground-based Cloud images responsible for rains. It will predict the estimated Rainfall by identifying the type of cloud by taking cloud images as input. The cloud images in the dataset are divided into three categories(classes) labeled as no-rain to very low-rain, low to medium-rain, and medium to high Rain based on the associated Precipitation responsible for the appropriate Rainfall. This model will be most helpful to the farmers to manage their Irrigation by knowing the status of Rainfall before every irrigation cycle or can also be helpful to take decisions on the outdoor events by taking prior knowledge of Rain. The model is trained on three classes to predict the Rainfall and firstly experimented with CNN. To improve the performance, the experiment is carried out with some best-pretrained models VGG16, Inception-V3, and XCeption using transfer learning and, the results are compared to the regular CNN model. The transfer learning technique is outperformed to get good accuracy as the dataset is too small and presented the best possible results of the model. Google colab with GPU setting makes the task fast and efficient to get the appropriate results in time, and performance achieved by transfer learning is excellent and can fulfill real-time requirements.

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Section: Introductionmentioning

confidence: 99%