Transfer Learning Approach - An Efficient Method to Predict Rainfall Based on Ground-Based Cloud Images
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".