A Deep Learning Approach to Predict Weather Data Using Cascaded LSTM Network
Bibliographic record
Abstract
Weather prediction is a challenging research problem although the revolutionary advancement in deep learning, along with the availability of big data, has significantly alleviated this problem. Moreover, in terms of robustness and computational cost, this problem has currently interested many researchers to develop numerous models. This paper proposes a lightweight yet powerful deep learning architecture for weather forecasting that can outperform some of the existing well-known models. This architecture mainly uses the LSTM layers in a stacked fashion, with a different number of units in each layer. It takes in multiple weather variables as input features for a given time sequence to forecast the same weather parameters in a multi-input multi-output (MIMO) structure. The resulting models are tested to predict the wind speed, relative humidity, dew point and temperature in this study and experimented with different hyper-parameters consisting a number of LSTM layers, a variable learning rate, number of LSTM units. Two models have been built cascading the basic 1hour-ahead model which predicts the weather parameters for the 2 hours and 3 hours ahead. The obtained results show that the cascaded models perform significantly better than the standard LSTM or 1D convolution networks in shorter period prediction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".