Rainfall Prediction Using Transformer Encoder-Decoder Architecture
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Solution Overview
Problem
Existing rainfall prediction methods, particularly those based on machine learning, struggle to effectively learn the complex nonlinear relationships between rainfall and other influencing factors, leading to limitations in prediction accuracy.
Innovation Solution
A rainfall prediction method based on machine learning is developed, utilizing an encoder-decoder architecture with a fully connected layer. The encoder employs a multi-head probabilistic sparse self-attention module and a distillation module to extract dependency features, while the decoder performs masked multi-head probabilistic sparse self-attention and multi-head self-attention to capture long-distance dependencies, ultimately adjusting the output through a fully connected layer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If recurrent structure networks (RNN, LSTM) are used for rainfall prediction, then sequence prediction capability is improved, but gradient vanishing and gradient exploding problems occur making the model difficult or impossible to train
Solution Approach 1:
The model segments the sequence processing into distinct encoder and decoder components with different attention mechanisms. The encoder uses self-attention to process input sequences independently, while the decoder uses masked multi-head attention to generate predictions, avoiding the sequential gradient propagation that causes vanishing/exploding gradients in RNNs.
Solution Approach 2:
The encoder-decoder architecture introduces an intermediary representation that captures sequence features without requiring continuous backward gradient flow. The encoder processes the entire input sequence forward to create intermediate representations, which the decoder then uses for prediction, eliminating the need for long-chain gradient propagation.
2Device complexity
If traditional statistical models are used for rainfall prediction, then simplicity is maintained, but capability to handle complex nonlinear relationships and special events is limited
Solution Approach 1:
The model changes the parameters and structure by implementing multi-head attention mechanisms with different projection dimensions. This allows the model to capture multiple types of relationships simultaneously, handling complex nonlinear patterns while maintaining a structured approach that builds upon simpler attention concepts.
Solution Approach 2:
The architecture combines multiple attention mechanisms (self-attention, masked multi-head attention) with encoder-decoder structure to create a composite model that handles complex nonlinear relationships. This composite approach integrates different functional components, each contributing specific capabilities for handling various aspects of meteorological data patterns.
3Reliability
If deterministic models with physical principles are used for rainfall prediction, then physical interpretability is improved, but prediction accuracy is not high due to complex construction
Solution Approach 1:
The model replaces traditional mechanical/deterministic physical modeling with a data-driven attention mechanism approach. Instead of explicitly modeling physical processes through differential equations, the attention mechanisms automatically learn and capture the underlying patterns in meteorological data, achieving higher accuracy while maintaining interpretability through the attention weights.
Data Source
AI summary
The present invention is a rainfall prediction method, system, device and medium based on machine learning, which relates to the field of meteorological prediction technology. It uses atmospheric precipitable water volume (PWV) data, rainfall data and related meteorological parameters to input into a trained rainfall prediction network, and realizes accurate prediction of rainfall through an improved Transformer model. The model includes an encoder, a decoder and a final output layer, wherein feature extraction is performed inside the encoder through a multi-head probabilistic sparse self-attention module and a distillation module, and the encoder output containing feature information is used as the input of the decoder. The decoder passes through the decoder mask multi-head probabilistic sparse self-attention layer, and performs a multi-head self-attention operation with the intermediate result output by the encoder, and finally adjusts the data output dimension through a fully connected layer to generate a prediction result.