Kernekoncepter
The author explores the use of machine learning techniques for time series predictions in unmonitored sites, highlighting the superiority of modern ML methods over traditional models and the challenges faced in transferring knowledge to new regions.
Resumé
The content delves into the challenges of predicting environmental variables in unmonitored sites due to inadequate monitoring. It discusses the use of machine learning methods, particularly deep learning architectures like LSTM networks, for accurate predictions. The article emphasizes the importance of incorporating site characteristics and process knowledge into ML frameworks for effective predictions.
Key points include:
- Limited monitoring data poses challenges for water resource prediction.
- Machine learning outperforms traditional models for hydrological predictions.
- Deep learning architectures like LSTMs are effective for time series modeling.
- Incorporating site characteristics is crucial for accurate predictions.
- Transfer learning and domain adaptation are essential strategies for improving model performance.
Statistik
The US Geological Survey streamflow monitoring network covers less than 1% of stream reaches in the United States (Ahuja, 2016).
Less than 5% of lakes with at least 4 hectares have sufficient temperature measurements (E. K. Read et al., 2017).
LSTM networks have shown superior performance compared to process-based and classical ML models (Jia, Zwart, et al., 2021).
Citater
"Modern machine learning methods outperform process-based and empirical models for hydrologic time series prediction." - Content
"Deep learning architectures like LSTMs have shown to outperform both state-of-the-art process-based models and classical ML models." - Content