Flood forecasting is an essential non-engineering measure for flood prevention and disaster
reduction. Many models have been developed to study the complex and highly random …

Time series prediction with neural networks has been the focus of much research in the past
few decades. Given the recent deep learning revolution, there has been much attention in …

Recent observations with varied schedules and types (moving average, snapshot, or
regularly spaced) can help to improve streamflow forecasts, but it is challenging to integrate …

Accurate river streamflow forecasts are a vital tool in the fields of water security, flood
preparation and agriculture, as well as in industry more generally. Traditional physics-based …

Recently, there has been much attention in the use of machine learning methods,
particularly deep learning for stock price prediction. A major limitation of conventional deep …

As the most water shortage and water polluted area in China, the water quality prediction is
of utmost needed and important in Haihe River Basin for its water resource management …

The occurrence of flash floods in urban catchments within the Mediterranean climate zone
has witnessed a substantial rise due to climate change, underscoring the urgent need for …

Forecasting discharge (Q) and water level (H) are essential factors in hydrological research
and flood prediction. In recent years, deep learning has emerged as a viable technique for …

The fourth industrial revolution essential components which include cloud computing
technology, artificial intelligence, big data, and the internet of things has also been affecting …

Various supervised machine-learning algorithms for wind power forecasting have been
developed in recent years to manage wind power fluctuations and effectively correlate to …