The study of artificial intelligence (AI) has been a continuous endeavor of scientists and
engineers for over 65 years. The simple contention is that human-created machines can do …

In recent years, Earth system sciences are urgently calling for innovation on improving
accuracy, enhancing model intelligence level, scaling up operation, and reducing costs in …

The management of water resources depends heavily on hydrological prediction, and
advances in machine learning (ML) present prospects for improving predictive modelling …

Flood forecasting is an essential requirement in integrated water resource management.
This paper suggests a Long Short-Term Memory (LSTM) neural network model for flood …

Natural hazards have the potential to cause catastrophic damage and significant
socioeconomic loss. The actual damage and loss observed in the recent decades has …

Despite the massive diversity in the modeling requirements for practical hydrological
applications, there remains a need to develop more reliable and intelligent expert systems …

Random forests (RF) is a supervised machine learning algorithm, which has recently started
to gain prominence in water resources applications. However, existing applications are …

The advancements of artificial intelligence models have demonstrated notable progress in
the field of hydrological forecasting. However, predictions of extreme climate events are still …

Prediction of river flow rates is an essential task for both flood protection and optimal water
resource management. The high uncertainty associated with basin characteristics …

Deep learning (DL), a new generation of artificial neural network research, has transformed
industries, daily lives, and various scientific disciplines in recent years. DL represents …