To improve the interpretability of estimation processes in machine learning, we applied the
Shapley additive explanation method to six tree-based models for predicting downstream …

Oyster farms provide a sustainable and profitable export for New Zealand. Oyster farms are
sensitive to changes in salinity that can cause significant crop loss if they persist too long …

The use of artificial neural network (ANN) models in water resources applications has grown
considerably over the last decade. However, an important step in the ANN modelling …

Salinity management in estuarine systems is crucial for developing effective water-
management strategies to maintain compliance and understand the impact of salt intrusion …

There is growing tension between high-performance machine-learning (ML) models and
explainability within the scientific community. In arsenic modelling, understanding why ML …

Electrical conductivity (EC), one of the most widely used indices for water quality
assessment, has been applied to predict the salinity of the Babol-Rood River, the greatest …

As critical transitional ecosystems, estuaries are facing the increasingly urgent threat of salt
wedge intrusion, which impacts their ecological balance as well as human-dependent …

Understanding the combined influences of meteorological and hydrological factors on water
level and flood events is essential, particularly in today's changing climate environments …

Salinity intrusion poses significant challenges to coastal regions worldwide. Reliable salinity
prediction models can provide valuable information to mitigate the impact and influence of …

This review focuses on the use of Interpretable Artificial Intelligence (IAI) and eXplainable
Artificial Intelligence (XAI) models for data imputations and numerical or categorical …