[HTML][HTML] How much X is in XAI: Responsible use of “Explainable” artificial intelligence in hydrology and water resources
Abstract Explainable Artificial Intelligence (XAI) offers the promise of being able to provide
additional insight into complex hydrological problems. As the “new kid on the block”, these …
additional insight into complex hydrological problems. As the “new kid on the block”, these …
An evaluation framework for input variable selection algorithms for environmental data-driven models
Abstract Input Variable Selection (IVS) is an essential step in the development of data-driven
models and is particularly relevant in environmental modelling. While new methods for …
models and is particularly relevant in environmental modelling. While new methods for …
Improved validation framework and R-package for artificial neural network models
Validation is a critical component of any modelling process. In artificial neural network (ANN)
modelling, validation generally consists of the assessment of model predictive performance …
modelling, validation generally consists of the assessment of model predictive performance …
Optimal control of total chlorine and free ammonia levels in a water transmission pipeline using artificial neural networks and genetic algorithms
In this study, a model predictive control (MPC) system is developed for the goldfield and
agricultural water system (GAWS) east of Perth in Western Australia. As part of the study …
agricultural water system (GAWS) east of Perth in Western Australia. As part of the study …
Coupled data-driven evolutionary algorithm for toxic cyanobacteria (blue-green algae) forecasting in Lake Kinneret
A Ostfeld, A Tubaltzev, M Rom… - Journal of Water …, 2015 - ascelibrary.org
Cyanobacteria blooming in surface waters have become a major concern worldwide, as
they are unsightly, and cause a variety of toxins, undesirable tastes, and odors. Approaches …
they are unsightly, and cause a variety of toxins, undesirable tastes, and odors. Approaches …