ARTIFICIAL INTELLIGENCE-BASED PREDICTION MODELS FOR ENVIRONMENTAL ENGINEERING

Evaluation of artificial intelligence based models for chemical biodegradability prediction.

This study presents a review of biodegradability modeling efforts including a detailed assessment of two models developed using an artificial intelligence based methodology. Validation results for these models using an independent, quality reviewed database, demonstrate that the models perform well when compared to another commonly used biodegradability model, against the same data. The ability...

Bayesian Prediction for Artificial Intelligence

This paper shows that the common method used for making predictions under uncertainty in AI and science is in error. This method is to use currently available data to select the best model from a given class of models-this process is called abduction-and then to use this model to make predictions about future data. The correct method requires averaging over all the models to make a prediction-w...

Prediction Based Diagnosis Using Artificial Intelligence Methods

Artificial-Intelligence-based Modeling for Environmental Applications and Decision Support

What we call “environment” or environmental systems is the natural world (or sections thereof) including humans and the artifacts they create. Environmental systems are complex systems, comprising a large number of interacting objects, substances, and processes which are subject to different specialized areas of science, such as biology, chemistry, hydrology, meteorology, and, when it comes to ...

Comparing CI Methods for Prediction Models in Environmental Engineering

The prediction of fill levels in stormwater tanks is an important practical problem in water resource management. In this study state-of-the-art CI methods, i.e. Neural Networks (NN) and Genetic Programming (GP), are compared with respect to their applicability to this problem. The performance of both methods crucially depends on their parametrization. We compare different parameter tuning appr...