Graeme C. Dandy

TL;DR: The steps that should be followed in the development of artificial neural network models are outlined, including the choice of performance criteria, the division and pre-processing of the available data, the determination of appropriate model inputs and network architecture, optimisation of the connection weights (training) and model validation.

TL;DR: Despite a significant amount of research activity on the use of ANNs for prediction and forecasting of water resources variables in river systems, little of this is focused on methodological issues and there is still a need for the development of robust ANN model development approaches.

TL;DR: This paper investigates a three-operator genetic algorithm comprising reproduction, crossover, and mutation, and applies the optimization techniques to a case study pipe network.

TL;DR: In this article, a case study is presented in which ANN methods are used to forecast salinity in the River Murray at Murray Bridge (South Australia) 14 days in advance, and the results obtained were most promising.

TL;DR: Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.