Artificial Neural Networks (ANNs) are being used increasingly to predict and forecast water resources variables. In this paper, the steps that should be followed in the development of such models are outlined. These include 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. The options available to modellers at each of these steps are discussed and the issues that should be considered are highlighted. A review of 43 papers dealing with the use of neural network models for the prediction and forecasting of water resources variables is undertaken in terms of the modelling process adopted. In all but two of the papers reviewed, feedforward networks are used. The vast majority of these networks are trained using the backpropagation algorithm. Issues in relation to the optimal division of the available data, data pre-processing and the choice of appropriate model inputs are seldom considered. In addition, the process of choosing appropriate stopping criteria and optimising network geometry and internal network parameters is generally described poorly or carried out inadequately. All of the above factors can result in non-optimal model performance and an inability to draw meaningful comparisons between different models. Future research efforts should be directed towards the development of guidelines which assist with the development of ANN models and the choice of when ANNs should be used in preference to alternative approaches, the assessment of methods for extracting the knowledge that is contained in the connection weights of trained ANNs and the incorporation of uncertainty into ANN models.

Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications

Communities of microscopic plant life, or phytoplankton, dominate the Earth's aquatic ecosystems. This important new book by Colin Reynolds covers the adaptations, physiology and population dynamics of phytoplankton communities in lakes and rivers and oceans. It provides basic information on composition, morphology and physiology of the main phyletic groups represented in marine and freshwater systems and in addition reviews recent advances in community ecology, developing an appreciation of assembly processes, co-existence and competition, disturbance and diversity. Although focussed on one group of organisms, the book develops many concepts relevant to ecology in the broadest sense, and as such will appeal to graduate students and researchers in ecology, limnology and oceanography.

/pdf/the-ecology-of-phytoplankton-4ev70n3jsq.pdf

The Ecology of Phytoplankton

Review and comparison of methods to study the contribution of variables in artificial neural network models

Artificial neural networks as a tool in ecological modelling, an introduction

https://lms.ctl.cyut.edu.tw/sys/read_attach.php?id=2718322

An ANN application for water quality forecasting.

http://secure.tutorsglobe.com/Atten_files/54_325534_2_Artificial-neural-network-approach-for-modelling-and-prediction.pdf

Artificial neural network approach for modelling and prediction of algal blooms

Novel application of a back-propagation artificial neural network model formulated to predict algal bloom

Development of a water temperature-ecological model to stimulate global warming effects on lake ecosystem

Effects of nutrients released from suspended solids (SS) discharged by urban runoff on eutrophication in enclosed water body is not well known. In order to estimate the effects, amounts of nitrogen (N) and phosphorus (P) released from SS with decomposition were measured. Bottom sediment and periphyton in an urban river and sediment on road were collected from urban area as postulated SS discharged by urban runoff. The amounts of N and P released from SS measured by the repeated batch experiment were larger than those measured by batch experiment. This result showed adsorption and desorption equilibrium of nutrients between SS and ambient water was important for estimating the amounts of N and P released from SS. The amounts of released N and P were related to the initial amount of organic matter in SS. The percentage of released amounts to initial amounts of N and P in SS were rather small : 5-30% for N and 0-40% for P in 30 days. This result showed nutrients loads expressed by T-N and T-P parameters would be overestimated from the viewpoint of nutrients availability. Characterization of extractable P in SS before and after decomposition experiment showed that the fraction of organic P was released easier than the fractions of inorganic P, e.g., Fe-P and Al-P.

Ken-ichi Yabunaka

Papers

Artificial neural network approach for modelling and prediction of algal blooms

Novel application of a back-propagation artificial neural network model formulated to predict algal bloom

Development of a water temperature-ecological model to stimulate global warming effects on lake ecosystem

Nutrients Release from Suspended Solids Discharged by Urban Runoff.