Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
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Rise in nutrients concentrations have been a persistent problem in streams and rivers throughout the world. Estimates of nutrient fluxes are necessity as well as challenges for water quality management. The observation of nutrient loads (of nitrogen and/or phosphorous) from watershed into river or stream system is not straight forward but complex function of hydrology, geology, and land use of the region. There are statistical approaches to predict the nutrients loads in rivers. Development of models based on temporal observations may improve understanding the underlying hydrological processes complex phenomena of nutrient concentrations in river. Present work utilized temporal patterns extracted from temporal observations of monthly flow data and nitrogen loads using wavelet theory. These patterns are then utilized by an artificial neural network (ANN). The wavelet-ANN conjunction model is then able to predict the monthly nutrient load.