Juan Carlos Gutiérrez-Estrada

TL;DR: The performance of a hybrid methodology combining feed forward CNN, fuzzy logic and genetic algorithm to forecast one-day ahead daily water demands at irrigation districts considering that only flows in previous days are available for the calibration of the models were analysed.

TL;DR: In this article, the performance of computational neural networks (CNNs) models to forecast 1-month ahead monthly anchovy catches in the north area of Chile considering only anchovy catch in previous months as inputs to the models was analyzed.

TL;DR: In this paper, the main problems in the management of large water supply and distribution systems is the forecasting of daily demand in order to schedule pumping effort and minimize costs, which is the main problem in water management.

TL;DR: This study evaluated the performance of feed forward ANN models trained to predict D. acuminata blooms using data from eight stations of the Andalucia HAB monitoring programme between 1998 and 2004 and showed that ANN models with a low number of input variables are able to reproduce trends in D.acuminata population dynamics.

TL;DR: In this article, a real-time approach based on linear multiple regression, univariate time series models (exponential smoothing and autoregressive integrated moving average (ARIMA) models) and computational neural networks (ANNs) is developed to predict the daily average ammonia concentration in rearing tanks with water recirculating.