in this study a machine learning algorithm was applied in order to develop a predictive model for the changes in phytoplankton biomass (chlorophyll a) in the lower nakdong river, south korea. we used a “hybrid evolutionary algorithm (hea)” which generated model consists of three functions ‘if-thenelse’ on the basis of a 15-year, weekly monitored ecological database. we used the average ...

Several ANN models were developed to prediction of monthly precipitation data in Mashhad synoptic station. From the total 636 monthly precipitation data (from 1958 to 2008), 580 data has been used for training networks and the rest selected randomly has been used for validation of the models. To extract the precipitation dynamic of this station by ANN, a new approach of three-layer feed-forward...

Abstract As one of the important hydrological elements rivers, flow is great significance to development and utilization water resources ecological environment. Based on excellent nonlinear processing capability CEEMDAN advantages BILSTM in time-series data modeling, a coupled CEEMDAN-BILSTM model constructed for prediction, i-month flows from 1951 2016 are used predict 2017 2021. The results s...

Linear models are widely used to perform time series forecasting. The Autoregressive stand out, due their simplicity in the parameters adjustment based on close-form solution. and Moving Average (ARMA) Infinite Impulse Response filters (IIR) also good alternatives, since they recurrent structures. However, is more complex, problem has no analytical This investigation performs linear predict mon...

In this paper, we develop and test a theoretical model of multi-market trading to explain the differences in the foreign share of trading volume of internationally cross-listed stocks. The model derives an equilibrium which predicts that, under fairly general conditions, the distribution of trading volume across exchanges competing for order flow is related to the correlation of the cross-liste...