Hybrid neural network models for hydrologic time series forecasting

By Ashu Jain, Avadhnam Madhav Kumar. Applied Soft Computing, 2007

460 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Environmental Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/987a0c3eb495f712e3ec493631b9ff07a95b4113

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In Electrical and Electronic Engineering

In Environmental Engineering

In Management Science and Operations Research

Citations in the corpus

Performance of Combined Double Seasonal Univariate Time Series Models for Forecasting Water Demand (2009, unknown): “Jain and Kumar (2007) and Coulibary and Baldwin (2005) employed arti…cial neural networks methods for hydrological time series forecasting.”
Temporal pattern attention for multivariate time series forecasting (2018, methodology): “…began with using naive RNN [J. Connor and Martin(1991)], improved with hybrid models that combined ARIMA and multilayer perceptrons [G. Zhang and Hu(1998), Zhang(2003), Jain and Kumar(2007)], and then most recently progressed to dynamic Boltzmann machines with RNN [Dasgupta and Os-ogami(2017)].”
A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile (2008, methodology): “This hybrid ARIMA–ANN approach has recently been used for tourist arrival forecasting (Aslanargun et al., 2007), hydrology (Jain and Kumar, 2007), supply chain management (Aburto and Weber, 2007), freshwater phytoplankton dynamics (Jeong et al., 2008), watersheds (Pulido-Calvo and Portela, 2007),…”
Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks (2017, methodology): “Efforts in this direction range from the early work using naive RNN models [7] and the hybrid models [16, 35, 36] combining the use of ARIMA [3] and Multilayer Perceptron (MLP), to the recent combination of vanilla RNN and Dynamic Boltzmann Machines in time series forecasting [8].”
An artificial neural network (p, d, q) model for timeseries forecasting (2010, unknown): “Artificial neural networks have been found to be a viable contender to various traditional time series models (Chen, Yang, Dong, & Abraham, 2005; Giordano, La Rocca, & Perna, 2007; Jain & Kumar, 2007).”
Predicting Water Level Fluctuations in Lake Michigan-Huron Using Wavelet-Expert System Methods (2014, background): “Since then, ANNs have enjoyed their popularity in many applications including hydrological processes (Abrahart et al. 2004; Altunkaynak 2007; Altunkaynak and Strom 2009; Alvisi et al. 2006; ASCE Committee 2000; Bardossy et al. 2006; Campolo et al. 1999; Dawson and Wilby 2001; Daliakopoulos et al. 20”
Improvement of Auto-Regressive Integrated Moving Average models using Fuzzy logic and Artificial Neural Networks (ANNs) (2009, background): “They have been found to be a viable contender to various traditional time series models [12,18,26].”
Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition (2019, background): “14, RBFNs has diverse uses such as function approximation (FA) [197], prediction [198], classification [199], control [200] etc.”