Journal ArticleDOI
River flow forecasting using recurrent neural networks
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Recurrent neural networks were used to train and forecast the monthly flows of a river in India, with a catchment area of 5189 km2 up to the gauging site, and performed better than the feed forward networks.Abstract:
Forecasting a hydrologic time series has been one of the most complicated tasks owing to the wide range of data, the uncertainties in the parameters influencing the time series and also due to the non availability of adequate data. Recently, Artificial Neural Networks (ANNs) have become quite popular in time series forecasting in various fields. This paper demonstrates the use of ANNs to forecast monthly river flows. Two different networks, namely the feed forward network and the recurrent neural network, have been chosen. The feed forward network is trained using the conventional back propagation algorithm with many improvements and the recurrent neural network is trained using the method of ordered partial derivatives. The selection of architecture and the training procedure for both the networks are presented. The selected ANN models were used to train and forecast the monthly flows of a river in India, with a catchment area of 5189 km2 up to the gauging site. The trained networks are used for both single step ahead and multiple step ahead forecasting. A comparative study of both networks indicates that the recurrent neural networks performed better than the feed forward networks. In addition, the size of the architecture and the training time required were less for the recurrent neural networks. The recurrent neural network gave better results for both single step ahead and multiple step ahead forecasting. Hence recurrent neural networks are recommended as a tool for river flow forecasting.read more
Citations
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Journal ArticleDOI
Review: Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions
TL;DR: Despite a significant amount of research activity on the use of ANNs for prediction and forecasting of water resources variables in river systems, little of this is focused on methodological issues and there is still a need for the development of robust ANN model development approaches.
Journal ArticleDOI
Rainfall-runoff modelling using Long Short-Term Memory (LSTM) networks
TL;DR: In this paper, a novel data-driven approach, using the Long Short-Term Memory (LSTM) network, a special type of recurrent neural network, was proposed for modeling storage effects in e.g. catchments with snow influence.
Journal ArticleDOI
Drought forecasting using feed-forward recursive neural network
Ashok K. Mishra,V. R. Desai +1 more
TL;DR: In this paper, the results obtained from three models and their potential to forecast drought over different lead times are presented in the Kansabati River Basin, which lies in the Purulia district of West Bengal, India.
Journal ArticleDOI
A Rainfall-Runoff Model With LSTM-Based Sequence-to-Sequence Learning
TL;DR: The LSTM‐seq2seq model shows sufficient predictive power and could be used to improve forecast accuracy in short‐term flood forecast applications and the seq2seq method was demonstrated to be an effective method for time series predictions in hydrology.
Journal ArticleDOI
Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting
Robert J. Abrahart,François Anctil,Paulin Coulibaly,Christian W. Dawson,Nick J. Mount,Linda See,Asaad Y. Shamseldin,Dimitri Solomatine,Elena Toth,Robert L. Wilby +9 more
TL;DR: The field is now firmly established and the research community involved has much to offer hydrological science, but it will be necessary to converge on more objective and consistent protocols for selecting and treating inputs prior to model development; extracting physically meaningful insights from each proposed solution; and improving transparency in the benchmarking and reporting of experimental case studies.
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