Journal ArticleDOI
Artificial Neural Networks in Hydrology. II: Hydrologic Applications
TLDR
The role of ANNs in various branches of hydrology has been examined here and it is suggested that ANNs should be considered as a “bridge network” to other types of neural networks.Abstract:
This paper forms the second part of the series on application of artificial neural networks (ANNs) in hydrology. The role of ANNs in various branches of hydrology has been examined here. It is foun...read more
Citations
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Journal ArticleDOI
Mathematical modeling of watershed hydrology.
TL;DR: In this article, a historical perspective of watershed hydrology modeling is provided, and new developments and challenges in watershed models are discussed, while model validation, error propagation, and analyses of uncertainty, risk, and reliability have not been treated as thoroughly.
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
Drought modeling-A review
Ashok K. Mishra,Vijay P. Singh +1 more
TL;DR: In this paper, Mishra et al. reviewed different methodologies used for drought modeling, which include drought forecasting, probability based modeling, spatio-temporal analysis, use of Global Climate Models (GCMs) for drought scenarios, land data assimilation systems for drought modelling, and drought planning.
Journal ArticleDOI
A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series
TL;DR: Developing a hydrological forecasting model based on past records is crucial to develop a water quality forecasting model that can be applied to the Yangtze River basin.
A comparison of performance of several artificial intelligence
TL;DR: Lin et al. as discussed by the authors developed a hydrological forecasting model based on past records, which is crucial to developing a water forecasting model. But the model is not suitable for forecasting the future.
References
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Journal ArticleDOI
Artificial Neural Network Modeling of the Rainfall‐Runoff Process
TL;DR: In this paper, the authors presented a new procedure (entitled linear least squares simplex, or LLSSIM) for identifying the structure and parameters of three-layer feed forward ANN models and demonstrated the potential of such models for simulating the nonlinear hydrologic behavior of watersheds.
Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR)
TL;DR: In this paper, an adaptive Artificial Neural Network (ANN) model that estimates rainfall rates using infrared satellite imagery and ground-surface information is proposed for both the Japanese Islands and the Florida peninsula using GOES-8 and NEXRAD data.
Journal ArticleDOI
Knowledge-based artificial neural networks
TL;DR: These tests show that the networks created by KBANN generalize better than a wide variety of learning systems, as well as several techniques proposed by biologists.
Journal ArticleDOI
Precipitation estimation from remotely sensed information using artificial neural networks
TL;DR: In this article, an adaptive Artificial Neural Network (ANN) model that estimates rainfall rates using infrared satellite imagery and ground-surface information is proposed for both the Japanese Islands and the Florida peninsula using GOES-8 and NEXRAD data.
Journal ArticleDOI
Neural Networks for River Flow Prediction
TL;DR: This paper demonstrates how a neural network can be used as an adaptive model synthesizer as well as a predictor in the flow prediction of the Huron River at the Dexter sampling station, near Ann Arbor, Mich.
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Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications
Holger R. Maier,Graeme C. Dandy +1 more