Neural Networks for River Flow Prediction

Karunanithi, N.; Grenney, William J.; Whitley, Darrell; Bovee, Ken D.

doi:10.1061/(asce)0887-3801(1994)8:2(201)

Cited by 585 publications

(243 citation statements)

References 7 publications

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“…ANN showed a strong capability in handling diversity of problems including rainfall-runoff, water quality, sedimentation and rainfall forecasting. It has been also an efficient and experimented model widely used in number of applications [7,8] such as the sales prediction [9] , shift failures [10] , estimating prices [11] and stock returns [12] .…”

Section: Introductionmentioning

confidence: 99%

Forecasting River Flow in the USA: A Comparison between Auto-Regression and Neural Network Non-Parametric Models

Baareh¹,

Sheta²,

Khnaifes³

2006

J. of Computer Science

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Forecasting a time series became one of the most challenging tasks to variety of data sets. The existence of large number of parameters to be estimated and the effect of uncertainty and outliers in the measurements makes the time series modeling too complicated. Recently, Artificial Neural Network (ANN) became quite successful tool to handle time series modeling problem. This paper provides a solution to the forecasting problem of the river flow for two well known Rivers in the USA. They are the Black Water River and the Gila River. The selected ANN models were used to train and forecast the daily flows of the first station no: 02047500, for the Black Water River near Dendron in Virginia and the second station no: 0944200 for the Gila River near Clifton in Arizona. The feed forward network is trained using the conventional back propagation learning algorithm with many variations in the NN inputs. We explored models built using various historical data. The selection process of various architectures and training data sets for the proposed NN models are presented. A comparative study of both ANN and the conventional Auto-Regression (AR) model networks indicates that the artificial neural networks performed better than the AR model. Hence, we recommend ANN as a useful tool for river flow forecasting.

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Section: Introductionmentioning

confidence: 99%

Forecasting River Flow in the USA: A Comparison between Auto-Regression and Neural Network Non-Parametric Models

Baareh¹,

Sheta²,

Khnaifes³

2006

J. of Computer Science

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“…(ii) The root mean square error (RMSE) is a measure of overall performance across the entire range of the dataset and provides a good measure of model performance for high flows [KARUNANITHI et al 1994], as it is sensitive to small differences in model performance and exhibits high sensitivity to the larger errors occurring for higher magnitudes. It is expressed as:…”

Section: Performance Indicesmentioning

confidence: 99%

Water demand forecasting using extreme learning machines

Tiwari¹,

Adamowski²,

Adamowski³

2016

Journal of Water and Land Development

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The capacity of recently-developed extreme learning machine (ELM) modelling approaches in forecasting daily urban water demand from limited data, alone or in concert with wavelet analysis (W) or bootstrap (B) methods (i.e., ELM, ELM W , ELM B ), was assessed, and compared to that of equivalent traditional artificial neural network-based models (i.e., ANN, ANN W , ANN B ). The urban water demand forecasting models were developed using 3-year water demand and climate datasets for the city of Calgary, Alberta, Canada. While the hybrid ELM B and ANN B models provided satisfactory 1-day lead-time forecasts of similar accuracy, the ANN W and ELM W models provided greater accuracy, with the ELM W model outperforming the ANN W model. Significant improvement in peak urban water demand prediction was only achieved with the ELM W model. The superiority of the ELM W model over both the ANN W or ANN B models demonstrated the significant role of wavelet transformation in improving the overall performance of the urban water demand model.

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“…비선형 시계열 모델은 일반적으로 비선형 학습 알 고리즘을 이용하여 구성되고 비교적 최근 개발 및 적용 연구가 진행되고 있다. 가장 대표적인 모델은 인간의 뇌 구조를 모사하여 고안된 인공신경망(artificial neural network, ANN) 모델로 수자원 변수와 관련하여 초기에 주로 지표수를 대상으로 연구가 진행되었고 (French et al, 1992;Karunanithi et al, 1994;Zealand et al, 1999;Hu et al, 2005) 지하수 분야에 대한 적용 연구로 확대 되고 있다 (Coulibaly et al, 2001;Mohanty et al, 2010). 최근 인공신경망과 퍼지 로직을 결합하여 입력 설 정에 있어 설명적 요소를 고려할 수 있게 하는 적응형 뉴 로 퍼지 추론 시스템(adaptive neuro fuzzy interference system)이 고안되었고 (Jang, 1993) 수자원 문제에 대한 적용성 평가 연구가 진행되고 있다 (Hong and White, 2009;Kisi and Shiri, 2012).…”

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A Comparative Study on Forecasting Groundwater Level Fluctuations of National Groundwater Monitoring Networks using TFNM, ANN, and ANFIS

Yoon¹,

Yoon²,

Kim³

et al. 2014

Journal of Soil and Groundwater Environment

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It is important to predict the groundwater level fluctuation for effective management of groundwater monitoring system and groundwater resources. In the present study, three different time series models for the prediction of groundwater level in response to rainfall were built, those are transfer function noise model (TFNM), artificial neural network (ANN), and adaptive neuro fuzzy interference system (ANFIS). The models were applied to time series data of Boen, Cheolsan, and Hongcheon stations in National Groundwater Monitoring Network. The result shows that the model performance of ANN and ANFIS was higher than that of TFNM for the present case study. As lead time increased, prediction accuracy decreased with underestimation of peak values. The performance of the three models at Boen station was worst especially for TFNM, where the correlation between rainfall and groundwater data was lowest and the groundwater extraction is expected on account of agricultural activities. The sensitivity analysis for the input structure showed that ANFIS was most sensitive to input data combinations. It is expected that the time series model approach and results of the present study are meaningful and useful for the effective management of monitoring stations and groundwater resources.

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Neural Networks for River Flow Prediction

Cited by 585 publications

References 7 publications

Forecasting River Flow in the USA: A Comparison between Auto-Regression and Neural Network Non-Parametric Models

Forecasting River Flow in the USA: A Comparison between Auto-Regression and Neural Network Non-Parametric Models

Water demand forecasting using extreme learning machines

A Comparative Study on Forecasting Groundwater Level Fluctuations of National Groundwater Monitoring Networks using TFNM, ANN, and ANFIS

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