State space neural networks for short term rainfall-runoff forecasting

Pan, Tsung-Yi; Wang, Ru-Yih

doi:10.1016/j.jhydrol.2004.04.010

Cited by 51 publications

(27 citation statements)

References 31 publications

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“…For identifying the architecture of an ANN associated with determining the number of neurons in each layer, the trial-and-error approach is still the most common (Imrie et at.,2000;Pan and Wang, 2004;Toth et al 2000). Some software packages perform the trial-and-error optimisation automatically.…”

Section: Ann Architecturementioning

confidence: 99%

A neural network model for short term river flow prediction

Teschl

Randeu

2006

Nat. Hazards Earth Syst. Sci.

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Abstract. This paper presents a model using rain gauge and weather radar data to predict the runoff of a small alpine catchment in Austria. The gapless spatial coverage of the radar is important to detect small convective shower cells, but managing such a huge amount of data is a demanding task for an artificial neural network. The method described here uses statistical analysis to reduce the amount of data and find an appropriate input vector. Based on this analysis, radar measurements (pixels) representing areas requiring approximately the same time to dewater are grouped.

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Section: Ann Architecturementioning

confidence: 99%

A neural network model for short term river flow prediction

Teschl

Randeu

2006

Nat. Hazards Earth Syst. Sci.

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“…The integration can provide not only the flexibility to represent any nonlinear functions but also the parallel inputs/outputs (causes/effects) relationships established between the neural model and the physical system (Pan & Wang, 2004). The presented RNN has five layers: input layer, hidden layer S, state layer, hidden layer O, and output layer.…”

Section: Deterministic Linearized Recurrent Neural Networkmentioning

confidence: 99%

“…Figure 10 shows that the DLRNN in the canonical form is clearly not a fully RNN. 28 validated events are fed to the model, and a new on-line learning method developed by Pan and Wang (2004), is applied to develop the DLRNN into a fully RNN via on-line learning. Table 5.…”

Section: Comparison Between Dlrnns In Different Formsmentioning

confidence: 99%

Application of Recurrent Neural Networks to Rainfall-runoff Processes

Pan¹,

Wang²,

Lai³

et al. 2008

Recurrent Neural Networks

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“…These related works have been categorized under both the hydrological approach [15][16][17] and the statistical approach which mostly applied artificial neural networks (ANNs) such as the back-propagation neural network (BPNN) [18][19][20][21][22], the state space neural network [23], the adaptive network-based fuzzy inference system (ANFIS) [24], the recurrent neural network (RNN) [21], support vector machine [1], and the radial basis function [2] as construction tools. The advantage of the short lead-time forecast is that it is fairly accurate in medium-low reservoir inflow, whereas the disadvantages are that (1) the effective forecasted lead-time is only 6 h; (2) the forecasted error in the high flow periods is high, within the range of 10% to 40% [1,2]; and (3) the time-lag circumstances of the forecasted flow rate of a longer forecasted lead-time are significant.…”

Section: Introductionmentioning

confidence: 99%

Coupled Heuristic Prediction of Long Lead-Time Accumulated Total Inflow of a Reservoir during Typhoons Using Deterministic Recurrent and Fuzzy Inference-Based Neural Network

Huang

Wei

2015

Water

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This study applies Real-Time Recurrent Learning Neural Network (RTRLNN) and Adaptive Network-based Fuzzy Inference System (ANFIS) with novel heuristic techniques to develop an advanced prediction model of accumulated total inflow of a reservoir in order to solve the difficulties of future long lead-time highly varied uncertainty during typhoon attacks while using a real-time forecast. For promoting the temporal-spatial forecasted precision, the following original specialized heuristic inputs were coupled: observed-predicted inflow increase/decrease (OPIID) rate, total precipitation, and duration from current time to the time of maximum precipitation and direct runoff ending (DRE). This study also investigated the temporal-spatial forecasted error feature to assess the feasibility of the developed models, and analyzed the output sensitivity of both single and combined heuristic inputs to determine whether the heuristic model is susceptible to the impact of future forecasted uncertainty/errors. Validation results showed that the long lead-time-predicted accuracy and stability of the RTRLNN-based accumulated total inflow model are better than that of the ANFIS-based model because of the real-time recurrent deterministic routing mechanism of RTRLNN. Simulations show that the RTRLNN-based model with coupled heuristic inputs (RTRLNN-CHI, average error percentage OPEN ACCESSWater 2015, 7 6517(AEP)/average forecast lead-time (AFLT): 6.3%/49 h) can achieve better prediction than the model with non-heuristic inputs (AEP of RTRLNN-NHI and ANFIS-NHI: 15.2%/31.8%) because of the full consideration of real-time hydrological initial/boundary conditions. Besides, the RTRLNN-CHI model can promote the forecasted lead-time above 49 h with less than 10% of AEP which can overcome the previous forecasted limits of 6-h AFLT with above 20%-40% of AEP.

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State space neural networks for short term rainfall-runoff forecasting

Cited by 51 publications

References 31 publications

A neural network model for short term river flow prediction

A neural network model for short term river flow prediction

Application of Recurrent Neural Networks to Rainfall-runoff Processes

Coupled Heuristic Prediction of Long Lead-Time Accumulated Total Inflow of a Reservoir during Typhoons Using Deterministic Recurrent and Fuzzy Inference-Based Neural Network

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