Artificial Neural Networks for Event Based Rainfall-Runoff Modeling

Sarkar, Archana; Kumar, Rakesh

doi:10.4236/jwarp.2012.410105

Cited by 51 publications

(26 citation statements)

References 18 publications

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“…Wu et al (2005) developed the using of ANNs for watershed runoff and river flow simulations. Back propagation (This technique is also sometimes called backward propagation of errors) ANN, runoff models applied by Sarkar et al (2006) to estimate and prediction daily runoff for a part of the Satluj river basin of India. Comparison of different ANN models applied by Kisi (2007) for short term daily river flow estimation.…”

Section: Introductionmentioning

confidence: 99%

River flow simulation using a multilayer perceptron-firefly algorithm model

Darbandi

Pourhosseini

2018

Appl Water Sci

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River flow estimation using records of past time series is importance in water resources engineering and management and is required in hydrologic studies. In the past two decades, the approaches based on the artificial neural networks (ANN) were developed. River flow modeling is a non-linear process and highly affected by the inputs to the modeling. In this study, the best input combination of the models was identified using the Gamma test then MLP-ANN and hybrid multilayer perceptron (MLP-FFA) is used to forecast monthly river flow for a set of time intervals using observed data. The measurements from three gauge at Ajichay watershed, East Azerbaijani, were used to train and test the models approach for the period from January 2004 to July 2016. Calibration and validation were performed within the same period for MLP-ANN and MLP-FFA models after the preparation of the required data. Statistics, the root mean square error and determination coefficient, are used to verify outputs from MLP-ANN to MLP-FFA models. The results show that MLP-FFA model is satisfactory for monthly river flow simulation in study area.

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Section: Introductionmentioning

confidence: 99%

River flow simulation using a multilayer perceptron-firefly algorithm model

Darbandi

Pourhosseini

2018

Appl Water Sci

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“…The developed ANN models have been able to predict this information with great accuracy. This shows that ANNs can be very efficient in modeling an eventbased rainfall-runoff process for determining the peak discharge and time to the peak discharge very accurately [12].…”

Section: Generalmentioning

confidence: 99%

Modeling of Triangular Unit Hydrographs Using an Artificial Neural Network in a Tropical River Basin

Saefulloh¹

2018

GEOMATE

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Rainfall-runoff models are crucial for estimating floods in a river basin. Most watersheds in Indonesia have a data deficiency problem, especially in natural watersheds (ungauged river basins), which may affect the accuracy of design and planning of water resources. Most synthetic unit hydrograph methods are not in accordance with the characteristics of Indonesian watersheds, and adjustments should be made to obtain accurate results. This study aimed to develop a simple triangular unit hydrograph generated by using a neural network for different watersheds in Indonesia. The triangular unit hydrograph consists of the peak discharge, time to peak, and time base developed using a neural network with a learning process from the observed unit hydrograph, and the result will be compared to the Snyder-Alexeyev synthetic unit hydrograph after being adjusted to obtain accurate results in comparison to observed data. An artificial neural network (ANN) model was developed by inputting basin characteristics such as catchment area (A), river length (L), basin slope (S), shape factor (F), and runoff coefficient (C). The model will generate the output of a triangular synthetic unit hydrograph consisting of peak discharge (Qp), time to peak (Tp), and time base (Tb). A case study is discussed in tropical river basins mostly on Java Island, where flood events are frequent. The simulation result from applying an ANN using generalized reduced gradient neural network (GRGNN) methods is significantly in line with historical data. The ANN simulation shows more accurate results than the adjusted Snyder-Alexeyev unit hydrograph. The results indicated that the synthetic unit hydrograph generated by an ANN can be applied to an ungauged river basin.

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“…Tampelini et al [15] presented the application of Elman neural network for rainfallrunoff simulation in Brazil. Sarkar and Kumar [16] modeled the event-based rainfall-runoff process using the ANN technique over the Ajay River basin. Hasanpour Kashani et al [17] evaluated capacity of the ANN [multilayer perceptron (MLP)] and Volterra model to approximate arbitrary non-linear rainfall-runoff processes in north of Iran.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Three Intelligent Techniques for Runoff Simulation

Kashani

Soltangeys

2018

CivileJournal

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In this study, performance of a feedback neural network, Elman, is evaluated for runoff simulation. The model ability is compared with two other intelligent models namely, standalone feedforward Multi-layer Perceptron (MLP) neural network model and hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) model. In this case, daily runoff data during monsoon period in a catchment located at south India were collected. Three statistical criteria, correlation coefficient, coefficient of efficiency and the difference of slope of a best-fit line from observed-estimated scatter plots to 1:1 line, were applied for comparing the performances of the models. The results showed that ANFIS technique provided significant improvement as compared to Elman and MLP models. ANFIS could be an efficient alternative to artificial neural networks, a computationally intensive method, for runoff predictions providing at least comparable accuracy. Comparing two neural networks indicated that, unexpectedly, Elman technique has high ability than MLP, which is a powerful model in simulation of hydrological processes, in runoff modeling.

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Artificial Neural Networks for Event Based Rainfall-Runoff Modeling

Cited by 51 publications

References 18 publications

River flow simulation using a multilayer perceptron-firefly algorithm model

River flow simulation using a multilayer perceptron-firefly algorithm model

Modeling of Triangular Unit Hydrographs Using an Artificial Neural Network in a Tropical River Basin

Comparison of Three Intelligent Techniques for Runoff Simulation

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