Runoff forecasting by artificial neural network and conventional model

Ghumman, Abdul Razzaq; Ghazaw, Yousry Mahmoud; Sohail, Anabia; Watanabe, Kunio

doi:10.1016/j.aej.2012.01.005

Cited by 82 publications

(33 citation statements)

References 20 publications

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“…The 2 coefficients obtained were 85% and 58% for calibration and validation periods, respectively. In a recent study in Pakistan where a feedforward neural network model was developed to predict the monthly runoff of an arid large watershed (9391 km 2 ) with an annual precipitation of 191 mm [38], the NS values were 0.88 and 0.63 for calibration and validation periods, respectively; these are in fairly good agreement with this study. Clearly, the rainfall-runoff processes were extremely nonlinear.…”

Section: Resultssupporting

confidence: 71%

An Assessment of a Proposed Hybrid Neural Network for Daily Flow Prediction in Arid Climate

Jajarmizadeh

Harun

Salarpour

2014

Modelling and Simulation in Engineering

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Rainfall-runoff simulation in hydrology using artificial intelligence presents the nonlinear relationships using neural networks. In this study, a hybrid network presented as a feedforward modular neural network (FF-MNN) has been developed to predict the daily rainfall-runoff of the Roodan watershed at the southern part of Iran. This FF-MNN has three layers-input, hidden, and output. The hidden layer has two types of neural expert or module. Hydrometeorological data of the catchment were collected for 21 years. Heuristic method was used to develop the MNN for exploring daily flow generalization. Two training algorithms, namely, backpropagation with momentum and Levenberg-Marquardt, were used. Sigmoid and linear transfer functions were employed to explore the network's optimum behavior. Cross-validation and predictive uncertainty assessments were carried out to protect overtiring and overparameterization, respectively. Results showed that the FF-MNN could satisfactorily predict stream flow during testing period. The Nash-Sutcliff coefficient, coefficient of determination, and root mean square error obtained using MNN during training and test periods were 0.85, 0.85, and 39.4 and 0.57, 0.58, and 32.2, respectively. The predictive uncertainties for both periods were 0.39 and 0.44, respectively. Generally, the study showed that the FF-MNN can give promising prediction for rainfall-runoff relations.

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Section: Resultssupporting

confidence: 71%

An Assessment of a Proposed Hybrid Neural Network for Daily Flow Prediction in Arid Climate

Jajarmizadeh

Harun

Salarpour

2014

Modelling and Simulation in Engineering

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“…It was known in early studies as multilayer perceptron, a basic structure with multiple dense layers. In the last few decades, several studies (Chang et al, 2015;Ömer Faruk, 2010;Sajikumar & Thandaveswara, 1999) have reported that the performances of ANN models are comparable to those of physical models, and they work well in areas with limited data (Ghumman et al, 2011). They can run fast because they do not require modeling of the internal structure (Hsu et al, 1995), and they can be more efficient with the standard support of hardware, software, and algorithm parallelization (LeCun et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning

Xiang

Yan²,

Demir

2020

Water Resources Research

467

169

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Rainfall-runoff modeling is a complex nonlinear time series problem. While there is still room for improvement, researchers have been developing physical and machine learning models for decades to predict runoff using rainfall data sets. With the advancement of computational hardware resources and algorithms, deep learning methods such as the long short-term memory (LSTM) model and sequence-to-sequence (seq2seq) modeling have shown a good deal of promise in dealing with time series problems by considering long-term dependencies and multiple outputs. This study presents an application of a prediction model based on LSTM and the seq2seq structure to estimate hourly rainfall-runoff. Focusing on two Midwestern watersheds, namely, Clear Creek and Upper Wapsipinicon River in Iowa, these models were used to predict hourly runoff for a 24-hr period using rainfall observation, rainfall forecast, runoff observation, and empirical monthly evapotranspiration data from all stations in these two watersheds. The models were evaluated using the Nash-Sutcliffe efficiency coefficient, the correlation coefficient, statistical bias, and the normalized root-mean-square error. The results show that the LSTM-seq2seq model outperforms linear regression, Lasso regression, Ridge regression, support vector regression, Gaussian processes regression, and LSTM in all stations from these two watersheds. The LSTM-seq2seq model shows sufficient predictive power and could be used to improve forecast accuracy in short-term flood forecast applications. In addition, the seq2seq method was demonstrated to be an effective method for time series predictions in hydrology. Key Points:• An hourly runoff model was developed using the LSTM sequence-to-sequence learning method for 24-hr predictions on USGS stations • The proposed model shows better performance than traditional data-driven models and is applicable to different watersheds • The advantages and limitations of seq2seq models and how this model structure could work on the rainfall-runoff modeling is presented Supporting Information:• Supporting Information S1 Correspondence to: Z. Xiang, zhongrun-xiang@uiowa.edu Citation: Xiang, Z., Yan, J., & Demir, I. (2020). A rainfall-runoff model with LSTM-based sequence-to-sequence learning. Water Resources Research, 56, e2019WR025326. https://doi.

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“…Usually, the failure to capture flood value could possibly be overcome by having a longer data set for training. According to Ghumman et al (2011), short data sets and poor data quality are common problems in generalization of neural networks. In this study, the result of flood prediction is promising in annually scale according statistical analysis and graphical presentation.…”

Section: Resultsmentioning

confidence: 99%

Development of Generalized Feed Forward Network for Predicting Annual Flood (depth) of a Tropical River

Salarpour¹,

Yusop²,

Jajarmizadeh³

et al. 2014

JSM

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The modeling of rainfall-runoff relationship in a watershed is very important in designing hydraulic structures, controlling flood and managing storm water. Artificial Neural Networks (ANNs) are known as having the ability to model nonlinear mechanisms. This study aimed at developing a Generalized Feed Forward (GFF) network model for predicting annual flood (depth) of Johor River in Peninsular Malaysia. In order to avoid over training, cross-validation technique was performed for optimizing the model. In addition, predictive uncertainty index was used to protect of over parameterization. The governing training algorithm was back propagation with momentum term and tangent hyperbolic types was used as transfer function for hidden and output layers. The results showed that the optimum architecture was derived by linear tangent hyperbolic transfer function for both hidden and output layers. The values of Nash and Sutcliffe (NS) and Root mean square error (RMSE) obtained 0.98 and 5.92 for the test period. Cross validation evaluation showed 9 process elements is adequate in hidden layer for optimum generalization by considering the predictive uncertainty index obtained (0.14) for test period which is acceptable. ABSTRAKPemodelan hubungan curahan hujan-aliran air di suatu kawasan tadahan adalah sangat penting dalam mereka bentuk struktur hidraulik, mengawal banjir dan menguruskan air ribut. Rangkaian neural tiruan (ANNs) dikenal pasti mempunyai keupayaan untuk memperaga mekanisme tak linear. Kajian ini bertujuan untuk membangunkan model rangkaian suapan ke hadapan menyeluruh (GFF) untuk meramalkan banjir tahunan (kedalaman) Sungai Johor di Semenanjung Malaysia. Untuk mengelakkan latihan berlebihan, teknik pengesahan silang telah dijalankan bagi mengoptimumkan model tersebut. Di samping itu, indeks ketidakpastian ramalan digunakan untuk melindungi daripada pemparameteran berlebihan. Algoritma latihan pentadbiran adalah perambatan balik terma momentum dan jenis tangen hiperbolik digunakan sebagai fungsi perpindahan bagi lapisan tersembunyi dan output. Hasil kajian menunjukkan bahawa seni bina yang optimum diperoleh melalui fungsi perpindahan linear tangen hiperbolik bagi lapisan tersembunyi dan output. Nilai Nash dan Sutcliffe (NS) serta punca min ralat kuasa dua (RMSE) memperoleh 0.98 dan 5.92 bagi masa ujian. Penilaian pengesahan silang menunjukkan 9 proses elemen adalah mencukupi dalam lapisan tersembunyi untuk pengitlakan yang optimum dengan mengambil kira ramalan indeks ketidaktentuan yang diperoleh (0.14) dalam masa ujian adalah diterima.Kata kunci: Banjir tahunan; ketidakpastian ramalan; pengesahan silang; rangkaian neural tiruan; suapan ke hadapan menyeluruh; Sungai Johor

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Runoff forecasting by artificial neural network and conventional model

Cited by 82 publications

References 20 publications

An Assessment of a Proposed Hybrid Neural Network for Daily Flow Prediction in Arid Climate

An Assessment of a Proposed Hybrid Neural Network for Daily Flow Prediction in Arid Climate

A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning

Development of Generalized Feed Forward Network for Predicting Annual Flood (depth) of a Tropical River

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