Evaluation of a physically based quasi-linear and a conceptually based nonlinear Muskingum methods

Perumal, Muthiah; Tayfur, Gökmen; Chintalacheruvu, Madhusudana Rao; Gürarslan, Gürhan

doi:10.1016/j.jhydrol.2017.01.025

Cited by 28 publications

(32 citation statements)

References 32 publications

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“…Considering the duration of these events (see Table 1), the total of data sets used in the testing stage was 740. Figure 4 shows the predicted hydrographs, where the performance of ANN was tested against that of the rating curve method (RCM) developed by Moramarco et al [24] who proposed the relation given by Equation (12):…”

Section: Real Hydrograph Predictionsmentioning

confidence: 99%

“…Their performance is as good as that of the St. Venant model. When Perumal et al [12] investigated the performance of some of the machine learning algorithms against that of the VPMM using a compound channel reach of 40 km, they calibrated the NMM model parameters separately for each river reach having different bed slopes. Herein, a single set of parameters was obtained for the same river reach having different bed slopes.…”

Section: Hydrograph Predictions In An Artificial Channel Reachmentioning

confidence: 99%

“…Flood hydrograph predictions are performed using several soft computing methods, such as the artificial neural network (ANN) [8], the genetic algorithm (GA) [9,10], and the Fuzzy logic methods [11]. Most recently, the performance of some of the machine learning algorithms against the Variable Parameter Muskingum Model (VPMM) was compared [12]. In this context, it is of considerable interest to investigate how well the machine learning methods work in the real field and this study attempts to answer to the demand presenting real flood hydrograph predictions by the ANN, the GA, the ACO, and the PSO and compares the performances against those of the RCM.…”

Section: Introductionmentioning

confidence: 99%

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Flood Hydrograph Prediction Using Machine Learning Methods

Tayfur

Singh

Moramarco

et al. 2018

Water

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Machine learning (soft) methods have a wide range of applications in many disciplines, including hydrology. The first application of these methods in hydrology started in the 1990s and have since been extensively employed. Flood hydrograph prediction is important in hydrology and is generally done using linear or nonlinear Muskingum (NLM) methods or the numerical solutions of St. Venant (SV) flow equations or their simplified forms. However, soft computing methods are also utilized. This study discusses the application of the artificial neural network (ANN), the genetic algorithm (GA), the ant colony optimization (ACO), and the particle swarm optimization (PSO) methods for flood hydrograph predictions. Flow field data recorded on an equipped reach of Tiber River, central Italy, are used for training the ANN and to find the optimal values of the parameters of the rating curve method (RCM) by the GA, ACO, and PSO methods. Real hydrographs are satisfactorily predicted by the methods with an error in peak discharge and time to peak not exceeding, on average, 4% and 1%, respectively. In addition, the parameters of the Nonlinear Muskingum Model (NMM) are optimized by the same methods for flood routing in an artificial channel. Flood hydrographs generated by the NMM are compared against those obtained by the numerical solutions of the St. Venant equations. Results reveal that the machine learning models (ANN, GA, ACO, and PSO) are powerful tools and can be gainfully employed for flood hydrograph prediction. They use less and easily measurable data and have no significant parameter estimation problem.

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Section: Real Hydrograph Predictionsmentioning

confidence: 99%

Section: Hydrograph Predictions In An Artificial Channel Reachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Flood Hydrograph Prediction Using Machine Learning Methods

Tayfur

Singh

Moramarco

et al. 2018

Water

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“…(7) Add the mark of having been processed to the statement in allLiaisonSentence. (8) If all the statements in allLiaisonSentence are processed, turn to step (9). Otherwise, turn to step (3).…”

Section: Mobile Information Systemsmentioning

confidence: 99%

Spoken English Assessment Using Confused Phoneme Assessment Model

Jiang

2021

Mobile Information Systems

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Under normal circumstances, in the process of evaluating the phoneme of spoken English which is easy to be confused, it is impossible to accurately assess the oral ability of different groups of people according to their oral ability. The assessment process has problems of poor robustness and low stability. We propose a spoken English assessment method based on an easily confused phoneme assessment model to address these problems. We design an English easily confused phoneme-based evaluation model in the proposed framework by adopting fuzzy logic for the assessment task. We also present HPD set for confused phonemes and introduce the easily confused phonemes in spoken English. Moreover, we derive four fuzzy measure assessment grades of E/G/NI/GR and present the assessment model for them. We continuously recognize and annotate spoken English to find the best-matched statement and complete the recognition and assessment of easily confused phoneme. Then, we also focus on spoken English assessment based on an easily confused phoneme assessment model. Empirical results demonstrate the superior performance of our proposed models over the conventional evaluation methods. Our proposed models improve the spoken English assessment method by 30% and the stability by 45%. Besides, our model is also suitable for the spoken English assessment of different groups of people.

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“…These can be further classified as hydraulic and hydrologic routing methods. The hydrologic routing methods are widely used in the field practices since early thirties and they have been developed essentially to overcome the tedious computations involved in the hydraulic routing methods (Perumal et al, 2017). Among the many lumped hydrological routing methods, the Muskingum method introduced by McCarthy (1938) is well known in the literature (Chow et al, 1988).…”

Section: Introductionmentioning

confidence: 99%

River discharge simulation using variable parameter McCarthy–Muskingum and wavelet-support vector machine methods

Yadav

Mathur

2018

Neural Comput & Applic

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In this study an extended version of variable parameter McCarthy-Muskingum (VPMM) method originally proposed by Perumal and Price (2013) was compared with the widely used data based model namely support vector machine (SVM) and hybrid wavelet-support vector machine (WA-SVM) to simulate the hourly discharge in Neckar River wherein significant lateral flow contribution by intermediate catchment rainfall prevails during flood wave movement. The discharge data from the year 1999 to 2002 has been used in this study. The extended VPMM method has been used to simulate nine flood events of the year 2002 and later the results were compared with SVM and WA-SVM models. The analysis of statistical and graphical results suggest that the extended VPMM method was able to predict the flood wave movement better than the SVM and WA-SVM models. A model complexity analysis was also conducted which suggest that the two parameter based extended VPMM method has less complexity than the three parameters based SVM and WA-SVM model. Further, the model selection criteria also gives the highest values for VPMM in 7 out of 9 flood events. The simulation of flood events suggested that both the approaches were able to capture the underlying physics and reproduced the target value close to the observed hydrograph. However, the VPMM models is slightly more efficient and accurate, than the SVM and WA-SVM model which are based only on the antecedent discharge data. The study captures the

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Evaluation of a physically based quasi-linear and a conceptually based nonlinear Muskingum methods

Cited by 28 publications

References 32 publications

Flood Hydrograph Prediction Using Machine Learning Methods

Flood Hydrograph Prediction Using Machine Learning Methods

Spoken English Assessment Using Confused Phoneme Assessment Model

River discharge simulation using variable parameter McCarthy–Muskingum and wavelet-support vector machine methods

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