Evaluation of total load sediment transport formulas using ANN

Yang, Chih Ted; Marsooli, Reza; Aalami, Mohammad Taghi

doi:10.1016/s1001-6279(10)60003-0

Cited by 81 publications

(30 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This study demonstrates a successful application of the ANN modeling concept to total load sediment transport. Despite having five index parameters, the conclusion that only eight parameters are required to predict total load, is in agreement with previous works [24]. The value of the ANN approach is that the nonlinear function need not be the same for all fluvial environments.…”

Section: Discussionsupporting

confidence: 90%

“…He showed that the estimates obtained by the ANNs were significantly superior to the corresponding classical sediment rating curve. Yang et al [24] used ANN to evaluate total sediment load formulae (where-river and location). Nagy [21] estimated that the natural sediment discharge in rivers in terms of sediment concentration by ANN model yields better results compared to several sediment-transport formulas [3,15,20,23].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Prediction of total bed material load for rivers in Malaysia: A case study of Langat, Muda and Kurau Rivers

et al. 2010

View full text Add to dashboard Cite

A soft computational technique is applied to predict sediment loads in three Malaysian rivers. The feed forward-back propagated (schemes) artificial neural network (ANNs) architecture is employed without any restriction to an extensive database compiled from measurements in Langat, Muda, Kurau different rivers. The ANN method demonstrated a superior performance compared to other traditional sediment-load methods. The coefficient of determination, 0.958 and the mean square error 0.0698 of the ANN method are higher than those of the traditional method. The performance of the ANN method demonstrates its predictive capability and the possibility of generalization of the modeling to nonlinear problems for river engineering applications.

show abstract

Section: Discussionsupporting

confidence: 90%

mentioning

confidence: 99%

Prediction of total bed material load for rivers in Malaysia: A case study of Langat, Muda and Kurau Rivers

et al. 2010

View full text Add to dashboard Cite

show abstract

“…For practical problems, using an easy method, which is usable for different cases, is more acceptable than sophisticated methods. In summary, Qnet is professional user friendly software and it has been used for simulating different complex problems (Kuo et al 2004, Yang et al 2009) and that is why it was used in this research. The performance of three soft computing techniques, namely GEP, ANFIS and ANN to predict the hourly water temperature at YYL at various measured depths was compared.…”

Section: General Overview Of Artificial Neural Network (Anns)mentioning

confidence: 99%

Water temperature prediction in a subtropical subalpine lake using soft computing techniques

Samadianfard¹,

Kazemi²,

Kisi³

et al. 2016

Earth sci. res. j.

View full text Add to dashboard Cite

Lake water temperature is one of the key parameters in determining the ecological conditions within a lake, as it influences both chemical and biological processes. Therefore, accurate prediction of water temperature is crucially important for lake management. In this paper, the performance of soft computing techniques including gene expression programming (GEP), which is a variant of genetic programming (GP), adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANNs) to predict hourly water temperature at a buoy station in the Yuan-Yang Lake (YYL) in north-central Taiwan at various measured depths was evaluated. To evaluate the performance of the soft computing techniques, three different statistical indicators were used, including the root mean squared error (RMSE), the mean absolute error (MAE), and the coefficient of correlation (R). Results showed that the GEP had the best performances among other studied methods in the prediction of hourly water temperature at 0, 2 and 3 meter depths below water surface, but there was a different trend in the 1 meter depth below water surface. In this depth, the ANN had better accuracy than the GEP and ANFIS. Despite the error (RMSE value) is smaller in ANN than GEP, there is an upper bound in scatter plot of ANN that imposes a constant value, which is not suitable for predictive purposes. As a conclusion, results from the current study demonstrated that GEP provided moderately reasonable trends for the prediction of hourly water temperature in different depths.La temperatura del agua es uno de los parámetros básicos para determinar las condiciones ecológicas de un lago, ya que está influenciada por procesos químicos y biológicos. Además, la exactitud en la predicción de la temperatura del agua es esencial para el manejo del lago. En este artículo se evalúa el desempeño de técnicas de soft computing como la Programación de Expresiones de Genes (PEG), que es una variante de la Programación Genética (PG), el Sistema Neuro-fuzzy de Inferencia Adaptativa (Anfis, en inglés) y las Redes Neuronales Artificiales (RNA) para predecir la temperatura del agua en diferentes niveles de una estación flotante del lago Yuan-Yang (YYL), en el centro-norte de Taiwán. Se utilizaron tres indicadores estadísticos, el Error Cuadrático Medio (ECM), el Error Absoluto Medio (MAE, en inglés) y el Coeficiente de Correlación (R) para evaluar el desempeño de las técnicas de computación. Los resultados muestran que la PEG es más exacta en la predicción de la temperatura del agua entre 0,2 y 3 metros de profundidad. Sin embargo, se evidencia una tendencia diferente a partir del metro de profundidad. A esta distancia de la superficie, las RNA son más exactas que la PEG y el Anfis. Los resultados de este estudio probaron claramente la usabilidad del PEG y las RNA en la predicción de la temperatura del agua a diferentes profundidades. Water temperature prediction in a subtropical subalpine lake using soft computing techniques Record ABSTRACT RESUMEN

show abstract

“…Ustanovili su da je najpogodnija metoda ANFIS te da se istom metodom može na zadovoljavajući način simulirati pojava histereze. Yang i dr. [25] analizirali su predviđanje ukupnog pronosa nanosa pomoću modela ANN. Za izučavanje modela ANN korišteni su podaci o prosječnoj brzini toka, nagibima vodne površine, prosječnoj dubini toka i srednjem promjeru čestica.…”

Section: Uvodunclassified

Procjena točnosti ANFIS-a u prognoziranju lebdećeg nanosa

2015

JCE

View full text Add to dashboard Cite

Procjena točnosti ANFIS-a u prognoziranju lebdećeg nanosa U radu se istražuju mogućnosti koje pruža prilagodljivi sustav neizrazitog zaključivanja zasnovanog na neuronskoj mreži (ANFIS) u predviđanju dnevnih količina lebdećeg nanosa koje je obavljeno na četiri stanice u SAD-u. U tu su svrhu izrađeni modeli s različitim kombinacijama ulaznih podataka kao osnova za određivanje najbolje moguće strukture. Dobiveni rezultati pokazuju da se najbolji model ANFIS ponaša bolje od modela SRC s obzirom na dobivene vrijednosti RMSE, MBE i R2. Rezultati također pokazuju da se pomoću modela ANFIS može pojednostavniti modeliranje nelinearne dinamike složenih sustava. Assessing ANFIS accuracy in estimation of suspended sediments Capabilities offered by an adaptive neuro-fuzzy inference system (ANFIS) in the estimation of daily sediment loads at four stations in the USA, are explored in the paper. For this purpose, models with various input combinations of data sets were constructed to enable identification of the best possible structure. The results show that the best ANFIS model exhibits better performance compared to the SRC model, in terms of the RMSE, MBE and R2 values. The results also indicate that the ANFIS model can be applied to facilitate modelling of nonlinear dynamics of complex systems.

show abstract

Evaluation of total load sediment transport formulas using ANN

Cited by 81 publications

References 12 publications

Prediction of total bed material load for rivers in Malaysia: A case study of Langat, Muda and Kurau Rivers

Prediction of total bed material load for rivers in Malaysia: A case study of Langat, Muda and Kurau Rivers

Water temperature prediction in a subtropical subalpine lake using soft computing techniques

Procjena točnosti ANFIS-a u prognoziranju lebdećeg nanosa

Contact Info

Product

Resources

About