Estimation of scour depth around cross-vane structures using a novel non-tuned high-accuracy machine learning approach

Azimi, Amir Hosein; Shabanlou, Saeid; Yosefvand, Fariborz; Rajabi, Ahmad; Yaghoubi, Behrouz

doi:10.1007/s12046-020-01390-6

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…Azimi et al . (2020) recreated dimensions of scour gaps formed around stone structures with different shapes placed in straight rectangular channels by means of a learning machine. These researchers demonstrated that the Froude number is the most important variable in the formation of scour gaps.…”

Section: Introductionmentioning

confidence: 99%

Stone weir scour modelling in curved canals using a weighted regularized extreme learning machine*

Shahbazbeygi

Yosefvand

Yaghoubi

et al. 2021

Irrigation and Drainage

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In this paper, a novel weighted regularized extreme learning machine (WRELM) was used for the first time to simulate the scour depth around J‐, I‐ and W‐shaped stone weirs in curved canals. In the first step, dimensionless parameters affecting the scour depth around stone weirs were detected. Then, six WRELM models were developed using these parameters. It is worth mentioning that three different experimental models were utilized for verifying the WRELM models. Moreover, 70% of the experimental data were used to train the models and the remaining 30% to test them. After that, the number of neurons existing within the hidden layer was chosen to be 13. Then the best activation function of the WRELM model was selected. The optimal regularization parameter was also found for this activation function. Conducting a comprehensive sensitivity analysis in the next step detected the best WRELM model, as well as the most influencing input variables. The results of the superior model were compared with the regularized extreme learning machine (RELM) and extreme learning machine (ELM) models to prove the noticeable superiority of the WRELM. Furthermore, a formula was proposed to compute the scour gap around stone weirs with different shapes situated in curved canals. Finally, the sensitivity of the input variables on scour values was examined.

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Section: Introductionmentioning

confidence: 99%

Stone weir scour modelling in curved canals using a weighted regularized extreme learning machine*

Shahbazbeygi

Yosefvand

Yaghoubi

et al. 2021

Irrigation and Drainage

Self Cite

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Generalized structure of group method of data handling to prognosticate scour around various cross-vane structures

et al. 2021

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Optimal operation of the dam reservoir in real time based on generalized structure of group method of data handling and optimization technique

Mansouri,

Fathian,

Nikbakht Shahbazi

et al. 2024

Appl Water Sci

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The historical data on water intake into the reservoir is collected and used within the framework of a deterministic optimization method to determine the best operating parameters for the dam. The principles that have been used to extract the best values of the flow release from the dam may no longer be accurate in the coming years when the inflow to dams will be changing, and the results will differ greatly from what was predicted. This represents this method’s main drawback. The objective of this study is to provide a framework that can be used to guarantee that the dam is running as efficiently as possible in real time. Because of the way this structure is created, if the dam’s inflows change in the future, the optimization process does not need to be repeated. In this case, deep learning techniques may be used to restore the ideal values of the dam’s outflow in the shortest amount of time. This is achieved by accounting for the environment’s changing conditions. The water evaluation and planning system simulator model and the MOPSO multi-objective algorithm are combined in this study to derive the reservoir’s optimal flow release parameters. The most effective flow discharge will be made feasible as a result. The generalized structure of the group method of data handling (GSGMDH), which is predicated on the results of the MOPSO algorithm, is then used to build a new model. This model determines the downstream needs and ideal release values from the reservoir in real time by accounting for specific reservoir water budget factors, such as inflows and storage changes in the reservoir. Next, a comparison is drawn between this model’s performance and other machine learning techniques, such as ORELM and SAELM, among others. The results indicate that, when compared to the ORELM and SAELM models, the GSGMDH model performs best in the test stage when the RMSE, NRMSE, NASH, and R evaluation indices are taken into account. These indices have values of 1.08, 0.088, 0.969, and 0.972, in that order. It is therefore offered as the best model for figuring out the largest dam rule curve pattern in real time. The structure developed in this study can quickly provide the best operating rules in accordance with the new inflows to the dam by using the GSGMDH model. This is done in a way that makes it possible to manage the system optimally in real time.

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Estimation of scour depth around cross-vane structures using a novel non-tuned high-accuracy machine learning approach

Cited by 4 publications

References 19 publications

Stone weir scour modelling in curved canals using a weighted regularized extreme learning machine*

Stone weir scour modelling in curved canals using a weighted regularized extreme learning machine*

Generalized structure of group method of data handling to prognosticate scour around various cross-vane structures

Optimal operation of the dam reservoir in real time based on generalized structure of group method of data handling and optimization technique

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