Application of MGGP, ANN, MHBMO, GRG, and Linear Regression for Developing Daily Sediment Rating Curves

Niazkar, Majid; Zakwan, Mohammad

doi:10.1155/2021/8574063

Cited by 12 publications

(3 citation statements)

References 32 publications

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“…In addition to this, ensemble models were employed for developing daily sediment rating curves to predict sediment transport rates (Niazkar and Zakwan 2021). They utilized Multigene Genetic Programming (MGGP), an emerging ML method, to develop daily sediment rating models for two river gauging sites.…”

Section: Experimental Modelingmentioning

confidence: 99%

“…For instance, advancements in ensemble ML models were observed in a study by Niazkar and Zakwan (2023), determining improved predictive capabilities. The emergence of novel approaches, such as the MGGP model illustrated in Niazkar and Zakwan (2021), signifies promising developments in sediment rating curve development. These advancements contribute to a better understanding of sediment transport dynamics, despite the existing challenges.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

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A Comprehensive Review on Sediment Transport, Flow Dynamics, and Hazards in Steep Channels

Mir,

Patel

2024

JWMM

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The hydrological channel networks of the steep mountains are extensively designated and organized geographic systems and are very complicated. They are composed of granular beds that are uneven and are subjected to fluid forces that fluctuate spatially and temporally. The flow and movement of sediment in these streams are significantly shaped by large rocks such as boulders. However, it is difficult to comprehend these mountain streams because significant information is unavailable regarding these channels as compared to plane bed streams. To address this issue, a critical review of the numerical, computational fluid dynamics, and machine learning mechanisms underlying sediment transport across different flow conditions in steep channels is presented while considering the current and foreseeable conditions for various sediment transport phenomena. The present study emphasized to carry out further analysis on these steep channels using advanced available techniques to get an insight into the morphology of these channels. Furthermore, the hazards associated with steep mountain channels are reviewed as they have a significant impact on infrastructure and habitation in mountainous regions.

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Section: Experimental Modelingmentioning

confidence: 99%

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

A Comprehensive Review on Sediment Transport, Flow Dynamics, and Hazards in Steep Channels

Mir,

Patel

2024

JWMM

View full text Add to dashboard Cite

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“…According to the relevant provisions of the Standard for Hydrological Information and Hydrological Forecasting of the People's Republic of China (GB22482-2008T) and international hydrological model evaluation standards, the error of flood peak occurrence time is within 3 h, the relative error of flood peak is within 20%, which is qualified, and the Nash efficiency coefficient is greater than 0.9, which is Class A accuracy; the Nash efficiency coefficient between 0.7 and 0.9 is of second order accuracy; the Nash efficiency coefficient is of Class C accuracy between 0.5 and 0.7. The calculation expressions are (Majid and Mohammad, 2021a;Majid et al, 2021b):…”

Section: Accuracy Assessment Of Flood Simulationmentioning

confidence: 99%

Influence of different rainfall patterns and soil water content on hydrological processes in small watersheds

Li,

Bao

et al. 2023

Front. Environ. Sci.

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Introduction: Due to climate and underlying surface changes, flash floods occur frequently in northwest China, and the extreme rainstorm weather and flash floods along the eastern foot of Helan Mountain are increasing.Method: By constructing a hydrological model to simulate ten historical mountain flood events in the Suyukou watershed of the eastern foot of Helan Mountain, this study explores the constitutive relationship between rainfall and flood, evaluates accuracy, and analyzes errors. Six characteristic rain patterns were designed based on three parameters representing the position of rainfall peaks and the trend characteristics of rainfall, using 63 actual rainfall processes with a duration of 6 h selected from the rainfall stations of Suyukou watershed. Early warning indicators under different conditions were carried out based on the set of rain patterns.Results: The research results indicate that eight out of the ten simulated floods have passed the qualification test and the simulation results are good. The HEC-HMS hydrological model is suitable for simulating mountain floods in small watersheds and can provide good support for the simulation and early warning of mountain flood disasters.Discussion: Designing a rain pattern set based on actual data has put forward higher requirements for mountain flood warning and forecasting, making the warning more in line with the local actual situation and greatly improving the effectiveness of warning and forecasting.

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NDVI Forecasting Model Based on the Combination of Time Series Decomposition and CNN – LSTM

Gao

Du²,

Lei³

et al. 2023

Water Resour Manage

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Normalized difference vegetation index(NDVI) is the most commonly used factor to re ect vegetation growth status, and improving the prediction accuracy of NDVI is of great signi cance to the development of regional ecology. In this study, a new NDVI forecasting model based on the combination of time series decomposition(TSD), convolutional neural network (CNN) and long short-term memory (LSTM) was proposed. In order to verify the performance of TSD-CNN-LSTM model and explore the response of NDVI to climatic factors, two forecasting models of temperature and precipitation based on its own historical information and four NDVI forecasting models that based on Temperature, precipitation and its own historical information were established. The results show that TSD-CNN-LSTM model based on its own historical information had the best forecasting performance, with the RMSE, NSE, MAE to be 0.4996, 0.9981, 0.4169 for temperature, 5.6941, 0.9822, 3.9855 for precipitation and 0.0573, 0.9617, 0.0447 for NDVI, respectively. Meanwhile, the NDVI forecasting models based on climatic factors show that the model based on the combination of temperature and precipitation has the better effect than that based on single temperature and single precipitation. Combined with the results of correlation analysis it can be inferred that NDVI changes are most signi cantly in uenced by the combination of temperature and precipitation, followed by temperature, and least in uenced by precipitation. The above ndings can provide a meaningful reference and guidance for the study of vegetation growth with climate changes.

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Application of MGGP, ANN, MHBMO, GRG, and Linear Regression for Developing Daily Sediment Rating Curves

Cited by 12 publications

References 32 publications

A Comprehensive Review on Sediment Transport, Flow Dynamics, and Hazards in Steep Channels

A Comprehensive Review on Sediment Transport, Flow Dynamics, and Hazards in Steep Channels

Influence of different rainfall patterns and soil water content on hydrological processes in small watersheds

NDVI Forecasting Model Based on the Combination of Time Series Decomposition and CNN – LSTM

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