☆ 4.3 Article

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

MATHEMATICAL PROBLEMS IN ENGINEERING (2021)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Meteorology & Atmospheric Sciences

Applications of innovative polygonal trend analyses to precipitation series of Eastern Black Sea Basin, Turkey

Tugce Hirca et al.

Summary: This study examines historical variations of hydroclimatic variables in Eastern Black Sea Basin (EBSB) in Turkey. The Mann-Kendall (MK) and Innovative Polygon Trend Analysis (IPTA) methods were utilized to analyze 56-year precipitation data collected at 8 measuring stations. IPTA showed to be more sensitive in trend detection than MK, identifying trends in more months and stations.

THEORETICAL AND APPLIED CLIMATOLOGY (2022)

添加到收藏夹

Article Green & Sustainable Science & Technology

Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers

Tarate Suryakant Bajirao et al.

Summary: This study analyzed the validity of simple and wavelet-coupled Artificial Intelligence models for daily Suspended Sediment estimation in the Koyna River basin of India. The results showed that data pre-processing using wavelet transform significantly improves the model's predictive efficiency and reliability, with the Coiflet wavelet-coupled ANFIS model performing the best. Sensitivity analysis revealed the importance of the previous one-day SSC as the most crucial input variable for daily SSC estimation in the Koyna River basin.

SUSTAINABILITY (2021)

添加到收藏夹

Article Mathematics, Interdisciplinary Applications

Assessment of Artificial Intelligence Models for Developing Single-Value and Loop Rating Curves

Majid Niazkar et al.

Summary: This study explores a novel hybrid genetic programming-gradient technique for estimating river discharges, which performs the best on some datasets. Comparison reveals that no single model outperforms all others on all datasets and evaluation criteria.

COMPLEXITY (2021)

添加到收藏夹

Article Mathematics, Interdisciplinary Applications

Assessment of Artificial Intelligence Models for Estimating Lengths of Gradually Varied Flow Profiles

Majid Niazkar et al.

Summary: The study utilized artificial intelligence models to estimate GVF profile lengths and found that GP and ANN significantly improved length estimation. Additionally, considering more subreaches in the direct method led to better results in terms of accuracy.

COMPLEXITY (2021)

添加到收藏夹

Article Water Resources

Effective discharge computation in the lower Drava River

Mohammad Zakwan et al.

Summary: Magnitude frequency analysis of suspended sediment transport in the lower Drava River reveals that discharges close to the average daily discharge play a significant role in sediment transport. Effective discharge was found to be less than half of Q(1.5) and Q(2), and data aggregation affects it. Factor load discharge estimation indicates that a specific discharge in the annual maximum discharge time series carries 90% of the total sediment load in the lower Drava River.

HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES (2021)

添加到收藏夹

Article Mathematics, Interdisciplinary Applications

A Comparative Analysis of Data-Driven Empirical and Artificial Intelligence Models for Estimating Infiltration Rates

Mohammad Zakwan et al.

Summary: The study shows that artificial intelligence models can more accurately estimate infiltration rates and perform well in comparison. Compared to traditional models, ANN and the hybrid MGGP-GRG model can significantly reduce errors and improve the accuracy of infiltration rate predictions.

COMPLEXITY (2021)

添加到收藏夹

Article Environmental Sciences

Artificial intelligence for suspended sediment load prediction: a review

Deepak Gupta et al.

Environmental Earth Sciences (2021)

添加到收藏夹

Article Multidisciplinary Sciences

Suspended sediment load prediction using long short-term memory neural network

Nouar AlDahoul et al.

Summary: This study proposes a Long Short-Term Memory model to predict suspended sediments in Malaysia's Johor River utilizing only discharge data. Performance evaluation shows that Long Short-Term Memory outperformed other models with regression values of 92.01%, 96.56%, 96.71%, and 99.45% for daily, weekly, 10-day, and monthly scenarios, respectively.

SCIENTIFIC REPORTS (2021)

添加到收藏夹

Article Green & Sustainable Science & Technology

Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering

Rana Muhammad Adnan et al.

Summary: The study proposed a new method for accurately estimating daily suspended sediments (SSs) by combining MARS and Kmeans algorithms, showing superior performance compared to other models at two stations on the Yangtze River. The MARS-KM models exhibited significant improvement in predicting SSs at both stations in different scenarios.

SUSTAINABILITY (2021)

添加到收藏夹

Article Engineering, Multidisciplinary

Optimum Design of Straight Circular Channels Incorporating Constant and Variable Roughness Scenarios: Assessment of Machine Learning Models

Majid Niazkar

Summary: This study used machine learning models to improve the accuracy of circular channel design, showing significant improvements over explicit design equations available in the literature. The application of the ML models demonstrated superior results in the optimum design of circular channels.

MATHEMATICAL PROBLEMS IN ENGINEERING (2021)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

An Excel VBA-based educational module for bed roughness predictors

Majid Niazkar

Summary: The estimation of flow resistance coefficients is crucial in open-channel flows for hydraulic modeling and water resources engineering. While many equations and methods exist, only the traditional Manning's and Keulegan's equations are widely taught and utilized. New bed roughness predictors face limitations due to lack of educational resources, and most of them are scheme-based rather than single equations. Proper definition of problem statements is essential for applying these predictors effectively.

COMPUTER APPLICATIONS IN ENGINEERING EDUCATION (2021)

添加到收藏夹

Article Water Resources

Understanding the effect of catchment characteristics on suspended sediment dynamics during flood events

Arman Haddadchi et al.

HYDROLOGICAL PROCESSES (2020)

添加到收藏夹

Article Water Resources

Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis

Ahmad Sharafati et al.

HYDROLOGICAL SCIENCES JOURNAL (2020)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Assessment of artificial intelligence models for calculating optimum properties of lined channels

Majid Niazkar

JOURNAL OF HYDROINFORMATICS (2020)

添加到收藏夹

Article Engineering, Civil

Developing a New Accuracy-Improved Model for Estimating Scour Depth Around Piers using a Hybrid Method

Majid Niazkar et al.

IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING (2019)

添加到收藏夹

Article Water Resources

Time-varying, nonlinear suspended sediment rating curves to characterize trends in water quality: An application to the Upper Hudson and Mohawk Rivers, New York

Kuk-Hyun Ahn et al.

HYDROLOGICAL PROCESSES (2019)

添加到收藏夹

Article Water Resources

Using sediment rating parameters to evaluate the changes in sediment transport regimes in the middle Yellow River basin, China

Shiyan Zhang et al.

HYDROLOGICAL PROCESSES (2019)

添加到收藏夹

Article Environmental Sciences

Prediction of Suspended Sediment Load Using Data-Driven Models

Rana Muhammad Adnan et al.

WATER (2019)

添加到收藏夹

Article Water Resources

Modelling suspended sediment concentration using artificial neural networks for Gangotri glacier

Rajesh Joshi et al.

HYDROLOGICAL PROCESSES (2016)

添加到收藏夹

Article Water Resources

River basin sediment flux assessments

M. Delmas et al.

HYDROLOGICAL PROCESSES (2011)

添加到收藏夹

Article Water Resources

Reliability of sediment rating curves for a deciduous forest watershed in Iran

Seyed Hamidreza Sadeghi et al.

HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES (2010)

添加到收藏夹

Article Engineering, Civil

Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data

M. Cobaner et al.

JOURNAL OF HYDROLOGY (2009)

添加到收藏夹

Article Water Resources

Estimation of daily suspended sediments using support vector machines

Mesut Cimen

HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES (2008)

添加到收藏夹

Article Engineering, Civil

A genetic programming approach to suspended sediment modelling

Ali Aytek et al.

JOURNAL OF HYDROLOGY (2008)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.