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To overcome these limitations, this study applies a machine learning algorithm to an aggregated sediment settling experimental database and develops a nonparametric data-driven model to estimate ...
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Machine learning for predicting sediment particle size distributions Galen Egan [email protected] 1 Introduction Marine sediment (i.e., mud) particles cover approximately 70% of the Earth's surface [Dutkiewicz et al., 2015], and play a critical role in a host of environmental engineering problems. Settling rates of marine particles control ...
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Urine sediment examination is the microscopic examination of the precipitated components obtained by centrifuging urine. It is a useful test for the diagnosis of various systemic diseases and is ...
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Proses sedimentasi dalam kimia industry dilakukan secara sinambung dengan menggunakan alat yang dikenal dengan wawa tkickener, sedangkan untuk skala …
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Sediment transport in natural waters can be divided into two major modes, namely bed-load and suspended-load transport (Fig. 1). Bed-load transport refers to the migration of …
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inference system (ANFIS), support vector machine (SVM), and different algorithms of evolutionary. This can be indicator of a high potential of machine learning based approaches for prediction of localized sediment transport characteristics. As such, the aim of the present chapter is to review how efficiently the employed machine
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The Conventional automated microscopic image analysis could be done in three steps. Pre-processing of the data followed by feature Extraction and Feature classification as shown in Fig. 2 (a). A general dataset may have various noises, unwanted values and it may be in an incorrect format, which cannot be directly applied to the system.
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The results indicate a noticeable benefit of feature engineering that empowers the use of simple ML algorithms, giving enhanced performance (78% accuracy) and applicability to a standard computer.
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Request PDF | Sediment Transport Modeling through Machine Learning Methods: Review of Current Challenges and Strategies | Sediment transportation in rivers is considered as an important issue for ...
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DOI: 10.1061/(asce)ir.1943-4774.0001689 Corpus ID: 251378790; Prediction of Bed-Load Sediment Using Newly Developed Support-Vector Machine Techniques @article{Samantaray2022PredictionOB, title={Prediction of Bed-Load Sediment Using Newly Developed Support-Vector Machine Techniques}, author={Sandeep Samantaray …
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CONTENTS . Preface_----- Abstract----- Introduction- _ _ __ _ _-_ _____ ___ -_ ___ __ __ __ _ Acknowledgments ____ _ _ _ - _ _ _ - _ - _ _ _ - _ _ _ _
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In the validation, the proposed machine learning neural network models were concluded better than the reference ANN model (Jothiprakash and Garg, 2009) and further it was found that the GRU performs best as compared to others.Therefore, the proposed models were used to forecast the sediment volume inside the Gobindsagar reservoir in the next …
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The spatio-temporal dynamism of sediment discharge (Qs) in rivers is influenced by various natural and anthropogenic factors. Unfortunately, most rivers are only monitored at a limited number of stations or not gauged at all. Therefore, this study aims to provide a remote-sensing-based alternative for Qs monitoring. The at-a-station hydraulic geometry (AHG) …
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pada karakteristik sedimen yang terdiri dari ukuran (size), bentuk (shape), berat volume (specific weigh) dan berat jenis (sepecific gravity) serta kecepatan jatuh (fall velocity). Hasil penelitian menunjukkan gradasi partikel sedimen …
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PDF | Sediment erosion is a major problem in hydropower plants of Himalayan and Andes region which causes frequent breakdown of electromechanical parts... | Find, read and cite all the research ...
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Sediment transport is a normal phenomenon in rivers and streams, contributing significantly to ecosystem production and preservation by replenishing vital nutrients and preserving aquatic life's natural habitats. Thus, sediment transport prediction through modeling is crucial for predicting flood events, tracking coastal erosion, planning …
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Comparison of the machine-learning method (TSVM model) and the traditional P criterion in recognizing S-type zircon. (A) The accuracy of both methods for non-S-and S-type zircon with different P ...
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There was no recorded work for the machine learning algorithm models for sediment yield index forecasting in the relevant literature. 2 Modeling approaches Three artificial intelligence data-driven models using machine learning methods have been deployed in this study for prediction of the sediment yield index (SYI), namely:
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PDF | Sediments accumulated in check dams are a valuable measure to estimate soil erosion rates. Here, geographic information systems (GIS) and three... | Find, read and cite all the research you ...
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The training dataset, including 123 S-type zircon and 176 non-S-type zircon, is not large but adequate for training the machine learning models, as evidenced by the learning curve on experiments incrementally increasing the number of training set data used to train the machine learning models (SI Appendix, Fig. S6). The test set, composed of …
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1. Introduction. The amount of available data on coastal systems has increased in recent years, ranging from topographic and bathymetric data (e.g., Turner et al., 2016), to compilations of sediment transport and physical forcing (e.g., Bolaños and Souza, 2010; Garel and Ferreira, 2015; Nelson et al., 2013; van der Werf et al., …
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Machine learning for predicting sediment particle size distributions Galen Egan [email protected] 1 Introduction Marine sediment (i.e., mud) particles cover …
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This investigation aims to explore a new version of machine learning classifiers for SSL classification at Johor River, Malaysia and achieves excellent classification of the SSL data under various scenarios. ABSTRACT The suspended sediment load (SSL) is one of the major hydrological processes affecting the …
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Sediment facies analysis is the first step in most Earth and environmental research studies; nevertheless, high-resolution facies reconstructions require specific …
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In this chapter, a comprehensive survey of machine learning techniques, specifically employed in the prediction of bed-load and total sediment load, was …
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89 Potential Sediment Yield Estimation 6 Using Machine Learning, Artificial Intelligence Techniques and GIS Abstract "Soil erosion"-induced land degradation is the
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The Lake Taihu is the three largest freshwater lakes in China, located in the core area of the Yangtze Delta. The total area of the Taihu Basin is 36,895 km 2, of which plain depressions and water surfaces account for about 84 %, it is a typical plain river network area.The water quality of most rivers around the Lake Taihu exceeds the Class …
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Runoff and sediment yield were measured with 46 rainfall events which occurred during the first year after machine traffic from 17 July 2015 to 11 July 2016 under natural conditions. According to Pearson correlation, runoff (r = 0.51), runoff coefficient (r = 0.55), and sediment yield (r = 0.51) were significantly correlated with slope gradient.
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Download PDF. Download PDF. Article; Open access; Published: 09 April 2021; Suspended sediment load prediction using long short-term memory neural network. ... Machine learning (ML) algorithms ...
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Faktor-faktor yang kubig yang mempengaruhi sedimentasi secara mekanik antara lain: 1. Sumber material batuan sedimen Sifat dan komposisi batuan sedimen sangat dipengaruhi oleh material-material asalnya. 2. Lingkungan Pengendapan Secara umum lingkungan pengendapan disebapkan dalam tiga bagian, yaitu: lingkungan pengendapan darat, …
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