[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118115-en":3,"doc-seo-118115-105":30,"detail-sidebar-cat-0-en-105":96},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118115,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Suspended Sediment Estimation Using Machine Learning Methods - Paper","Suspended sediment in rivers plays a critical role in efficient water-resource use and the design and operation of hydraulic structures. This study estimates daily suspended sediment discharge using traditional multi-linear regression alongside machine learning approaches, including support vector machines (SVM) and the M5 decision tree (M5T). Daily stream flow, daily maximum/minimum water temperature, and suspended sediment concentration serve as inputs for all models. Performance is compared using determination coefficient (R²), root mean square error (RMSE), and mean absolute error (MAE), showing machine learning delivers stronger predictive performance.","How to cite: Taşar B., Üneş F., Demirci M., Güzel H., Varçin H. (2024) Suspended Sediment Estimation Using Machine Learning Methods. 2024 ”Air and Water – Components of the Environment” Conference Proceedings, Cluj-Napoca, Romania, p. 105-114, DOI: 10.24193/AWC2024_ 10.  \nSUSPENDED SEDIMENT ESTIMATION USING MACHINE LEARNING METHODS  \nBESTAMI TAŞAR 1, FATIH ÜNEŞ2 *, MUSTAFA DEMİRCİ3, HASAN  \nGÜZEL 4 HAKAN VARÇİN5  \nDOI: 10.24193/AWC2024_10  \nABSTRACT. Suspended Sediment Estimation Using Machine Learning Methods. Suspended sediment in rivers is important for efficiently using water resources and hydraulic structures. In this study, the suspended sediment load of rivers was estimated using traditional multi-linear regression (MLR), machine learning methods such as the support vector machines (SVM) and M5 decision tree (M5T). Data on daily stream flow, daily maximum and minimum water temperature and suspended sediment concentration in the river were used as input data in all models to predict daily suspended sediment discharge. The performance of all methods is evaluated based on a statistical approach. Determination coefficient (R2), root mean square error (RMSE) and mean absolute error (MAE) are used as comparison criteria. Overall, the machine learning approaches better predict suspended sediment discharge.  \nKeywords: Sediment Discharge, Prediction, Linear regression, Support Vector  \nMachines, M5 tree.  \nIntroduction  \nAccurate prediction of suspended sediment is of great importance in understanding the morphology of the river and utility water supply problems. Suspended sediment load in streams can be determined by different methods such as direct measurements at sediment observation stations, sediment rating curves, regression, artificial intelligence methods, and empirical approaches based on experimental studies. Although direct measurements from sediment observation stations are the most reliable way of determining the sediment material, it is a timeconsuming, costly, and error-prone method due to the sampling procedure (Olive & Rieger, 1988; Öztürk & Apaydın,2001) . Another method is artificial intelligence techniques or flexible calculation methods. Flexible calculation methods attempt to model suspended sediments using techniques such as Artificial Neural Networks (ANN), Fuzzy Logic Systems (FL), Adaptive Neural Fuzzy Systems (ANFIS), or  \n1 Iskenderun Technical University, Hatay – TURKEY, [e-mail: ](e-mail: bestami.tasar@iste.edu.tr)[bestami.tasar@iste.edu.tr](e-mail: bestami.tasar@iste.edu.tr)[ ](e-mail: bestami.tasar@iste.edu.tr)2* Iskenderun Technical University, Hatay – TURKEY, e-mail: [fatih.unes@iste.edu.tr](fatih.unes@iste.edu.tr)  \n3 Iskenderun Technical University, /Hatay – TURKEY, [e-mail: ](e-mail: mustafa.demirci@iste.edu.tr)[mustafa.demirci@iste.edu.tr](e-mail: mustafa.demirci@iste.edu.tr)[ ](e-mail: mustafa.demirci@iste.edu.tr)4 Iskenderun Technical University, Hatay – TURKEY, [e-mail: ](e-mail: hasan.guzel@iste.edu.tr)[hasan.guzel@iste.edu.tr](e-mail: hasan.guzel@iste.edu.tr)[ ](e-mail: hasan.guzel@iste.edu.tr)5 Iskenderun Technical University, Hatay – TURKEY, [e-mail: ](e-mail: hakan.varcin@iste.edu.tr)[hakan.varcin@iste.edu.tr](e-mail: hakan.varcin@iste.edu.tr)  \nGenetic Algorithm (GA) using various inputs. Empirical approaches are another method used in the literature based on experimental studies used in predicting suspended sediment (Lane and Kalinske 1941, Einstein 1950, Brooks 1963) . When the literature is examined, some studies draw attention, especially under these groupings.  \nIn recent years, artificial intelligence approaches have been widely used in water resource management and hydrological projects (Melesse et al. (2011); Üneş and Demirci, (2015); Üneş et al. (2020); Baek et al. (2020); Han and Morrison (2022)) . Memarian et al. (2013) observed the sediment load by combining artificial neural networks with genetic algorithms. Demirci and Baltaci (2013) predicted suspended sediment in a ","cbCaihVHeDmKOYIP","https://ap.wps.com/l/cbCaihVHeDmKOYIP","pdf",919363,1,10,"English","en",105,"# Introduction\n## Study Area\n## Methodology and Models\n## Model Evaluation Metrics\n## Results and Discussion","[{\"question\":\"Which models are used to estimate suspended sediment discharge in this study?\",\"answer\":\"The study compares multi-linear regression (MLR) with machine learning models: support vector machines (SVM) and the M5 decision tree (M5T).\"},{\"question\":\"What input data are used for the prediction models?\",\"answer\":\"Models use daily stream flow, daily maximum and minimum water temperature, and measured suspended sediment concentration to predict daily suspended sediment discharge.\"},{\"question\":\"How is model performance evaluated and compared?\",\"answer\":\"Performance is assessed using determination coefficient (R²), root mean square error (RMSE), and mean absolute error (MAE).\"},{\"question\":\"What is the main finding about prediction accuracy?\",\"answer\":\"Machine learning approaches provide better overall prediction of suspended sediment discharge than the traditional linear regression method.\"}]","Suspended Sediment Estimation Using Machine Learning Methods - 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