A comparison of machine learning models for suspended sediment load classification
نویسندگان
چکیده
The suspended sediment load (SSL) is one of the major hydrological processes affecting sustainability river planning and management. Moreover, sediments have a significant impact on dam operation reservoir capacity. To this end, reliable applicable models are required to compute classify SSL in rivers. application machine learning has become common solve complex problems such as modeling. present research investigated ability several data. This investigation aims explore new version classifiers for classification at Johor River, Malaysia. Extreme gradient boosting, random forest, support vector machine, multi-layer perceptron k-nearest neighbors been used values divided into multiple discrete ranges, where each range can be considered category or class. study illustrates two different scenarios related number categories, which five 10 with time scales, daily weekly. performance proposed was evaluated by statistical indicators. Overall, achieved excellent data under various scenarios.
منابع مشابه
Comparison and evaluation of intelligent models for river suspended sediment estimation (case study: Kakareza River, Iran)
Sediment transport constantly influences river and civil structures and the lack ofinformation about its exact amount makes management efforts less effective. Hence,achieving a proper procedure to estimate the sediment load in rivers is important. We usedsupport vector machine model to estimate the sediments of the Kakareza River in LorestanProvince and the results were compared with those obta...
متن کاملA Wavelet Support Vector Machine Combination Model for Daily Suspended Sediment Forecasting
Abstract In this study, wavelet support vector machine (WSWM) model is proposed for daily suspended sediment (SS) prediction. The WSVM model is achieved by combination of two methods; discrete wavelet analysis and support vector machine (SVM). The developed model was compared with single SVM. Daily discharge (Q) and SS data from Yadkin River at Yadkin College, NC station in the USA were used. I...
متن کاملComparison of Machine Learning Algorithms for Broad Leaf Species Classification Using UAV-RGB Images
Abstract: Knowing the tree species combination of forests provides valuable information for studying the forest’s economic value, fire risk assessment, biodiversity monitoring, and wildlife habitat improvement. Fieldwork is often time-consuming and labor-required, free satellite data are available in coarse resolution and the use of manned aircraft is relatively costly. Recently, unmanned aeria...
متن کاملComparison and evaluation of the performance of data-driven models for estimating suspended sediment downstream of Doroodzan Dam
Dams control most of the sediment entering the reservoir by creating static environments. However, sediment leaving the dam depends on various factors such as dam management method, inlet sediment, water height in the reservoir, the shape of the reservoir, and discharge flow. In this research, the amount of suspended sediment of Doroodzan Dam based on a statistical period of 25 years has been i...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Engineering Applications of Computational Fluid Mechanics
سال: 2022
ISSN: ['1997-003X', '1994-2060']
DOI: https://doi.org/10.1080/19942060.2022.2073565