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https://ascelibrary.org/doi/10.1061/%28ASCE%291084-0699%282006%2911%3A3%28199%29
This paper examines the potential of the support vector machine (SVM) in long-term prediction of lake water levels. Lake Erie mean monthly water levels from 1918 to 2001 are used to predict future water levels up to 12 months ahead. The results are compared with a widely used neural network model called a multilayer perceptron (MLP) and with a ...
https://www.researchgate.net/publication/248803002_Application_of_Support_Vector_Machine_in_Lake_Water_Level_Prediction
In a research, [9] studied the potential of the support vector machine to predict the level of water contained in Lake Erie in the longrun (12 months). They also made comparisons between SVM, ANN ...
https://ascelibrary.org/doi/full/10.1061/%28ASCE%291084-0699%282006%2911%3A3%28199%29
May 03, 2006 · This paper examines the potential of the support vector machine (SVM) in long-term prediction of lake water levels. Lake Erie mean monthly water levels from 1918 to 2001 are used to predict future water levels up to 12 months ahead. The results are compared with a widely used neural network model called a multilayer perceptron (MLP) and with a ...
https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/1472-6947-10-16
Mar 22, 2010 · A supervised machine learning method, the support vector machine (SVM) algorithm [], has demonstrated high performance in solving classification problems in many biomedical fields, especially in bioinformatics [2, 3].In contrast to logistic regression, which depends on a pre-determined model to predict the occurrence or not of a binary event by fitting data to a logistic curve, SVM ...Cited by: 222
https://www.sciencedirect.com/science/article/pii/S1568494614000611
3.1.4. Lake and reservoir water level prediction. Khan and Coulibaly examined the potential of the support vector machine (SVM) in long-term prediction of lake water levels. Lake Erie mean monthly water levels from 1918 to 2001 were used to predict future water levels up to 12 months ahead.Cited by: 256
https://link.springer.com/chapter/10.1007/978-3-319-71273-4_26
Dec 30, 2017 · Urban Water Flow and Water Level Prediction Based on Deep Learning ... This paper aims to provide an appropriate methodology for long-term prediction for the water flow and water level parameters of the Shannon river in Ireland over a 30-year period from 1983–2013 through a framework that is composed of three phases: city wide scale analytics ...Cited by: 10
https://www.sciencedirect.com/science/article/pii/S002216941000819X
The purpose of this paper is to investigate the performance of wavelet-support vector machine conjunction model for monthly streamflow forecasting and to compare this with the performance of single support vector machine (SVM) models. 2. Support vector machines (SVMs)Cited by: 213
https://link.springer.com/article/10.1007/s11356-017-9243-7
May 23, 2017 · However, the efficacy of these AI models in predicting DO levels in the hypoxic river systems having multiple pollution sources and complicated pollutants behaviors is unclear. Given this dilemma, we developed a promising AI model, known as support vector machine (SVM), to predict the DO concentration in a hypoxic river in southeastern China.Cited by: 21
https://www.scientific.net/AMM.851.840
Aug 01, 2016 · Slope stability estimation is a complex engineering problem involving many factors. A hybrid model based on the combination of finite element software GEO-STUDIO and support vector machine (SVM) is proposed to address the problem. The study took a high slope of Jingjiang reach of Yangtze River as the research object. Several important parameters, including values of geometric …Author: Gui Lan Tao, Zi Shun Yao, Bin Zheng Tan, Cong Cong Gao, Yi Wen Yao
https://www.scientific.net/AMM.145.455
Dec 01, 2011 · In Taiwan, even though the average annual rainfall is up to 2500 mm, water shortage during the dry season happens sometimes. Especially in recent years, water shortage has seriously affected the agriculture, industry, commerce, and even the essential daily water use. Under the threat of climate change in the future, efficient use of water resources becomes even more challenging.Cited by: 6
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