Application of Support Vector Machine in Lake Water Level Prediction

By MohammadSajjad Khan, Paulin Coulibaly. 2006

249 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/af66576cfdd67ee089ee0b605392169bc79c817e

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In Artificial Intelligence

In Environmental Engineering

In Management Science and Operations Research

Citations in the corpus

Application of support vector machine and relevance vector machine to determine evaporative losses in reservoirs (2012, methodology): “Researches use trial and error approach for determination of best performance of SVM and RVM models (Sivapragasam and Nitin Muttil, 2004; Khan and Coulibaly, 2006; Pal, 2006; Ahmad et al., 2007; Goh and Goh, 2007; Pal and Deswal, 2008; Samui and Sitharam, 2008; Das et al., 2009).”
Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models (2016, methodology): “Several researchers use trial and error approaches for determination of the best performance of the RVM models (Khan and Coulibaly 2006; Pal 2006; Sivapragasam and Muttil 2005).”
Monthly Rainfall Forecasting Using EEMD-SVR Based on Phase-Space Reconstruction (2016, background): “…2006; Feng et al. 2015), typhoon rainfall (Lin et al. 2009), groundwater level (Suryanarayana et al. 2014), rainfall runoff (Wang et al. 2013), real-time daily flow (Maheswaran and Khosa 2013), lake water level (Khan and Coulibaly 2006), and riverine suspended sediment load (Nourani et al. 2016).”
Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada (2012, methodology): “Khan and Coulibaly [2006] found that an SVM performed better than MLP ANNs in 3–12 month predictions of lake water levels.”
Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model (2017, methodology): “Regarding ANN models, despite their widespread use, some studies show that the ANN model has difficulty in the training phase due to iterative tuning of parameters and may not produce a unique solution with various model runs (Khan and Coulibaly 2006; Coulibaly and Evora 2007).”
M5 model tree application in daily river flow forecasting in Sohu Stream, Turkey (2013, methodology): “Keeping in view the better performance by radial basis function kernel function for various civil engineering applications [13, 17, 22, 23, 38], present study uses a”
A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series (2009, background): “There are three parameters in using RBF kernels: C, ε and σ . the accuracy of a SVM model is largely dependent on the selection of the model parameters.”
Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models (2014, methodology): “Khan and Coulibaly (2006) found that a SVR model performed better than ANNs in 3–12 month predictions of lake water levels.”
A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists (2017, methodology): “…using vegetation indices and precipitation (Ahmad et al., 2010), streamflow (Asefa et al., 2006), evapotranspiration (Tabari et al., 2012), lake level (Khan & Coulibaly, 2006), groundwater level (Yoon et al., 2011), flood stage (Yu et al., 2006), and sediment yield (Çimen, 2008), among others.”
A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer (2011, unknown): “, 2006), and lake water level (Asefa et al., 2005; Khalil et al., 2006; Khan and Coulibaly, 2006).”