Support Vector Regression

Awad, Mariette; Khanna, Rahul

doi:10.1007/978-1-4302-5990-9_4

Cited by 796 publications

(470 citation statements)

References 7 publications

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“…SVR is a machine learning model that can apply kernel functions to map original data into a higher dimension and to the input for minimizing the ε-insensitive loss function (Awad & Khanna, 2015). Studies have shown that using SVR has great potential in rainfall-runoff predictions (Granata et al, 2016;Yan et al, 2018).…”

Section: Water Resources Researchmentioning

confidence: 99%

A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning

Xiang

Yan²,

Demir

2020

Water Resources Research

426

169

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Rainfall-runoff modeling is a complex nonlinear time series problem. While there is still room for improvement, researchers have been developing physical and machine learning models for decades to predict runoff using rainfall data sets. With the advancement of computational hardware resources and algorithms, deep learning methods such as the long short-term memory (LSTM) model and sequence-to-sequence (seq2seq) modeling have shown a good deal of promise in dealing with time series problems by considering long-term dependencies and multiple outputs. This study presents an application of a prediction model based on LSTM and the seq2seq structure to estimate hourly rainfall-runoff. Focusing on two Midwestern watersheds, namely, Clear Creek and Upper Wapsipinicon River in Iowa, these models were used to predict hourly runoff for a 24-hr period using rainfall observation, rainfall forecast, runoff observation, and empirical monthly evapotranspiration data from all stations in these two watersheds. The models were evaluated using the Nash-Sutcliffe efficiency coefficient, the correlation coefficient, statistical bias, and the normalized root-mean-square error. The results show that the LSTM-seq2seq model outperforms linear regression, Lasso regression, Ridge regression, support vector regression, Gaussian processes regression, and LSTM in all stations from these two watersheds. The LSTM-seq2seq model shows sufficient predictive power and could be used to improve forecast accuracy in short-term flood forecast applications. In addition, the seq2seq method was demonstrated to be an effective method for time series predictions in hydrology. Key Points:• An hourly runoff model was developed using the LSTM sequence-to-sequence learning method for 24-hr predictions on USGS stations • The proposed model shows better performance than traditional data-driven models and is applicable to different watersheds • The advantages and limitations of seq2seq models and how this model structure could work on the rainfall-runoff modeling is presented Supporting Information:• Supporting Information S1 Correspondence to: Z. Xiang, zhongrun-xiang@uiowa.edu Citation: Xiang, Z., Yan, J., & Demir, I. (2020). A rainfall-runoff model with LSTM-based sequence-to-sequence learning. Water Resources Research, 56, e2019WR025326. https://doi.

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Section: Water Resources Researchmentioning

confidence: 99%

A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning

Xiang

Yan²,

Demir

2020

Water Resources Research

426

169

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“…As one variant of the SVM, SVR still attempts to minimize the generalization error bound so as to achieve generalized performance [45]. Furthermore, the kernel function is utilized in the SVR to avoid the calculations in high-dimensional space.…”

Section: Experimental Settingmentioning

confidence: 99%

Building Energy Consumption Prediction: An Extreme Deep Learning Approach

et al. 2017

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Building energy consumption prediction plays an important role in improving the energy utilization rate through helping building managers to make better decisions. However, as a result of randomness and noisy disturbance, it is not an easy task to realize accurate prediction of the building energy consumption. In order to obtain better building energy consumption prediction accuracy, an extreme deep learning approach is presented in this paper. The proposed approach combines stacked autoencoders (SAEs) with the extreme learning machine (ELM) to take advantage of their respective characteristics. In this proposed approach, the SAE is used to extract the building energy consumption features, while the ELM is utilized as a predictor to obtain accurate prediction results. To determine the input variables of the extreme deep learning model, the partial autocorrelation analysis method is adopted. Additionally, in order to examine the performances of the proposed approach, it is compared with some popular machine learning methods, such as the backward propagation neural network (BPNN), support vector regression (SVR), the generalized radial basis function neural network (GRBFNN) and multiple linear regression (MLR). Experimental results demonstrate that the proposed method has the best prediction performance in different cases of the building energy consumption.

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“…It can yield improved generalization performance through minimizing the generalization error bound [45]. In addition, the kernel trick is adopted to realize the nonlinear transformation of input features.…”

Section: Support Vector Regressionmentioning

confidence: 99%

Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction

Ding

et al. 2018

Energies

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Abstract:To enhance the prediction performance for building energy consumption, this paper presents a modified deep belief network (DBN) based hybrid model. The proposed hybrid model combines the outputs from the DBN model with the energy-consuming pattern to yield the final prediction results. The energy-consuming pattern in this study represents the periodicity property of building energy consumption and can be extracted from the observed historical energy consumption data. The residual data generated by removing the energy-consuming pattern from the original data are utilized to train the modified DBN model. The training of the modified DBN includes two steps, the first one of which adopts the contrastive divergence (CD) algorithm to optimize the hidden parameters in a pre-train way, while the second one determines the output weighting vector by the least squares method. The proposed hybrid model is applied to two kinds of building energy consumption data sets that have different energy-consuming patterns (daily-periodicity and weekly-periodicity). In order to examine the advantages of the proposed model, four popular artificial intelligence methods-the backward propagation neural network (BPNN), the generalized radial basis function neural network (GRBFNN), the extreme learning machine (ELM), and the support vector regressor (SVR) are chosen as the comparative approaches. Experimental results demonstrate that the proposed DBN based hybrid model has the best performance compared with the comparative techniques. Another thing to be mentioned is that all the predictors constructed by utilizing the energy-consuming patterns perform better than those designed only by the original data. This verifies the usefulness of the incorporation of the energy-consuming patterns. The proposed approach can also be extended and applied to some other similar prediction problems that have periodicity patterns, e.g., the traffic flow forecasting and the electricity consumption prediction.

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Support Vector Regression

Cited by 796 publications

References 7 publications

A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning

A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning

Building Energy Consumption Prediction: An Extreme Deep Learning Approach

Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction

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