Wavelet Support Vector Regression Research Articles

Sustainable groundwater management through an optimal water supply system using a coupled simulation-optimization approach.

Meeting people's water supply needs in cities is an urgent challenge. Especially in coastal cities, surface water sources are usually inadequate and so groundwater is frequently over-exploited, resulting in its rapid decrease and intrusion of saltwater. Conjunctive use of surface water and groundwater is the best alternative approach to mitigate the overuse of groundwater through effective distribution of surface water sources. It requires numerous runs of the simulation-optimization model to select an optimal pattern of water distribution by keeping the groundwater levels under control. In this study, a new simulation-optimization model is developed using wavelet support vector regression (WSVR) and genetic algorithm (GA) to propose an optimal water distribution system to Visakhapatnam city on the East Coast of India, fulfilling the constraints of surface water quantity, aquifer pumping, and drawdown. Estimation of groundwater pumping and groundwater level variation in spatial context is challenging in urban environment. To overcome this, the calibrated modular finite-difference flow (MODFLOW) model for this study area has been used to prepare the spatial and temporal variation of model inputs such as groundwater pumping and groundwater levels. The WSVR-GA model's performance to reduce the groundwater pumping is evaluated in three distinct cases. The surface water resources from three sources are distributed to different wards in the city source-wise in case I and centralized in cases II and III, while the source-wise surface water constraints are limited to monthly in cases I and II and annual in case III. The WSVR-GA management model suggested ward-wise groundwater pumping restrictions, resulting in 9.57 MCM, 11.64 MCM, and 12.54 MCM increase in total groundwater storage capacity in cases I, II, and III respectively. Cases II and III offer 21% and 25%, respectively, more storage than case I. Thus, centralized distribution systems have increased the sustainability of groundwater supplies by preventing overdrafts caused by a lack of surface water resources. Validation of results using MODFLOW indicates a substantial rise in groundwater levels in the study area.

Reservoir water level forecasting using wavelet support vector regression (WSVR) based on teaching learning-based optimization algorithm (TLBO)

Reservoir water level forecasting using wavelet support vector regression (WSVR) based on teaching learning-based optimization algorithm (TLBO)

Estimating the incubated river water quality indicator based on machine learning and deep learning paradigms: BOD5 Prediction.

As an indicator measured by incubating organic material from water samples in rivers, the most typical characteristic of water quality items is biochemical oxygen demand (BOD5) concentration, which is a stream pollutant with an extreme circumstance of organic loading and controlling aquatic behavior in the eco-environment. Leading monitoring approaches including machine leaning and deep learning have been evolved for a correct, trustworthy, and low-cost prediction of BOD5 concentration. The addressed research investigated the efficiency of three standalone models including machine learning (extreme learning machine (ELM) and support vector regression (SVR)) and deep learning (deep echo state network (Deep ESN)). In addition, the novel double-stage synthesis models (wavelet-extreme learning machine (Wavelet-ELM), wavelet-support vector regression (Wavelet-SVR), and wavelet-deep echo state network (Wavelet-Deep ESN)) were developed by integrating wavelet transformation (WT) with the different standalone models. Five input associations were supplied for evaluating standalone and double-stage synthesis models by determining diverse water quantity and quality items. The proposed models were assessed using the coefficient of determination (R2), Nash-Sutcliffe (NS) efficiency, and root mean square error (RMSE). The significance of addressed research can be found from the overall outcomes that the predictive accuracy of double-stage synthesis models were not always superior to that of standalone models. Overall results showed that the SVR with 3th distribution (NS = 0.915) and the Wavelet-SVR with 4th distribution (NS = 0.915) demonstrated more correct outcomes for predicting BOD5 concentration compared to alternative models at Hwangji station, and the Wavelet-SVR with 4th distribution (NS = 0.917) was judged to be the most superior model at Toilchun station. In most cases for predicting BOD5 concentration, the novel double-stage synthesis models can be utilized for efficient and organized data administration and regulation of water pollutants on both stations, South Korea.

Developing a wavelet-AI hybrid model for short- and long-term predictions of the pollutant concentration of particulate matter10

Prediction of the particle concentration has received a great deal of research interest in recent decades, especially in the cities exposed to dust storms; hence, the use of early warning systems based on more accurate predictions to inform about the air quality situation in the next hours is very important. Thus, this study was conducted to improve the prediction accuracy of the particulate matter of aerodynamic diameter ≤ 2.5-μm particle concentration in the short term and long term, by combining the discrete wavelet transform technique with the artificial intelligence methods, i.e., adaptive neuro-fuzzy inference system, support vector regression, and artificial neural network. The data of the concentration of the suspended particles in Yazd city regarding 16 influential parameters were used during 2015–2019 in 6 models with and without wavelet, and the results obtained from these models were assessed and compared for hourly (short-term) and daily (long-term) timescales. The results based on 6 evaluation and validation criteria showed that the wavelet-support vector regression and wavelet-adaptive neuro-fuzzy inference system models have higher accuracy than other models in daily forecasting with an accuracy of about 99% and wavelet-support vector regression has higher accuracy in hourly forecasting with an accuracy of about 96%. The models combined with the wavelet transform produce better results rather than the single models showing the high effect of wavelet transformation in increasing the accuracy of artificial intelligence models used for prediction of the particulate matter ≤ 10 μm concentration.

Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression

Utilizing new approaches to accurately predict groundwater level (GWL) in arid regions is of vital importance. In this study, support vector regression (SVR), Gaussian process regression (GPR), and their combination with wavelet transformation (named wavelet-support vector regression (W-SVR) and wavelet-Gaussian process regression (W-GPR)) are used to forecast groundwater level in Semnan plain (arid area) for the next month. Three different wavelet transformations, namely Haar, db4, and Symlet, are tested. Four statistical metrics, namely root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R 2), and Nah-Sutcliffe efficiency (NS), are used to evaluate performance of different methods. The results reveal that SVR with RMSE of 0.04790 (m), MAPE of 0.00199%, R 2 of 0.99995, and NS of 0.99988 significantly outperforms GPR with RMSE of 0.55439 (m), MAPE of 0.04363%, R2 of 0.99264, and NS of 0.98413. Besides, the hybrid W-GPR-1 model (i.e. GPR with Harr wavelet) remarkably improves the accuracy of GWL prediction compared to GPR. Finally, the hybrid W-SVR-3 model (i.e. SVR with Symlet) provides the best GWL prediction with RMSE, MAPE, R2, and NS of 0.01290 (m), 0.00079%, 0.99999, and 0.99999, respectively. Overall, the findings indicate that hybrid models can accurately predict GWL in arid regions.

Study on Improvement and Risk Assessment of Marine Logistics Finance Development Model Based on the Background of Free Trade Zone Construction

Fu, Z. and Wang, J., 2020. Study on improvement and risk assessment of marine logistics finance development model based on the background of free trade zone construction. In: Liu, X. and Zhao, L. (eds.), Today's Modern Coastal Society: Technical and Sociological Aspects of Coastal Research. Journal of Coastal Research, Special Issue No. 111, pp. 222–225. Coconut Creek (Florida), ISSN 0749-0208.The free trade area has a more important impact on the marine logistics industry, promoting the country's re-export trade, offshore trade, offshore finance, and shipping services. Through the determination of maritime logistics enterprises, this paper provides a method for the determination of the comprehensive risk value of logistics financial business. Through the establishment of the risk evaluation model by the wavelet support vector regression algorithm, from the development of the comprehensive value-at-risk business, the paper summarizes the concepts, practice forms, development process, and important relations of the relevant trade and shipping logistics industry and the foreign free trade zone.

A novel Hybrid Wavelet-Locally Weighted Linear Regression (W-LWLR) Model for Electrical Conductivity (EC) Prediction in Surface Water

Rivers are the most common and vital sources of water, which play a fundamental role in ecological systems and human life. Water quality assessment is a major element of managing water resources and accurate prediction of water quality is very essential for better management of rivers. The electrical conductivity (EC) is known as one of the most important water quality parameters to predict salinity and mineralization of water. The present study introduces a novel hybrid wavelet-locally weighted linear regression (W-LWLR) method to predict the monthly EC of the Sefidrud River in Iran. 240 monthly discharge (Q) and EC samples, over a period of 20 years, were collected. The data were divided into two frequency components at two decomposition levels using the mother wavelet Bior 6.8. To compare the performance of various methods, the standalone LWLR, support vector regression (SVR), wavelet support vector regression (W-SVR), autoregressive integrated moving average (ARIMA), wavelet ARIMA (W-ARIMA), multivariate linear regression (MLR), and wavelet MLR (W-MLR) were also used. The discrete wavelet transform (DWT) was coupled with the LWLR, SVR, and ARIMA to create the W-LWLR, W-SVR, W-ARIMA methods to predict the EC parameter. The comparisons demonstrated that the W-LWLR was more accurate and efficient than the LWLR, SVR, W-SVR, ARIMA, and W-ARIMA methods. The correlation coefficient (R) values were 0.973, 0.95, 0.565, 0.473, 0.425, 0.917 for the W-LWLR, W-SVR, LWLR, SVR, ARIMA, and W-ARIMA methods, respectively. Further, the root mean square error (RMSE) of W-LWLR was 89.78, while the corresponding values for W-SVR, LWLR, SVR, ARIMA, W-ARIMA, MLR, and W-MLR were 123.50, 319.95, 341.20, 350.153, 155.292, 351.774, and 157.856 respectively. The overall comparison metrics and error analysis demonstrated the superiority of the new proposed W-LWLR method for water quality prediction.

A new intelligent method for monthly streamflow prediction: hybrid wavelet support vector regression based on grey wolf optimizer (WSVR–GWO)

Forecasting daily and monthly streamflows is necessary for short- and long-term water resources management, particularly in extreme cases, e.g., flood and drought. Accurate models are needed to plan and manage water resources in watersheds. Recently, support vector regression (SVR) shows the ability to handle the hydrological forecasting issues, but the accuracy of SVR depends on the appropriate choice of parameters and selection of proper inputs. A new meta-heuristic algorithm called grey wolf optimizer (GWO) is used in the current research in order to improve SVR accuracy in forecasting monthly streamflow. The proposed approach is compared with other evolutionary methods, particle swarm optimization, shuffled complex evolution, and multi-verse optimization, that are employed in tuning SVR parameters. Furthermore, the proposed methods were also combined with wavelet transform and tested using monthly streamflow data from two gauging stations, Ain Bedra and Fermatou, in Algeria. To assess the performance of the developed models, Nash–Sutcliffe efficiency (NSE), correlation coefficient, root mean squared error (RMSE), and mean absolute error (MAE) were used. The obtained results indicate that multi-linear regression provides appropriate input variables to SVR models. According to all of the performance measures used, hybrid models exhibit better performances, in monthly streamflow prediction, compared with single versions. For example, for the Ain Bedra station, the NSEcriterion and the correlation coefficient values increased considerably from 27.36% and 0.5405, for the single models, to 95.72% and 0.9786, for the hybrid models. A great decrease has also been obtained for the RMSE and MAE values, which decreased from 0.1562 m3/s and 0.1244 m3/s to 0.6433m3/s and 0.3047 m3/s. In addition, the new GWO algorithm outperformed the other algorithms in terms of both forecasting accuracy and convergence.

Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates

ABSTRACTEvaporation rate is one of the key parameters in determining the ecological conditions and it has an irrefutable role in the proper management of water resources. In this paper, the efficiency of some data-driven techniques including support vector regression (SVR) and artificial neural networks (ANN) and combination of them with wavelet transforms (WSVR and WANN) were investigated for predicting evaporation rates at Tabriz (Iran) and Antalya (Turkey) stations. For evaluating the performances of studied techniques, four different statistical indicators were utilized namely the root mean square error (RMSE), the mean absolute error (MAE), the correlation coefficient (R), and Nash–Sutcliffe efficiency (NSE). Additionally, Taylor diagrams were implemented to test the similarity among the observed and predicted data. Outcomes showed that at Tabriz station, the ANN3 (third input combination that are air temperatures and solar radiation used by ANN) with RMSE of 0.701, MAE of 0.525, R of 0.990 and NSE of 0.977 had better performances in comparison with WANN, SVR and WSVR. So, the wavelet transforms did not have positive effects in increasing the precision of ANN and SVR predictions at Tabriz station. Also, approximately the same trend was seen at Antalya station. In other words, ANN5 (fifth input combination that are air temperatures, relative humidity and solar radiation used by ANN) with RMSE of 0.923, MAE of 0.697, R of 0.962 and NSE of 0.898 had a more accurate predictions among others. Conversely, wavelet transform reduced the prediction errors of SVR at Antalya station. So, the WSVR5 with RMSE of 1.027, MAE of 0.728, R of 0.950 and NSE of 0.870 predicted evaporation rates of Antalya station more precisely than other SVR models. As a conclusion, results from the current study proved that ANN provided reasonable trends for evaporation modeling at both Tabriz and Antalya stations.

Comparative Study of Hybrid-Wavelet Artificial Intelligence Models for Monthly Groundwater Depth Forecasting in Extreme Arid Regions, Northwest China

Prediction of groundwater depth (GWD) is a critical task in water resources management. In this study, the practicability of predicting GWD for lead times of 1, 2 and 3 months for 3 observation wells in the Ejina Basin using the wavelet-artificial neural network (WA-ANN) and wavelet-support vector regression (WA-SVR) is demonstrated. Discrete wavelet transform was applied to decompose groundwater depth and meteorological inputs into approximations and detail with predictive features embedded in high frequency and low frequency. WA-ANN and WA-SVR relative of ANN and SVR were evaluated with coefficient of correlation (R), Nash-Sutcliffe efficiency (NS), mean absolute error (MAE), and root mean squared error (RMSE). Results showed that WA-ANN and WA-SVR have better performance than ANN and SVR models. WA-SVR yielded better results than WA-ANN model for 1, 2 and 3-month lead times. The study indicates that WA-SVR could be applied for groundwater forecasting under ecological water conveyance conditions.

Pre-processing data using wavelet transform and PCA based on support vector regression and gene expression programming for river flow simulation

An accurate estimation of flow using different models is an issue for water resource researchers. In this study, support vector regression (SVR) and gene expression programming (GEP) models in daily and monthly scale were used in order to simulate Gamasiyab River flow in Nahavand, Iran. The results showed that although the performance of models in daily scale was acceptable and the result of SVR model was a little better, their performance in the daily scale was really better than the monthly scale. Therefore, wavelet transform was used and the main signal of every input was decomposed. Then, by using principal component analysis method, important sub-signals were recognized and used as inputs for the SVR and GEP models to produce wavelet-support vector regression (WSVR) and wavelet-gene expression programming. The results showed that the performance of WSVR was better than the SVR in such a way that the combination of SVR with wavelet could improve the determination coefficient of the model up to 3% and 18% for daily and monthly scales, respectively. Totally, it can be said that the combination of wavelet with SVR is a suitable tool for the prediction of Gamasiyab River flow in both daily and monthly scales.

A Probabilistic Wavelet–Support Vector Regression Model for Streamflow Forecasting with Rainfall and Climate Information Input*

Abstract It is essential to explore reliable streamflow forecasting techniques for water resources management. In this study, a Bayesian wavelet–support vector regression model (BWS model) is developed for one- and multistep-ahead streamflow forecasting using local meteohydrological observations and climate indices including El Niño–Southern Oscillation (ENSO) and the Indian Ocean dipole (IOD) as potential predictors. To accomplish this, a two-step strategy is applied. In the first step, the discrete wavelet transform is coupled with a support vector regression model for streamflow prediction. The three key factors of mother wavelets, decomposition levels, and edge effects are considered in the wavelet decomposition phase when using the hybrid wavelet–support vector regression model (WS model). Different combinations of these factors form a variety of WS models with corresponding forecasts. The second step combines multiple candidate WS models with “good” performance via Bayesian model averaging. This integrates the predictive strengths of different candidate WS models, giving a realistic assessment of the predictive uncertainty. The new ensemble model is used to forecast daily and monthly streamflows at two sites in Dongjiang basin, southern China. The results show that the proposed BWS model consistently generates more reliable predictions for daily (lead times of 1–7 days) and monthly (lead times of 1–3 months) forecasts as compared with the best single-member WS models and the adaptive neuro-fuzzy inference system (ANFIS). Furthermore, the proposed BWS model provides detailed information about the predictive uncertainty.

An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam, India

Accurate and reliable prediction of the groundwater level variation is significant and essential in water resources management of a basin. The situation is complicated by the fact that the variation of groundwater level is highly nonlinear in nature because of interdependencies and uncertainties in the hydro-geological process. Models such as Artificial Neural Networks (ANN) and Support Vector Machine (SVM) have proved to be effective in modeling virtually any nonlinear function with a greater degree of accuracy. In recent times, combining several techniques to form a hybrid tool to improve the accuracy of prediction has become a common practice for various applications. This integrated method increases the efficiency of the model by combining the unique features of the constituent models to capture different patterns in the data. In the present study, an attempt is made to predict monthly groundwater level fluctuations using integrated wavelet and support vector machine modeling. The discrete wavelet transform with two coefficients (db2 wavelet) is adopted for decomposing the input data into wavelet series. These series are further used as input variables in different combinations for Support Vector Regression (SVR) model to forecast groundwater level fluctuations. The monthly data of precipitation, maximum temperature, mean temperature and groundwater depth for the period 2001–2012 are used as the input variables. The proposed Wavelet-Support Vector Regression (WA-SVR) model is applied to predict the groundwater level variations for three observation wells in the city of Visakhapatnam, India. The performance of the WA-SVR model is compared with SVR, ANN and also with the traditional Auto Regressive Integrated Moving Average (ARIMA) models. Results indicate that WA-SVR model gives better accuracy in predicting groundwater levels in the study area when compared to other models.

Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models

Summary Long-term drought forecasts can provide valuable information to help mitigate some of the consequences of drought. Data driven models are suitable forecast tools due to their minimal information requirements and rapid development times. This study compares the effectiveness of five data driven models for forecasting long-term (6 and 12 months lead time) drought conditions in the Awash River Basin of Ethiopia. The Standard Precipitation Index (SPI 12 and SPI 24) was forecasted using a traditional stochastic model (ARIMA) and compared to machine learning techniques such as artificial neural networks (ANNs), and support vector regression (SVR). In addition to these three model types, wavelet transforms were used to pre-process the inputs for ANN and SVR models to form WA-ANN and WA-SVR models; this is the first time that WA-SVR models have been explored and tested for long-term SPI forecasting. The performances of all models were compared using RMSE, MAE, R 2 and a measure of persistence. The forecast results indicate that the coupled wavelet neural network (WA-ANN) models were better than all the other models in this study for forecasting SPI 12 and SPI 24 values over lead times of 6 and 12 months in the Awash River Basin.

Traffic Flow Anomaly Detection Based on Wavelet Denoising and Support Vector Regression

In order to improve the speed and accuracy of traffic flow anomaly detection in real-time traffic system, we proposed an anomaly detection algorithm which is based on wavelet denoising and support vector regression. Firstly, we use wavelet transform to decompose and restructure the sampled data, and then apply support vector regression to data training. By fitting the obtained data, it can achieve dynamic prediction of traffic flow parameters. Through comparing the predictive values with the measured values of traffic flow parameters, we can achieve traffic anomaly detection. Experimental results show that the method proposed in this paper has a higher detection rate under the same false alarm rate.

Configuration for Predicting Travel-Time Using Wavelet Packets and Support Vector Regression

Travel-time prediction has gained significance over the years especially in urban areas due to increasing traffic congestion. In this paper, the basic building blocks of the travel-time prediction models are discussed, with a small review of the previous work. A model for the travel-time prediction on freeways based on wavelet packet decomposition and support vector regression (WDSVR) is proposed, which used the multi-resolution and equivalent frequency distribution ability of the wavelet transform to train the support vector machines. The results are compared against the classical support vector regression (SVR) method. Our results indicated that the wavelet reconstructed coefficient when used as an input to the support vector machine for regression performed better (with selected wavelets only), when compared with the support vector regression model (without wavelet decomposition) with a prediction horizon of 45 minutes and more. The data used in this paper was taken from the California Department of Transportation (Caltrans) of District 12 with a detector density of 2.73, experiencing daily peak hours except most weekends. The data was stored for a period of 214 days accumulated over 5-minute intervals over a distance of 9.13 miles. The results indicated MAPE ranging from 12.35% to 14.75% against the classical SVR method with MAPE ranging from 12.57% to 15.84% with a prediction horizon of 45 minutes to 1 hour. The basic criteria for selection of wavelet basis for preprocessing the inputs of support vector machines are also explored to filter the set of wavelet families for the WDSVR model. Finally, a configuration of travel-time prediction on freeways is presented with interchangeable prediction methods.

Real-time Lifetime Prediction Method Based on Wavelet Support Vector Regression and Fuzzy <I>c</I>-means Clustering

Real-time Lifetime Prediction Method Based on Wavelet Support Vector Regression and Fuzzy <I>c</I>-means Clustering

Overhead Line Fault Location Using Wavelet Packet Decomposition and Support Vector Regression

This paper realizes the fault location in overhead line by using wavelet packet decomposition (WPD) and support vector regression (SVR). All various types of faults at different locations and various fault inception angles on a 735kV-360km overhead line power system are used. The system only utilizes voltage signals with single-end measurements. WPD is used to extract distinctive features from 1/2 cycle of post fault signals after noises have been eliminated by low pass filter. A SVR is trained with features obtained from WPD and consequently used in precise location of fault on the transmission line. The simulation results show, fault location on transmission line can be determined rapidly and correctly irrespective of fault impedance.

WSVR-based fuzzy neural network with annealing robust algorithm for system identification

This paper proposes a fuzzy neural network (FNN) based on wavelet support vector regression (WSVR) approach for system identification, in which an annealing robust learning algorithm (ARLA) is adopted to adjust the parameters of the WSVR-based FNN (WSVR-FNN). In the WSVR-FNN, first, the WSVR method with a wavelet kernel function is used to determine the number of fuzzy rules and the initial parameters of FNN. After initialization, the adjustment for the parameters of FNNs is performed by the ARLA. Combining the self-learning ability of neural networks, the compact support of wavelet functions, the adaptive ability of fuzzy logic, and the robust learning capability of ARLA, the proposed FNN has the superiority among the several existed FNNs. To demonstrate the performance of the WSVR-FNN, two nonlinear dynamic plants and a chaotic system taken from the extant literature are considered to illustrate the system identification. From the simulation results, it shows that the proposed WSVR-FNN has the superiority over several presented FNNs even the number of training parameters is considerably small.

Tax Forecasting Based on Linear Discriminant Analysis-wavelet Support Vector Regression Algorithm

Abstract The accurate prediction for future tax values is the key to make the reasonable tax collection policy. In order to improve the prediction accuracy of wavelet support vector regression, linear discriminant analysis-wavelet support vector regression algorithm is proposed to predict future tax values in the paper. In the linear discriminant analysis-wavelet support vector regression algorithm,the linear discriminant analysis(LDA) is used to optimize wavelet support vector regression algorithm. Tax data of China from 1978 to 2001 are used as our experimental data. The comparison of tax prediction values between the LDA-WSVR model and traditional WSVR model with the 3~7 input nodes respectively shows that the prediction results for future tax of LDA-WSVR are better than those of traditional WSVR.