Assessment of Hellwig Method for Predictors’ Selection in Groundwater Level Time Series Forecasting

Effective groundwater planning and management should be based on the prediction of available water volume. The complex nature of groundwater systems makes this complicated and requires the use of complex methods. Data-driven models using computational intelligence are becoming increasingly popular in that field. The key issue in predictive modelling is the selection of input variables. Wrocław-Osobowice irrigation fields were a wastewater treatment plant until 2013. The monitoring of groundwater levels is being continued to assess the water relations in that area after the end of their exploitation. The aim of the study was to assess the Hellwig method for predictors’ selection in groundwater level forecasting with support vector regression models. Data covered the daily time series of groundwater level in the period 2015–2019. Obtained models with a root mean squared error (RMSE) of 0.024–0.292 m and r2 of 0.7–0.9 were considered as high quality. Moreover, they showed good prediction ability for high as well as low groundwater values. Additionally, the proposed method is simple, and its implementation only requires access to groundwater level measurement data. It may be useful in groundwater management and planning in terms of actual climate change and threat of water deficits.

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Fast Prediction with Sparse Multikernel LS-SVR Using Multiple Relevant Time Series and Its Application in Avionics System

Mathematical Problems in Engineering ◽

10.1155/2015/460514 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10

Author(s):

Yang M. Guo ◽

Pei He ◽

Xiang T. Wang ◽

Ya F. Zheng ◽

Chong Liu ◽

...

Keyword(s):

Time Series ◽

Prediction Model ◽

Prediction Accuracy ◽

Mean Squared Error ◽

Computation Time ◽

Support Vector ◽

Good Prediction ◽

Trend Prediction ◽

Good Prediction Accuracy ◽

Fast Prediction

Health trend prediction is critical to ensure the safe operation of highly reliable systems. However, complex systems often present complex dynamic behaviors and uncertainty, which makes it difficult to develop a precise physical prediction model. Therefore, time series is often used for prediction in this case. In this paper, in order to obtain better prediction accuracy in shorter computation time, we propose a new scheme which utilizes multiple relevant time series to enhance the completeness of the information and adopts a prediction model based on least squares support vector regression (LS-SVR) to perform prediction. In the scheme, we apply two innovative ways to overcome the drawbacks of the reported approaches. One is to remove certain support vectors by measuring the linear correlation to increase sparseness of LS-SVR; the other one is to determine the linear combination weights of multiple kernels by calculating the root mean squared error of each basis kernel. The results of prediction experiments indicate preliminarily that the proposed method is an effective approach for its good prediction accuracy and low computation time, and it is a valuable method in applications.

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Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction

Journal of Water and Land Development ◽

10.1515/jwld-2017-0012 ◽

2017 ◽

Vol 32 (1) ◽

pp. 103-112 ◽

Cited By ~ 22

Author(s):

Basant Yadav ◽

Sudheer Ch ◽

Shashi Mathur ◽

Jan Adamowski

Keyword(s):

Soft Computing ◽

Groundwater Level ◽

Groundwater Resources ◽

Support Vector ◽

Complex Nature ◽

Data Set ◽

Groundwater Levels ◽

Soft Computing Techniques ◽

Learning Machine ◽

The Comparative Study

Abstract Fluctuation of groundwater levels around the world is an important theme in hydrological research. Rising water demand, faulty irrigation practices, mismanagement of soil and uncontrolled exploitation of aquifers are some of the reasons why groundwater levels are fluctuating. In order to effectively manage groundwater resources, it is important to have accurate readings and forecasts of groundwater levels. Due to the uncertain and complex nature of groundwater systems, the development of soft computing techniques (data-driven models) in the field of hydrology has significant potential. This study employs two soft computing techniques, namely, extreme learning machine (ELM) and support vector machine (SVM) to forecast groundwater levels at two observation wells located in Canada. A monthly data set of eight years from 2006 to 2014 consisting of both hydrological and meteorological parameters (rainfall, temperature, evapotranspiration and groundwater level) was used for the comparative study of the models. These variables were used in various combinations for univariate and multivariate analysis of the models. The study demonstrates that the proposed ELM model has better forecasting ability compared to the SVM model for monthly groundwater level forecasting.

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A self-adapting multi-LSTM ensemble regression mode for failure prediction of transmission line network from wireless mesh nodes’ data

Journal of Computational Methods in Sciences and Engineering ◽

10.3233/jcm-204550 ◽

2020 ◽

pp. 1-9

Author(s):

Hongbin Sun ◽

Mingjun Liu ◽

Zhejun Qing ◽

Chandler Miller

Keyword(s):

Time Series ◽

Transmission Line ◽

Time Series Data ◽

Failure Prediction ◽

Series Data ◽

Good Prediction ◽

Similar Time ◽

Prediction Ability ◽

Wireless Mesh ◽

Ensemble Regression

Transmission lines’ condition monitoring is an important part of smart grid construction. To ensure fast and efficient transmission of data, many mash-based wireless networks devices are adopted to collect status information. Since these nodes are exposed to the natural environment, vulnerable to damage, so it is very necessary to be predicting nodes’ fault. However, these mesh nodes are affected by a variety of complex and time-series factors, and traditional models are difficult to achieve effective failure prediction. To solve this problem, this paper proposes a self-adapting multi-LSTM ensemble regression model for transmission line network’s wireless mesh node failure prediction (MLSTM-FP), through establishes the corresponding relationship between similar time factors and LSTMs, the proposed model can realize multi time series data self-adapting and accurate failure prediction of transmission line network’s wireless mesh nodes, The experimental results show that the proposed method has a good prediction ability than traditional methods.

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Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning

Computational Intelligence and Neuroscience ◽

10.1155/2020/7980434 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Hye-Jin Kim ◽

Sung Min Park ◽

Byung Jin Choi ◽

Seung-Hyun Moon ◽

Yong-Hyuk Kim

Keyword(s):

Machine Learning ◽

Time Series ◽

Quality Control ◽

Mean Squared Error ◽

Machine Learning Algorithms ◽

Support Vector ◽

Weather Element ◽

Applied Machine Learning ◽

Squared Error ◽

Atmospheric Data

We propose three quality control (QC) techniques using machine learning that depend on the type of input data used for training. These include QC based on time series of a single weather element, QC based on time series in conjunction with other weather elements, and QC using spatiotemporal characteristics. We performed machine learning-based QC on each weather element of atmospheric data, such as temperature, acquired from seven types of IoT sensors and applied machine learning algorithms, such as support vector regression, on data with errors to make meaningful estimates from them. By using the root mean squared error (RMSE), we evaluated the performance of the proposed techniques. As a result, the QC done in conjunction with other weather elements had 0.14% lower RMSE on average than QC conducted with only a single weather element. In the case of QC with spatiotemporal characteristic considerations, the QC done via training with AWS data showed performance with 17% lower RMSE than QC done with only raw data.

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Intelligent Localization and Quantitative Evaluation of Anterior Talofibular Ligament Injury using Magnetic Resonance Imaging of Ankle

10.21203/rs.3.rs-579237/v1 ◽

2021 ◽

Author(s):

Wen Yan ◽

Xianghong Meng ◽

Jinglai Sun ◽

Hui Yu ◽

Zhi Wang

Keyword(s):

Ankle Injuries ◽

Ligament Injury ◽

Lateral Ligament ◽

Support Vector ◽

Anterior Talofibular Ligament ◽

Good Prediction ◽

Feature Points ◽

Characteristic Analysis ◽

Prediction Ability ◽

Mean Values

Abstract Background: There is a high incidence of injury to the lateral ligament of the ankle in daily living and sports activities. The anterior talofibular ligament (ATFL) is the most frequent types of ankle injuries. It is of great clinical significance to achieve intelligent localization and injury evaluation of ATFL due to its vulnerability.Methods: According to the specific characteristics of bones in different slices, the key slice was extracted by image segmentation and characteristic analysis. Then, the talus and fibula in the key slice were segmented by distance regularized level set evolution (DRLSE), and the curvature of their contour pixels was calculated to find useful feature points including the neck of talus, the inner edge of fibula, and the outer edge of fibula. ATFL area can be located using these feature points so as to quantify its first-order gray features and second-order texture features. Support vector machine (SVM) was performed for evaluation of ATFL injury.Results: Data were collected retrospectively from 158 patients who underwent MRI, and were divided into normal (68) and tear (90) group. The positioning accuracy and Dice coefficient were used to measure the performance of ATFL localization, and the mean values are 87.7% and 77.1%, respectively, which is helpful for the following feature extraction. SVM gave a good prediction ability with accuracy of 93.8%, sensitivity of 88.9%, specificity of 100%, precision of 100%, and F1 score of 94.2% in the test set.Conclusion: Experimental results indicate that the proposed method is reliable in diagnosing ATFL injury. This study may provide a potentially viable method for aided clinical diagnoses of some ligament injury.

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Support Vector Machine to Predict Electricity Consumption in the Energy Management Laboratory

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v5i3.2947 ◽

2021 ◽

Vol 5 (3) ◽

pp. 466-473

Author(s):

Azam Zamhuri Fuadi ◽

Irsyad Nashirul Haq ◽

Edi Leksono

Keyword(s):

Support Vector Machine ◽

Energy Management ◽

Mean Squared Error ◽

Measurement Data ◽

Electricity Consumption ◽

Absolute Error ◽

Support Vector ◽

Mean Error ◽

Squared Error ◽

Electrical Loads

Predicted electricity consumption is needed to perform energy management. Electricity consumption prediction is also very important in the development of intelligent power grids and advanced electrification network information. we implement a Support Vector Machine (SVM) to predict electrical loads and results compared to measurable electrical loads. Laboratory electrical loads have their own characteristics when compared to residential, commercial, or industrial, we use electrical load data in energy management laboratories to be used to be predicted. C and Gamma as searchable parameters use GridSearchCV to get optimal SVM input parameters. Our prediction data is compared to measurement data and is searched for accuracy based on RMSE (Root Square Mean Error), MAE (Mean Absolute Error) and MSE (Mean Squared Error) values. Based on this we get the optimal parameter values C 1e6 and Gamma 2.97e-07, with the result RSME (Root Square Mean Error) ; 0.37, MAE (meaning absolute error); 0.21 and MSE (Mean Squared Error); 0.14.

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Intelligent localization and quantitative evaluation of anterior talofibular ligament injury using magnetic resonance imaging of ankle

BMC Medical Imaging ◽

10.1186/s12880-021-00660-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Wen Yan ◽

Xianghong Meng ◽

Jinglai Sun ◽

Hui Yu ◽

Zhi Wang

Keyword(s):

Ankle Injuries ◽

Ligament Injury ◽

Lateral Ligament ◽

Support Vector ◽

Anterior Talofibular Ligament ◽

Good Prediction ◽

Feature Points ◽

Characteristic Analysis ◽

Prediction Ability ◽

Mean Values

Abstract Background There is a high incidence of injury to the lateral ligament of the ankle in daily living and sports activities. The anterior talofibular ligament (ATFL) is the most frequent types of ankle injuries. It is of great clinical significance to achieve intelligent localization and injury evaluation of ATFL due to its vulnerability. Methods According to the specific characteristics of bones in different slices, the key slice was extracted by image segmentation and characteristic analysis. Then, the talus and fibula in the key slice were segmented by distance regularized level set evolution (DRLSE), and the curvature of their contour pixels was calculated to find useful feature points including the neck of talus, the inner edge of fibula, and the outer edge of fibula. ATFL area can be located using these feature points so as to quantify its first-order gray features and second-order texture features. Support vector machine (SVM) was performed for evaluation of ATFL injury. Results Data were collected retrospectively from 158 patients who underwent MRI, and were divided into normal (68) and tear (90) group. The positioning accuracy and Dice coefficient were used to measure the performance of ATFL localization, and the mean values are 87.7% and 77.1%, respectively, which is helpful for the following feature extraction. SVM gave a good prediction ability with accuracy of 93.8%, sensitivity of 88.9%, specificity of 100%, precision of 100%, and F1 score of 94.2% in the test set. Conclusion Experimental results indicate that the proposed method is reliable in diagnosing ATFL injury. This study may provide a potentially viable method for aided clinical diagnoses of some ligament injury.

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Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach

Sensors ◽

10.3390/s20226671 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6671

Author(s):

Sharif Hossain ◽

Christopher W.K. Chow ◽

Guna A. Hewa ◽

David Cook ◽

Martin Harris

Keyword(s):

Machine Learning ◽

Drinking Water ◽

Real Time ◽

Learning Algorithm ◽

Measurement Data ◽

Treatment Plant ◽

Water Treatment Plant ◽

Kernel Functions ◽

Support Vector ◽

Organic Nitrate

The spectra fingerprint of drinking water from a water treatment plant (WTP) is characterised by a number of light-absorbing substances, including organic, nitrate, disinfectant, and particle or turbidity. Detection of disinfectant (monochloramine) can be better achieved by separating its spectra from the combined spectra. In this paper, two major focuses are (i) the separation of monochloramine spectra from the combined spectra and (ii) assessment of the application of the machine learning algorithm in real-time detection of monochloramine. The support vector regression (SVR) model was developed using multi-wavelength ultraviolet-visible (UV-Vis) absorbance spectra and online amperometric monochloramine residual measurement data. The performance of the SVR model was evaluated by using four different kernel functions. Results show that (i) particles or turbidity in water have a significant effect on UV-Vis spectral measurement and improved modelling accuracy is achieved by using particle compensated spectra; (ii) modelling performance is further improved by compensating the spectra for natural organic matter (NOM) and nitrate (NO3) and (iii) the choice of kernel functions greatly affected the SVR performance, especially the radial basis function (RBF) appears to be the highest performing kernel function. The outcomes of this research suggest that disinfectant residual (monochloramine) can be measured in real time using the SVR algorithm with a precision level of ± 0.1 mg L−1.

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FPGA-Based Implementation of a Multilayer Perceptron Suitable for Chaotic Time Series Prediction

Technologies ◽

10.3390/technologies6040090 ◽

2018 ◽

Vol 6 (4) ◽

pp. 90 ◽

Cited By ~ 12

Author(s):

Ana Pano-Azucena ◽

Esteban Tlelo-Cuautle ◽

Sheldon Tan ◽

Brisbane Ovilla-Martinez ◽

Luis de la Fraga

Keyword(s):

Time Series ◽

Multilayer Perceptron ◽

Time Series Data ◽

Mean Squared Error ◽

Time Series Prediction ◽

Chaotic Time Series ◽

Series Data ◽

Support Vector ◽

Maximum Lyapunov Exponent ◽

Inference System

Many biological systems and natural phenomena exhibit chaotic behaviors that are saved in time series data. This article uses time series that are generated by chaotic oscillators with different values of the maximum Lyapunov exponent (MLE) to predict their future behavior. Three prediction techniques are compared, namely: artificial neural networks (ANNs), the adaptive neuro-fuzzy inference system (ANFIS) and least-squares support vector machines (SVM). The experimental results show that ANNs provide the lowest root mean squared error. That way, we introduce a multilayer perceptron that is implemented using a field-programmable gate array (FPGA) to predict experimental chaotic time series.

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Traffic Speed Time Series Short Term Forecasting Using Aggregated Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.587-589.2057 ◽

2014 ◽

Vol 587-589 ◽

pp. 2057-2062

Author(s):

Jian Gu ◽

Shu Yan Chen

Keyword(s):

Time Series ◽

Support Vector Machine ◽

Least Square ◽

Support Vector ◽

Short Term ◽

Support Vector Machine Regression ◽

Prediction Ability ◽

Aggregated Model ◽

Traffic Speed ◽

Short Term Forecasting

This paper integrated superiority from time series model and least square support vector machine regression model with data aggregation for traffic speed short term forecasting. Based on the results of traffic data variations analysis, the practicability that speed data can be aggregated to several periods was confirmed, and aggregated model can be developed to forecast the speed with auto regression (AR) model and support vector machine regression (SVR). Then the speed data in case study were integrated to 4 periods at the location of Remote Traffic Microwave Sensors (RTMS) 2047 on 2ndRing Road Expressway in Beijing. Arguments with coefficients from AR models then act as the independent variables of LSSVR in aggregated model. Short term traffic speed was predicted by aggregated model, and the results indicated that taking advantages of time periods variation rule inside the aggregated model would help save the model running time cost under the premise of accuracy with better prediction ability than LSSVR in certain conditions.

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