scholarly journals Estimation of Discharge and Total Water Level at Yedgaon Dam using Data Driven Techniques

2021 ◽  
Vol 1197 (1) ◽  
pp. 012021
Author(s):  
Preeti S. Kulkarni ◽  
Shreenivas Londhe ◽  
Nikita Sainkar ◽  
Sayali Rote
Keyword(s):  
Water Level ◽  
Correlation Coefficient ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Single Equation ◽  
Data Driven ◽  
Support Vector ◽  
Good Prediction ◽  
M5 Model Tree ◽  
Using Data

Abstract A reservoir operation planning using Data driven Techniques is gaining its momentum in hydrological area with good prediction and Estimation capabilities. The present work aims at using the 5 years data of Water Level to estimate the discharge and water level at the Yedgaon dam which is like pick up weir having its own yield and storage. It receives water from Dimbhe (though DLBC), Wadaj (through MLBC), Manikdoh (through river) and through Pimpalgaojoge (through river), in the Kukadi project of Maharashtra State, India. 4 different models were developed to estimate the water level using the Data Driven Techniques: M5 Model Tree, Support Vector Regression, Multi Gene Genetic Programming and Random Forest. The Accuracy of the developed models is assessed by the values of coefficient of correlation, coefficient of efficiency, mean absolute error and root mean squared error and comparison is done between actual values and Predicted values. The results indicated that the MGGP model was superior as compared to other techniques with correlation coefficient as 0.86 with an advantage of a single equation to estimate the water level.

Prediction of Apnea-Hypopnea Index Using Sound Data Collected by a Noncontact Device

2020 ◽  
Vol 162 (3) ◽  
pp. 392-399
Author(s):  
Jeong-Whun Kim ◽  
Taehoon Kim ◽  
Jaeyoung Shin ◽  
Kyogu Lee ◽  
Sunkyu Choi ◽  
...  
Keyword(s):  
Random Forest ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Sleep Stages ◽  
Support Vector ◽  
Apnea Hypopnea Index ◽  
Tertiary Referral Hospital ◽  
Obstructive Sleep ◽  
Using Data ◽  
Sound Features

Objective To predict the apnea-hypopnea index (AHI) in patients with obstructive sleep apnea (OSA) using data from breathing sounds recorded using a noncontact device during sleep. Study Design Prospective cohort study. Setting Tertiary referral hospital. Subject and Methods Audio recordings during sleep were performed using an air-conduction microphone during polysomnography. Breathing sounds recorded from all sleep stages were analyzed. After noise reduction preprocessing, the audio data were segmented into 5-second windows and sound features were extracted. Estimation of AHI by regression analysis was performed using a Gaussian process, support vector machine, random forest, and simple linear regression, along with 10-fold cross-validation. Results In total, 116 patients who underwent attended, in-laboratory, full-night polysomnography were included. Overall, random forest resulted in the highest performance with the highest correlation coefficient (0.83) and least mean absolute error (9.64 events/h) and root mean squared error (13.72 events/h). Other models resulted in somewhat lower but similar performances, with correlation coefficients ranging from 0.74 to 0.79. The estimated AHI tended to be underestimated as the severity of OSA increased. Regarding bias and precision, estimation performances in the severe OSA subgroup were the lowest, regardless of the model used. Among sound features, derivative of the area methods of moments of overall standard deviation demonstrated the highest correlation with AHI. Conclusion AHI was fairly predictable by using data from breathing sounds generated during sleep. The prediction model may be useful not only for prescreening but also for follow-up after treatment in patients with OSA.

Machine learning and Grad-Cam based vascular aging assessment using photoplethysmogram (Preprint)

2021 ◽  
Author(s):  
Hangsik Shin
Keyword(s):  
Machine Learning ◽  
Correlation Coefficient ◽  
Age Estimation ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Vascular Aging ◽  
Squared Error ◽  
Vascular Age

BACKGROUND Arterial stiffness due to vascular aging is a major indicator for evaluating cardiovascular risk. OBJECTIVE In this study, we propose a method of estimating age by applying machine learning to photoplethysmogram for non-invasive vascular age assessment. METHODS The machine learning-based age estimation model that consists of three convolutional layers and two-layer fully connected layers, was developed using segmented photoplethysmogram by pulse from a total of 752 adults aged 19–87 years. The performance of the developed model was quantitatively evaluated using mean absolute error, root-mean-squared-error, Pearson’s correlation coefficient, coefficient of determination. The Grad-Cam was used to explain the contribution of photoplethysmogram waveform characteristic in vascular age estimation. RESULTS Mean absolute error of 8.03, root mean squared error of 9.96, 0.62 of correlation coefficient, and 0.38 of coefficient of determination were shown through 10-fold cross validation. Grad-Cam, used to determine the weight that the input signal contributes to the result, confirmed that the contribution to the age estimation of the photoplethysmogram segment was high around the systolic peak. CONCLUSIONS The machine learning-based vascular aging analysis method using the PPG waveform showed comparable or superior performance compared to previous studies without complex feature detection in evaluating vascular aging. CLINICALTRIAL 2015-0104

scholarly journals Data-Driven Modelling of Smart Building Ventilation Subsystem

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Grigore Stamatescu ◽  
Iulia Stamatescu ◽  
Nicoleta Arghira ◽  
Ioana Fagarasan
Keyword(s):  
Data Mining ◽  
Data Streams ◽  
Data Driven ◽  
Support Vector ◽  
Commercial Building ◽  
Monitoring And Control ◽  
Smart Building ◽  
Building Ventilation ◽  
Using Data ◽  
Rich Data

Considering the advances in building monitoring and control through networks of interconnected devices, effective handling of the associated rich data streams is becoming an important challenge. In many situations, the application of conventional system identification or approximate grey-box models, partly theoretic and partly data driven, is either unfeasible or unsuitable. The paper discusses and illustrates an application of black-box modelling achieved using data mining techniques with the purpose of smart building ventilation subsystem control. We present the implementation and evaluation of a data mining methodology on collected data from over one year of operation. The case study is carried out on four air handling units of a modern campus building for preliminary decision support for facility managers. The data processing and learning framework is based on two steps: raw data streams are compressed using the Symbolic Aggregate Approximation method, followed by the resulting segments being input into a Support Vector Machine algorithm. The results are useful for deriving the behaviour of each equipment in various modi of operation and can be built upon for fault detection or energy efficiency applications. Challenges related to online operation within a commercial Building Management System are also discussed as the approach shows promise for deployment.

scholarly journals Multi-step streamflow forecasting using data-driven non-linear methods in contrasting climate regimes

2013 ◽  
Vol 16 (3) ◽  
pp. 671-689 ◽  
Author(s):  
Daniel J. Karran ◽  
Efrat Morin ◽  
Jan Adamowski
Keyword(s):  
Wavelet Transforms ◽  
Model Performance ◽  
Probability Of Detection ◽  
Data Driven ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Lead Times ◽  
Streamflow Forecasting ◽  
Non Linear ◽  
Using Data

Considering the popularity of using data-driven non-linear methods for forecasting streamflow, there has been no exploration of how well such models perform in climate regimes with differing hydrological characteristics, nor has the performance of these models, coupled with wavelet transforms, been compared for lead times of less than 1 month. This study compares the use of four different models, namely artificial neural networks (ANNs), support vector regression (SVR), wavelet-ANN, and wavelet-SVR in a Mediterranean, Oceanic, and Hemiboreal watershed. Model performance was tested for 1, 2 and 3 day forecasting lead times, measured by fractional standard error, the coefficient of determination, Nash–Sutcliffe model efficiency, multiplicative bias, probability of detection and false alarm rate. SVR based models performed best overall, but no one model outperformed the others in more than one watershed, suggesting that some models may be more suitable for certain types of data. Overall model performance varied greatly between climate regimes, suggesting that higher persistence and slower hydrological processes (i.e. snowmelt, glacial runoff, and subsurface flow) support reliable forecasting using daily and multi-day lead times.

scholarly journals Visualization & Prediction of COVID-19 Future Outbreak by Using Machine Learning

2021 ◽  
Vol 13 (3) ◽  
pp. 16-32
Author(s):  
Ahmed Hassan Mohammed Hassan ◽  
◽  
Arfan Ali Mohammed Qasem ◽  
Walaa Faisal Mohammed Abdalla ◽  
Omer H. Elhassan
Keyword(s):  
Machine Learning ◽  
Polynomial Regression ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Future Perspective ◽  
Support Vector ◽  
Squared Error ◽  
Vector Machines ◽  
The World ◽  
Negative Factors

Day by day, the accumulative incidence of COVID-19 is rapidly increasing. After the spread of the Corona epidemic and the death of more than a million people around the world countries, scientists and researchers have tended to conduct research and take advantage of modern technologies to learn machine to help the world to get rid of the Coronavirus (COVID-19) epidemic. To track and predict the disease Machine Learning (ML) can be deployed very effectively. ML techniques have been anticipated in areas that need to identify dangerous negative factors and define their priorities. The significance of a proposed system is to find the predict the number of people infected with COVID19 using ML. Four standard models anticipate COVID-19 prediction, which are Neural Network (NN), Support Vector Machines (SVM), Bayesian Network (BN) and Polynomial Regression (PR). The data utilized to test these models content of number of deaths, newly infected cases, and recoveries in the next 20 days. Five measures parameters were used to evaluate the performance of each model, namely root mean squared error (RMSE), mean squared error (MAE), mean absolute error (MSE), Explained Variance score and r2 score (R2). The significance and value of proposed system auspicious mechanism to anticipate these models for the current cenario of the COVID-19 epidemic. The results showed NN outperformed the other models, while in the available dataset the SVM performs poorly in all the prediction. Reference to our results showed that injuries will increase slightly in the coming days. Also, we find that the results give rise to hope due to the low death rate. For future perspective, case explanation and data amalgamation must be kept up persistently.

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

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.

scholarly journals ASSESSMENT OF RAINFALL-RUNOFF SIMULATION MODEL BASED ON SATELLITE ALGORITHM

2015 ◽  
Vol XL-1-W5 ◽  
pp. 529-541
Author(s):  
A. R. Nemati ◽  
M. Zakeri Niri ◽  
S. Moazami
Keyword(s):  
Conceptual Model ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Data Driven ◽  
Support Vector ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Research Fields ◽  
Hydrology And Water Resources ◽  
Trmm 3B42

Simulation of rainfall-runoff process is one of the most important research fields in hydrology and water resources. Generally, the models used in this section are divided into two conceptual and data-driven categories. In this study, a conceptual model and two data-driven models have been used to simulate rainfall-runoff process in Tamer sub-catchment located in Gorganroud watershed in Iran. The conceptual model used is HEC-HMS, and data-driven models are neural network model of multi-layer Perceptron (MLP) and support vector regression (SVR). In addition to simulation of rainfall-runoff process using the recorded land precipitation, the performance of four satellite algorithms of precipitation, that is, CMORPH, PERSIANN, TRMM 3B42 and TRMM 3B42RT were studied. In simulation of rainfall-runoff process, calibration and accuracy of the models were done based on satellite data. The results of the research based on three criteria of correlation coefficient (R), root mean square error (RMSE) and mean absolute error (MAE) showed that in this part the two models of SVR and MLP could perform the simulation of runoff in a relatively appropriate way, but in simulation of the maximum values of the flow, the error of models increased.

Modelling and Forecasting Portfolio Inflows

2022 ◽  
pp. 1427-1448
Author(s):  
Mogari I. Rapoo ◽  
Elias Munapo ◽  
Martin M. Chanza ◽  
Olusegun Sunday Ewemooje
Keyword(s):  
Banking Sector ◽  
Mean Squared Error ◽  
Model Performance ◽  
Absolute Error ◽  
Support Vector ◽  
Vector Autoregressive ◽  
Squared Error ◽  
Daily Data ◽  
Determining Factors ◽  
Push Factor

This chapter analyses efficiency of support vector regression (SVR), artificial neural networks (ANNs), and structural vector autoregressive (SVAR) models in terms of in-sample forecasting of portfolio inflows (PIs). Time series daily data sourced from Rand Merchant Bank (RMB) covering the period of 1st March 2004 to 1st February 2016 were used. Mean squared error, root mean squared error, mean absolute error, mean absolute squared error, and root mean scaled log error were used to evaluate model performance. The results showed that SVR has the best modelling performance when compared to others. In determining factors that affect allocation of PIs into South Africa based on SVAR, 69% of the variation was explained by pull factors while 9% was explained by push factor. Hence, SVR model is more accurate than ANNs. This chapter therefore recommends that banking sector particularly RMB should use machine learning technique in modelling PIs for a better financial solution.

scholarly journals Statistical modelling of agrometeorological time series by exponential smoothing

2016 ◽  
Vol 30 (1) ◽  
pp. 57-65 ◽  
Author(s):  
Małgorzata Murat ◽  
Iwona Malinowska ◽  
Holger Hoffmann ◽  
Piotr Baranowski
Keyword(s):  
Time Series ◽  
Mean Squared Error ◽  
Statistical Modelling ◽  
Absolute Error ◽  
Exponential Smoothing ◽  
Growth And Yield ◽  
Climate Change Scenarios ◽  
Autocorrelation Functions ◽  
Using Data

Abstract Meteorological time series are used in modelling agrophysical processes of the soil-plant-atmosphere system which determine plant growth and yield. Additionally, long-term meteorological series are used in climate change scenarios. Such studies often require forecasting or projection of meteorological variables, eg the projection of occurrence of the extreme events. The aim of the article was to determine the most suitable exponential smoothing models to generate forecast using data on air temperature, wind speed, and precipitation time series in Jokioinen (Finland), Dikopshof (Germany), Lleida (Spain), and Lublin (Poland). These series exhibit regular additive seasonality or non-seasonality without any trend, which is confirmed by their autocorrelation functions and partial autocorrelation functions. The most suitable models were indicated by the smallest mean absolute error and the smallest root mean squared error.

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