Statistical Analysis of Flow Parameters for the Graphical Simulation Outputs

System dynamics simulation software, in general, depicts graphical interpretations. The values of the parameters, on the other hand, are required for prediction. The goal of this research is to develop a novel multivariate model that can predict flow parameters while simulating flow under various scenarios. The project involves looking for variations in the streamline and constructing a new multivariate model for each elliptic cylinder system's velocity magnitude. Furthermore, the flow zones were split into three groups based on streamline behavior. As a result, utilizing simulation outputs, new models for flow zones are developed using linear and semiparametric regression. The best fitted model for each flow region was determined using mean square error (MSE), root of mean square error (RMSE), and mean absolute percentage error (MAPE). Based on the fitted smoothing curve of the velocity magnitude, a summary statistic and variability may be assessed. The presented models can be used to predict magnitude in any point of fluid flow using these models.

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Multi-State Load Demand Forecasting Using Hybridized Support Vector Regression Integrated with Optimal Design of Off-Grid Energy Systems—A Metaheuristic Approach

Processes ◽

10.3390/pr9071166 ◽

2021 ◽

Vol 9 (7) ◽

pp. 1166

Author(s):

Bashir Musa ◽

Nasser Yimen ◽

Sani Isah Abba ◽

Humphrey Hugh Adun ◽

Mustafa Dagbasi

Keyword(s):

Support Vector Regression ◽

Mean Square Error ◽

Prediction Accuracy ◽

Demand Forecasting ◽

Load Forecasting ◽

Coefficient Of Determination ◽

Percentage Error ◽

Support Vector ◽

Mean Square ◽

Hybrid Renewable Energy System

The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as indicators to evaluate the prediction accuracy of the algorithms. The results reveal that there is an increase in performance for both SVR-HHO and SVR-PSO over traditional SVR. SVR-HHO has the highest R2 values of 0.9951, 0.8963, 0.9951, and 0.9313, the lowest MSE values of 0.0002, 0.0070, 0.0002, and 0.0080, and the lowest MAPE values of 0.1311, 0.1452, 0.0599, and 0.1817, respectively, for Kano, Abuja, Niger, and Lagos State. The results of SVR-HHO also prove more advantageous over SVR-PSO in all the states concerning load forecasting skills. This paper also designed a hybrid renewable energy system (HRES) that consists of solar photovoltaic (PV) panels, wind turbines, and batteries. As inputs, the system used solar radiation, temperature, wind speed, and the predicted load demands by SVR-HHO in all the states. The system was optimized by using the PSO algorithm to obtain the optimal configuration of the HRES that will satisfy all constraints at the minimum cost.

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PEMODELAN JUMLAH PENDUDUK MISKIN DI JAWA TENGAH MENGGUNAKAN GEOGRAPHICALLY WEIGHTED REGRESSION (GWR)

Jurnal Litbang Sukowati Media Penelitian dan Pengembangan ◽

10.32630/sukowati.v4i2.122 ◽

2019 ◽

Vol 4 (2) ◽

pp. 10

Author(s):

Sugi Haryanto ◽

Gilang Axelline Andriani

Keyword(s):

Sustainable Development ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Geographically Weighted Regression ◽

Sustainable Development Goals ◽

Weighted Regression ◽

Percentage Error ◽

Mean Square ◽

Development Goals

Kemiskinan merupakan sesuatu yang sering menjadi ukuran keberhasilan kepemimpinan seorang kepala daerah. Selain itu juga sebagai tujuan pertama Sustainable Development Goals (SDG’s) untuk dientaskan. Kebijakan yang tepat sangat penting dibuat demi tercapainya tujuan pembangunan berkelanjutan. Pemodelan Geographically Weighted Regression (GWR) penting digunakan untuk menyusun model di setiap kabupaten/kota sebagai dasar pembuat kebijakan. Peubah yang digunakan dalam penelitian ini yaitu jumlah penduduk miskin, Indeks Pembangunan Manusia (IPM), Tingkat Pengangguran Terbuka (TPT), dan Upah Minimum Kabupaten/kota (UMK). Tujuan penelitian ini yaitu menentukan faktor-faktor yang berpengaruh terhadap jumlah penduduk miskin di setiap kabupaten/kota di Jawa Tengah. Pemodelan GWR lebih efektif dalam menggambarkan jumlah penduduk miskin di kabupaten/kota di Jawa Tengah tahun 2018. Hal ini ditunjukkan dengan adanya penigkatan nilai R2 serta penurunan nilai Root Mean Square Error (RMSE) dan Mean Absolute Percentage Error (MAPE).

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FORECASTING INTERNATIONAL TOURIST ARRIVALS IN MALAYSIA USING SARIMA AND HOLT-WINTERS MODEL

Journal of Tourism, Hospitality and Environment Management ◽

10.35631/jthem.518004 ◽

2020 ◽

Vol 5 (18) ◽

pp. 41-51

Author(s):

Norliana Mohd Lip ◽

Nur Shafiqah Jumery ◽

Fatin Amira Ahmad Termizi ◽

Nurul Atiqa Mulyadi ◽

Norhasnelly Anuar ◽

...

Keyword(s):

United Kingdom ◽

Mean Square Error ◽

Time Series Data ◽

Tourism Industry ◽

Series Data ◽

Percentage Error ◽

Mean Square ◽

Public And Private ◽

The United Kingdom ◽

Tourism Flows

Tourism can be described as the activities of visitors who make a visit to the main destination outside their usual environment for less than a year for any purpose. The tourism industry has become one of the influential sectors in global economic growth. Thus, tourism forecasting plays an important role in public and private sectors concerning future tourism flows. This study is an attempt to determine the best model in forecasting the international tourist's arrival in Malaysia based on Box-Jenkins and Holt-Winters model. The comparison of the accuracy of the techniques between Box-Jenkins SARIMA and Holt-Winters model was done based on the value of Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The secondary time series data were obtained from the Tourism Malaysia Department, which consists of a number of tourist arrivals from Singapore, Korea, and the United Kingdom from the year 2013 until the year 2017. The findings of this study suggest that the SARIMA and Holt-Winters model are suitable to be used in forecasting tourist arrivals. This study found that the Holt-Winters model is the appropriate model to forecast tourist arrivals from the United Kingdom (UK) and Korea. While SARIMA (1,1,1) (1,1,1)12 is the appropriate model for forecasting tourist arrivals from Singapore.

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The Analysis of Dynamic O/D Adjustment for Bicycle Traffic Demand Estimation with AIMSUN Simulation Model: A Case Study of Nakhon Sawan Municipality in Thailand

The Open Transportation Journal ◽

10.2174/1874447801812010352 ◽

2018 ◽

Vol 12 (1) ◽

pp. 352-365 ◽

Cited By ~ 1

Author(s):

Karn Chalermwongphan ◽

Prapatpong Upala

Keyword(s):

Simulation Model ◽

Root Mean Square ◽

Goodness Of Fit ◽

Simulation Software ◽

Mean Bias Error ◽

Percentage Error ◽

Bias Error ◽

Mean Square ◽

Goodness Of Fit Test ◽

Traffic Demand

Aim: This research aimed to present the process of estimating bicycle traffic demand in order to design bike routes that meet the daily transportation needs of the people in Nakhon Sawan Municipality. Methods: The primary and secondary traffic data were collected to develop a virtual traffic simulation model with the use of the AIMSUN simulation software. The model validation method was carried out to adjust the origin and destination survey data (O/D matrix) by running dynamic O/D adjustment. The 99 replication scenarios were statistically examined and assessed using the goodness-of-fit test. The 9 measures, which were examined, included: 1) Root Mean Square Error (RMSE), 2) Root Mean Square Percentage Error (RMSPE%), 3) Mean Absolute Deviation (MAD), 4) Mean Bias Error (MBE), 5) Mean Percentage Error (MPE%), 6) Mean Absolute Percentage Error (MAPE%), 7) Coefficient of Determination (R2), 8) GEH Statistic (GEH), and 9) Thiel’s U Statistic (Theil’s U). Results: The resulting statistical values were used to determine the acceptable ranges according to the acceptable indicators of each factor. Conclusion: It was found that there were only 8 scenarios that met the evaluation criteria. The selection and ranking process was consequently carried out using the multi-factor scoring method, which could eliminate errors that might arise from applying only one goodness-of-fit test measure.

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Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach

Applied Sciences ◽

10.3390/app10207079 ◽

2020 ◽

Vol 10 (20) ◽

pp. 7079

Author(s):

Elias Eze ◽

Tahmina Ajmal

Keyword(s):

Dissolved Oxygen ◽

Mean Square Error ◽

Short Term Memory ◽

Sensor Data ◽

Percentage Error ◽

Forecasting Model ◽

Mean Square ◽

Intrinsic Mode Functions ◽

Short Term ◽

Do Concentration

Dissolved oxygen (DO) concentration is a vital parameter that indicates water quality. We present here DO short term forecasting using time series analysis on data collected from an aquaculture pond. This can provide the basis of data support for an early warning system, for an improved management of the aquaculture farm. The conventional forecasting approaches are commonly characterized by low accuracy and poor generalization problems. In this article, we present a novel hybrid DO concentration forecasting method with ensemble empirical mode decomposition (EEMD)-based LSTM (long short-term memory) neural network (NN). With this method, first, the sensor data integrity is improved through linear interpolation and moving average filtering methods of data preprocessing. Next, the EEMD algorithm is applied to decompose the original sensor data into multiple intrinsic mode functions (IMFs). Finally, the feature selection is used to carefully select IMFs that strongly correlate with the original sensor data, and integrate into both inputs for the NN. The hybrid EEMD-based LSTM forecasting model is then constructed. The performance of this proposed model in training and validation sets was compared with the observed real sensor data. To obtain the exact evaluation accuracy of the forecasted results of the hybrid EEMD-based LSTM forecasting model, four statistical performance indices were adopted: mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Results are presented for the short term (12-h) and the long term (1-month) that are encouraging, indicating suitability of this technique for forecasting DO values.

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Indoor air quality prediction using optimizers: A comparative study

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-200259 ◽

2020 ◽

Vol 39 (5) ◽

pp. 7053-7069

Author(s):

Jagriti Saini ◽

Maitreyee Dutta ◽

Gonçalo Marques

Keyword(s):

Air Quality ◽

Comparative Study ◽

Indoor Air Quality ◽

Mean Square Error ◽

Indoor Air ◽

Well Being ◽

Percentage Error ◽

Cow Dung ◽

Mean Square ◽

Human Beings

Indoor air pollution (IAP) has become a serious concern for developing countries around the world. As human beings spend most of their time indoors, pollution exposure causes a significant impact on their health and well-being. Long term exposure to particulate matter (PM) leads to the risk of chronic health issues such as respiratory disease, lung cancer, cardiovascular disease. In India, around 200 million people use fuel for cooking and heating needs; out of which 0.4% use biogas; 0.1% electricity; 1.5% lignite, coal or charcoal; 2.9% kerosene; 8.9% cow dung cake; 28.6% liquified petroleum gas and 49% use firewood. Almost 70% of the Indian population lives in rural areas, and 80% of those households rely on biomass fuels for routine needs. With 1.3 million deaths per year, poor air quality is the second largest killer in India. Forecasting of indoor air quality (IAQ) can guide building occupants to take prompt actions for ventilation and management on useful time. This paper proposes prediction of IAQ using Keras optimizers and compares their prediction performance. The model is trained using real-time data collected from a cafeteria in the Chandigarh city using IoT sensor network. The main contribution of this paper is to provide a comparative study on the implementation of seven Keras Optimizers for IAQ prediction. The results show that SGD optimizer outperforms other optimizers to ensure adequate and reliable predictions with mean square error = 0.19, mean absolute error = 0.34, root mean square error = 0.43, R2 score = 0.999555, mean absolute percentage error = 1.21665%, and accuracy = 98.87%.

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Using Multi-Temporal MODIS NDVI Data to Monitor Tea Status and Forecast Yield: A Case Study at Tanuyen, Laichau, Vietnam

Remote Sensing ◽

10.3390/rs12111814 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1814

Author(s):

Phamchimai Phan ◽

Nengcheng Chen ◽

Lei Xu ◽

Zeqiang Chen

Keyword(s):

Remote Sensing ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Vegetation Index ◽

Coefficient Of Determination ◽

Percentage Error ◽

Support Vector ◽

Mean Square ◽

Multi Temporal

Tea is a cash crop that improves the quality of life for people in the Tanuyen District of Laichau Province, Vietnam. Tea yield, however, has stagnated in recent years, due to changes in temperature, precipitation, the age of the tea bushes, and diseases. Developing an approach for monitoring tea bushes by remote sensing and Geographic Information Systems (GIS) might be a way to alleviate this problem. Using multi-temporal remote sensing data, the paper details an investigation of the changes in tea health and yield forecasting through the normalized difference vegetation index (NDVI). In this study, we used NDVI as a support tool to demonstrate the temporal and spatial changes in NDVI through the extract tea NDVI value and calculate the mean NDVI value. The results of the study showed that the minimum NDVI value was 0.42 during January 2013 and February 2015 and 2016. The maximum NDVI value was in August 2015 and June 2017. We indicate that the linear relationship between NDVI value and mean temperature was strong with R 2 = 0.79 Our results confirm that the combination of meteorological data and NDVI data can achieve a high performance of yield prediction. Three models to predict tea yield were conducted: support vector machine (SVM), random forest (RF), and the traditional linear regression model (TLRM). For period 2009 to 2018, the prediction tea yield by the RF model was the best with a R 2 = 0.73 , by SVM it was 0.66, and 0.57 with the TLRM. Three evaluation indicators were used to consider accuracy: the coefficient of determination ( R 2 ), root-mean-square error (RMSE), and percentage error of tea yield (PETY). The highest accuracy for the three models was in 2015 with a R 2 ≥ 0.87, RMSE < 50 kg/ha, and PETY less 3% error. In the other years, the prediction accuracy was higher in the SVM and RF models. Meanwhile, the RF algorithm was better than PETY (≤10%) and the root mean square error for this algorithm was significantly less (≤80 kg/ha). RMSE and PETY showed relatively good values in the TLRM model with a RMSE from 80 to 100 kg/ha and a PETY from 8 to 15%.

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Comparing Four Types Methods for Karst NDVI Prediction Based on Machine Learning

Atmosphere ◽

10.3390/atmos12101341 ◽

2021 ◽

Vol 12 (10) ◽

pp. 1341

Author(s):

Yuju Ma ◽

Liyuan Zuo ◽

Jiangbo Gao ◽

Qiang Liu ◽

Lulu Liu

Keyword(s):

Neural Network ◽

Mean Square Error ◽

Human Activities ◽

Vegetation Index ◽

Terrestrial Ecosystem ◽

Coefficient Of Determination ◽

Percentage Error ◽

Support Vector ◽

Mean Square ◽

Response Factors

As a link for energy transfer between the land and atmosphere in the terrestrial ecosystem, karst vegetation plays an important role. Karst vegetation is not only affected by environmental factors but also by intense human activities. The nonlinear characteristics of vegetation growth are induced by the interaction mechanism of these factors. Previous studies of this relationship were not comprehensive, and it is necessary to further explore it using a suitable method. In this study, we selected climate, human activities, topography, and soil texture as the response factors; a nonlinear relationship model between the karst normalized difference vegetation index (NDVI) and these factors was established by applying a back propagation neural network (BPNN), a radial basis function neural network (RBFNN), the random forest (RF) algorithm, and support vector regression (SVR); and then, the karst NDVI was predicted. The coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the obtained results were calculated, and the mean R2 values of the BPNN, RBFNN, RF, and SVR models were determined to be 0.77, 0.86, 0.89, and 0.91, respectively. Compared with the BPNN, RBFNN, and RF models, the SVR model had the lowest errors, with mean MSE, RMSE, and MAPE values of 0.001, 0.02, and 2.77, respectively. The results show that the BPNN, RBFNN, RF, and SVR models are within acceptable ranges for karst NDVI prediction, but the overall performance of the SVR model is the best, and it is more suitable for karst vegetation prediction.

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ECG Signal Modeling Using Volatility Properties: Its Application in Sleep Apnea Syndrome

Journal of Healthcare Engineering ◽

10.1155/2021/4894501 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Maryam Faal ◽

Farshad Almasganj

Keyword(s):

Sleep Apnea ◽

Mean Square Error ◽

Arima Model ◽

Sleep Apnea Syndrome ◽

Percentage Error ◽

Ecg Signal ◽

Mean Square ◽

Ecg Signals ◽

Mean And Variance ◽

Apnea Syndrome

This study presents and evaluates the mathematical model to estimate the mean and variance of single-lead ECG signals in sleep apnea syndrome. Our objective is to use the volatility property of the ECG signal for modeling. ECG signal is a stochastic signal whose mean and variance are time-varying. So, we propose to decompose this nonstationarity into two additive components; a homoscedastic Autoregressive Integrated Moving Average (ARIMA) and a heteroscedastic time series in terms of Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH), where the former captures the linearity property and the latter the nonlinear characteristics of the ECG signal. First, ECG signals are segmented into one-minute segments. The heteroskedasticity property is then examined through various tests such as the ARCH/GARCH test, kurtosis, skewness, and histograms. Next, the ARIMA model is applied to signals as a linear model and EGARCH as a nonlinear model. The appropriate orders of models are estimated by using the Bayesian Information Criterion (BIC). We assess the effectiveness of our model in terms of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The data in this article is obtained from the Physionet Apnea-ECG database. Results show that the ARIMA-EGARCH model performs better than other models for modeling both apneic and normal ECG signals in sleep apnea syndrome.

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Forecasting Electricity Consumption Using Time Series Model

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.30.22124 ◽

2018 ◽

Vol 7 (4.30) ◽

pp. 218 ◽

Cited By ~ 1

Author(s):

Y.W. Lee ◽

K.G. Tay ◽

Y.Y. Choy

Keyword(s):

Mean Square Error ◽

Future Development ◽

Linear Trend ◽

Moving Average ◽

Demand Forecasting ◽

Electricity Consumption ◽

Electricity Sector ◽

Percentage Error ◽

Mean Square ◽

Monthly Electricity Consumption

Electricity demand forecasting is important for planning and facility expansion in the electricity sector. Accurate forecasts can save operating and maintenance costs, increased the reliability of power supply and delivery system, and correct decisions for future development. Universiti Tun Hussein Onn Malaysia (UTHM) which is a developing university in Malaysia has been growing since its formation in 1993. Thus, it is important for UTHM to forecast the electricity consumption in future so that the future development can be determined. Hence, UTHM electricity consumption was forecasted by using the simple moving average (SMA), weighted moving average (WMA), simple exponential smoothing (SES), Holt linear trend (HL), Holt-Winters (HW) and centered moving average (CMA). The monthly electricity consumption from January 2011 to December 2017 was used to forecast January to December 2018 monthly electricity consumption. HW gives the smallest mean absolute error (MAE) and mean absolute percentage error (MAPE), while CMA produces the lowest mean square error (MSE) and root mean square error (RMSE). As there is a decreasing population of UTHM after the moving of four faculties to Pagoh and HW forecasted trend is decreasing whereas CMA is increasing, hence HW might forecast better in this problem.

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