scholarly journals Radar Echo Spatiotemporal Sequence Prediction Using an Improved ConvGRU Deep Learning Model

Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 88
Author(s):  
Wei He ◽  
Taisong Xiong ◽  
Hao Wang ◽  
Jianxin He ◽  
Xinyue Ren ◽  
...  
Keyword(s):  
Deep Learning ◽  
Input Data ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Weather Prediction ◽  
Absolute Error ◽  
Sequence Prediction ◽  
Squared Error ◽  
Radar Echo

Precipitation nowcasting is extremely important in disaster prevention and mitigation, and can improve the quality of meteorological forecasts. In recent years, deep learning-based spatiotemporal sequence prediction models have been widely used in precipitation nowcasting, obtaining better prediction results than numerical weather prediction models and traditional radar echo extrapolation results. Because existing deep learning models rarely consider the inherent interactions between the model input data and the previous output, model prediction results do not sufficiently meet the actual forecast requirement. We propose a Modified Convolutional Gated Recurrent Unit (M-ConvGRU) model that performs convolution operations on the input data and previous output of a GRU network. Moreover, this adopts an encoder–forecaster structure to better capture the characteristics of spatiotemporal correlation in radar echo maps. The results of multiple experiments demonstrate the effectiveness of the proposed model. The balanced mean absolute error (B-MAE) and balanced mean squared error (B-MSE) of M-ConvGRU are slightly lower than Convolutional Long Short-Term Memory (ConvLSTM), but the mean absolute error (MAE) and mean squared error (MSE) of M-ConvGRU are 6.29% and 10.25% lower than ConvLSTM, and the prediction accuracy and prediction performance for strong echo regions were also improved.

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 Designing accurate emulators for scientific processes using calibration-driven deep models

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Jayaraman J. Thiagarajan ◽  
Bindya Venkatesh ◽  
Rushil Anirudh ◽  
Peer-Timo Bremer ◽  
Jim Gaffney ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Heterogeneous Data ◽  
Small Data ◽  
Machine Learning Methods ◽  
Squared Error ◽  
Noise Structure ◽  
The Mean ◽  
Modern Machine

Abstract Predictive models that accurately emulate complex scientific processes can achieve speed-ups over numerical simulators or experiments and at the same time provide surrogates for improving the subsequent analysis. Consequently, there is a recent surge in utilizing modern machine learning methods to build data-driven emulators. In this work, we study an often overlooked, yet important, problem of choosing loss functions while designing such emulators. Popular choices such as the mean squared error or the mean absolute error are based on a symmetric noise assumption and can be unsuitable for heterogeneous data or asymmetric noise distributions. We propose Learn-by-Calibrating, a novel deep learning approach based on interval calibration for designing emulators that can effectively recover the inherent noise structure without any explicit priors. Using a large suite of use-cases, we demonstrate the efficacy of our approach in providing high-quality emulators, when compared to widely-adopted loss function choices, even in small-data regimes.

Electricity Consumption Analysis Using Demographic Variables Case Study – Nakhonratchasima, Thailand

2013 ◽  
Vol 734-737 ◽  
pp. 1679-1682
Author(s):  
Sureeporn Meehom ◽  
Nopphadon Khodpun
Keyword(s):  
High Speed ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Electricity Consumption ◽  
Absolute Error ◽  
Demographic Variables ◽  
Resources Management ◽  
Squared Error ◽  
High Speed Diesel

Electricity energy is vital in social and economic for nation development. The electricity consumption analysis plays an important role for sustainable energy and electricity resources management in the future. In this paper, the influence of demographical variables on the annual electricity consumption in Nakhonratchasima has been investigated by multiple regression analysis. It is founded that the electricity consumption correlated with four demographic variables, which are the number of electricity consumers, the amount of high speed diesel usages, the number of industrial factory and the number of employed labor force. The historical electricity consumption and all variables for the period 20022010 have been analyzed in 8 models for electricity prediction in 2011. In conclusion, the effective model has been selected by comparison of adjusted R2, mean absolute error (MAE) and root mean squared error (RMSE) of the proposed models. Model 8 is acceptable in relation to electricity consumption analysis with adjusted-R2, RMSE and MAE equal to 0.9980, 0.7540% and 0.6095% respectively. The results indicate that the model using all four variables has strong ability to predict future annual electricity consumption with 4,195,837,877 kWh in 2011.

On the mean squared error, the mean absolute error and the like

1999 ◽  
Vol 28 (8) ◽  
pp. 1813-1822 ◽  
Author(s):  
Shaul K. Bar-Lev ◽  
Benzion Boukai ◽  
Peter Enis
Keyword(s):  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Squared Error ◽  
The Mean

Toward Better Understanding of the Contiguous Rain Area (CRA) Method for Spatial Forecast Verification

2009 ◽  
Vol 24 (5) ◽  
pp. 1401-1415 ◽  
Author(s):  
Elizabeth E. Ebert ◽  
William A. Gallus
Keyword(s):  
Correlation Coefficient ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Weather Prediction ◽  
Forecast Errors ◽  
Forecast Verification ◽  
Rain Area ◽  
Squared Error ◽  
Verification Methods ◽  
The Mean

Abstract The contiguous rain area (CRA) method for spatial forecast verification is a features-based approach that evaluates the properties of forecast rain systems, namely, their location, size, intensity, and finescale pattern. It is one of many recently developed spatial verification approaches that are being evaluated as part of a Spatial Forecast Verification Methods Intercomparison Project. To better understand the strengths and weaknesses of the CRA method, it has been tested here on a set of idealized geometric and perturbed forecasts with known errors, as well as nine precipitation forecasts from three high-resolution numerical weather prediction models. The CRA method was able to identify the known errors for the geometric forecasts, but only after a modification was introduced to allow nonoverlapping forecast and observed features to be matched. For the perturbed cases in which a radar rain field was spatially translated and amplified to simulate forecast errors, the CRA method also reproduced the known errors except when a high-intensity threshold was used to define the CRA (≥10 mm h−1) and a large translation error was imposed (>200 km). The decomposition of total error into displacement, volume, and pattern components reflected the source of the error almost all of the time when a mean squared error formulation was used, but not necessarily when a correlation-based formulation was used. When applied to real forecasts, the CRA method gave similar results when either best-fit criteria, minimization of the mean squared error, or maximization of the correlation coefficient, was chosen for matching forecast and observed features. The diagnosed displacement error was somewhat sensitive to the choice of search distance. Of the many diagnostics produced by this method, the errors in the mean and peak rain rate between the forecast and observed features showed the best correspondence with subjective evaluations of the forecasts, while the spatial correlation coefficient (after matching) did not reflect the subjective judgments.

scholarly journals Peramalan Penjualan dengan Metode Exponential Smoothing (Studi Kasus : Penjualan Bakso Kemasaan/Kiloan Rumah Bakso Bang Ipul)

2019 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Hisyam Ihsan ◽  
Rahmat Syam ◽  
Fahrul Ahmad
Keyword(s):  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Exponential Smoothing ◽  
Trial And Error ◽  
Squared Error ◽  
Forecasting Method ◽  
Operational Activities ◽  
Exponential Smoothing Method ◽  
Single Exponential

Abstrak. Peramalan penjualan memungkinkan sebuah perusahan memilih kebijakan yang optimal untuk membuat keputusan yang sesuai dan mempertahankan efisiensi dari kegiatan operasional. Rumah Bakso Bang Ipul adalah salah satu usaha yang melakukan penjualan yakni penjualan bakso kemasaan/kiloan. Oleh sebab itu,. Rumah Bakso Bang Ipul sangat memerlukan peramalan penjualan untuk meningkatkan keuntungan dan menghindari terjadinya kelebihan atau kekurangan persedian bakso kemasaan/kiloan. Penelitian ini dilakukan peramalan dengan metode exponential smoothing. Adapun parameter atau a yang digunakan dalam meramalkan penjualan adalah a = 0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8, dan 0.9. Singel exponential smoothing melakukan perbandingan dalam menentukan nilai a, dengan mencari nilai a tersebut secara trial and error sampai menemukan a yang memiliki error minimum dengan pencarian menggunakan metode mean absolute error (MAE) dan metode Mean Squaered error (MSE). Sehingga dipilih a = 0.1 dengan nilai MAE = 6.23 dan nilai MSE = 58.32. berdasarkan hasil ini, dengan menggunakan metode singel exponential smoothing dan a =0.1 diperoleh hasil peramalan penjualan bakso bang ipul pada bulan juni 2018 sebanyak 48 kilogram.Kata Kunci: Peramalan, Metode Exponential Smoothing, Metode Singel Exponential SmoothingAbstract. Sales forecasting enables an optimal policy of the company had to make the appropriate decision and maintain the efficiency of operational activities. Rumah Bakso Bang Ipul is a business that sells packaged meatballs. Therefore, Rumah Bakso Bang Ipul is in need of sales forecasting to increase profit and avoid the occurrence or lack of supply of packaged meatballs. This research was conducted by the method of exponential smoothing forecasting. As for parameter or a used predicting sales is a = 0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8, and 0.9. single exponential smoothing do a comparison in determining the value of a, by searching for the value of such a trial and error to find a that has minimum error with search method using the mean absolute error (MAE) and mean squared error (MSE). So that selected a = 0.1 with MAE value = 6.23 and MSE Value = 58.32. Based on  these results, using the method of single exponential smoothing and retrieved results forecasting Rumah Bakso Bang Ipul in July 2018 as much as 48 kilograms.Keywords: Forecasting, Method of exponential smoothing, Method of single exponential smoothing.

scholarly journals A COMPARATIVE STUDY OF IMAGE FILTERING ON VARIOUS NOISY PIXELS

2013 ◽  
pp. 157-165
Author(s):  
SONALI R. MAHAKALE ◽  
NILESHSINGH V. THAKUR
Keyword(s):  
Comparative Study ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Signal To Noise Ratio ◽  
Research Work ◽  
Absolute Error ◽  
Image Filtering ◽  
Squared Error ◽  
The Comparative Study ◽  
Work Done

This paper deals with the comparative study of research work done in the field of Image Filtering. Different noises can affect the image in different ways. Although various solutions are available for denoising them, a detail study of the research is required in order to design a filter which will fulfill the desire aspects along with handling most of the image filtering issues. An output image should be judged on the basis of Image Quality Metrics for ex-: Peak-Signal-to-Noise ratio (PSNR), Mean Squared Error (MSE) and Mean Absolute Error (MAE) and Execution Time.

scholarly journals Predicting Fire Brigades Operational Breakdowns: A Real Case Study

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1383 ◽  
Author(s):  
Selene Cerna ◽  
Christophe Guyeux ◽  
Guillaume Royer ◽  
Céline Chevallier ◽  
Guillaume Plumerel
Keyword(s):  
Public Service ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Machine Learning Techniques ◽  
Fire Departments ◽  
Squared Error ◽  
Learning Techniques ◽  
Over Time

Over the years, fire departments have been searching for methods to identify their operational disruptions and establish strategies that allow them to efficiently organize their resources. The present work develops a methodology for breakage calculation and another for predicting disruptions based on machine learning techniques. The main objective is to establish indicators to identify the failures due to the temporal state of the organization in the human and vehicular material. Likewise, by forecasting disruptions, to determine strategies for the deployment or acquisition of the necessary armament. This would allow improving operational resilience and increasing the efficiency of the firemen over time. The methodology was applied to the Departmental Fire and Rescue Doubs (SDIS25) in France. However, it is generic enough to be extended and adapted to other fire departments. Considering a historic of breakdowns of 2017 and 2018, the best predictions of public service breakdowns for the year 2019, presented a root mean squared error of 2.5602 and a mean absolute error of 2.0240 on average with the XGBoost technique.

PREDIKSI HARGA CRYPTOCURRENCY DENGAN METODE K-NEAREST NEIGHBOURS

2018 ◽  
Vol 14 (2) ◽  
pp. 137
Author(s):  
Haerul Fatah ◽  
Agus Subekti
Keyword(s):  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Search Algorithm ◽  
Absolute Error ◽  
Nearest Neighbour ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Nearest Neighbours

Uang elektronik menjadi pilihan yang mulai ramai digunakan oleh banyak orang, terutama para pengusaha, pebisnis dan investor, karena menganggap bahwa uang elektronik akan menggantikan uang fisik dimasa depan. Cryptocurrency muncul sebagai jawaban atas kendala uang eletronik yang sangat bergantung kepada pihak ketiga. Salah satu jenis Cryptocurrency yaitu Bitcoin. Analogi keuangan Bitcoin sama dengan analogi pasar saham, yakni fluktuasi harga tidak tentu setiap detik. Tujuan dari penelitian yang dilakukan yaitu melakukan prediksi harga Cryptocurrency dengan menggunakan metode KNN (K-Nearest Neighbours). Hasil dari penelitian ini diketahui bahwa model KNN yang paling baik dalam memprediksi harga Cryptocurrency adalah KNN dengan parameter nilai K=3 dan Nearest Neighbour Search Algorithm : Linear NN Search. Dengan nilai Mean Absolute Error (MAE) sebesar 0.0018 dan Root Mean Squared Error (RMSE) sebesar 0.0089.

A Grey Wolf Optimization-Based Method for Segmentation and Evaluation of Scaling in Reinforced Concrete Bridges

2021 ◽  
pp. 1-54
Author(s):  
Eslam Mohammed Abdelkader ◽  
Osama Moselhi ◽  
Mohamed Marzouk ◽  
Tarek Zayed
Keyword(s):  
Neural Network ◽  
Reinforced Concrete ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Concrete Bridges ◽  
Grey Wolf Optimization ◽  
Elman Neural Network ◽  
Reinforced Concrete Bridges ◽  
Squared Error

Bridges are prone to severe deterioration agents which promote their degradation over the course of their lifetime. Furthermore, maintenance budgets are being trimmed. This state of circumstances entails the development of a computer vision-based method for the condition assessment of bridge elements in an attempt to circumvent the drawbacks of visual inspection-based models. Scaling is progressive local flaking or loss in the surface portion of concrete that affects the functional and structural integrity of reinforced concrete bridges. As such, this research study proposes a self-adaptive three-tier method for the automated detection and assessment of scaling severity levels in reinforced concrete bridges. The first tier relies on the integration of cross entropy function and grey wolf optimization (GWO) algorithm for the segmentation of scaling pixels. The second tier is designated for the autonomous interpretation of scaling area. In this model, a hybrid feature extraction algorithm is proposed based on the fusion of singular value decomposition and discrete wavelet transform for the efficient and robust extraction of the most dominant features in scaling images. Then an integration of Elman neural network and GWO algorithm is proposed for the sake of improving the prediction accuracies of scaling area though optimization of both structure and parameters of Elman neural network. The third tier aims at establishing a unified scaling severity index to assess the extent of severities of scaling according to its area and depth. The developed method is validated through multi-layered comparative analysis that involved performance evaluation comparisons, statistical comparisons and box plots. Results demonstrated that the developed scaling detection model significantly outperformed a set of widely-utilized classical segmentation models achieving mean squared error, mean absolute error, peak signal to noise ratio and cross entropy of 0.175, 0.407, 55.754 and 26011.019, respectively. With regards to the developed scaling evaluation model, it accomplished remarkable better and more robust performance that other meta-heuristic-based Elman neural network models and conventional prediction models. In this context, it obtained mean absolute percentage error, root-mean squared error and mean absolute error 1.513%, 29.836 and 12.066, respectively, as per split validation. It is anticipated that the developed integrated computer vision-based method could serve as the basis of automated, reliable and cost-effective inspection platform of reinforced concrete bridges which can assist departments of transportation in taking effective preventive maintenance and rehabilitation actions.

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