A MACHINE LEARNING APPROACH FOR PREDICTING THE ELECTRO-MECHANICAL IMPEDANCE DATA OF BLENDED RC STRUCTURES SUBJECTED TO CHLORIDE LADEN ENVIRONMENT

2021 ◽  
Author(s):  
Tushar Bansal ◽  
Visalakshi Talakokula ◽  
Prabhakar Sathujoda
Keyword(s):  
Machine Learning ◽  
Moving Average ◽  
Arima Model ◽  
Mechanical Impedance ◽  
Rc Structures ◽  
Autoregressive Integrated Moving Average ◽  
Existing Structures ◽  
Impedance Data ◽  
Machine Learning Approach ◽  
Corrosion Data

Abstract The application of the electro-mechanical impedance (EMI) technique using piezo sensors for structural health monitoring (SHM) is based on baseline/healthy signature data, which poses serious limitations when it needs to be applied to existing structures. Therefore, the present research utilizes autoregressive integrated moving average (ARIMA), an effective time series forecasting machine learning (ML) algorithm to predict the baseline/healthy EMI data and futuristic data of reinforced concrete (RC) corroded specimens. The EMI data from the ARIMA model is validated with the experimental data, and the results obtained prove that the model could be utilized to predict the baseline and forecast the EMI corrosion data effectively. These results will aid the researchers to predict the baseline data for the existing structures and utilize the EMI technique for SHM purposes.

Short term Forecast of the COVID-19 Epidemic in top 15 Affected Countries in the World using ARIMA Model with Machine Learning Approach (Preprint)

2020 ◽  
Author(s):  
Pavan Kumar ◽  
Ranjit Sah ◽  
Alfonso J. Rodriguez-Morales ◽  
Himangshu Kalita Jr ◽  
Akshaya Srikanth Bhagavathula ◽  
...  
Keyword(s):  
Machine Learning ◽  
Moving Average ◽  
Arima Model ◽  
The United States ◽  
Learning Approach ◽  
Average Model ◽  
Machine Learning Approach ◽  
The World ◽  
Moving Average Model ◽  
Auto Regressive

BACKGROUND The COVID-19 pendemic reached more than 200 countries, which was recognized during December-19 from CHINA and affected more than 28 lakh people on date April 26, 2020 (data source:Johns Hopkins Corona Virus Resource Center). OBJECTIVE We here predicted some trajectories of COVID-19 in the coming days (until July 2, 2020) using the most advanced Auto-Regressive Integrated Moving Average Model (ARIMA). METHODS Here we have used the Auto-Regressive Integrated Moving Average Model (ARIMA). Mathematical approaches are widely used to infer critical epidemiological transitions and parameters of COVID-19. Methods such as epidemic curve fitting, surveillance data during the early transmission R0, and other epidemic models are frequently applied to generate forecasts of COVID-19 pandemic across the world. RESULTS Our analysis predicted very frightening outcomes, which defines to worsen the conditions in Iran, entire Europe, especially Italy, Spain, and France. While South Korea, after the initial blast, has come to stability, the same goes for the COVID-19 origin country China with more positive recovery cases and confirm to remain stable. The United States of America (USA) is come as a surprise and going to become the epicenter for new cases during the mid-April 2020. CONCLUSIONS Based on our predictions, public health officials should tailor aggressive interventions to grasp the power exponential growth, and rapid infection control measures at hospital levels are urgently needed to curtail the COVID-19 pandemic. This study analyzed at global level and extracted data upon Machine Learning approach using Artificial intelligence techniques for top 10% or 20 countries.

scholarly journals A statistical study of COVID-19 pandemic in Egypt

2021 ◽  
Vol 54 (1) ◽  
pp. 233-244
Author(s):  
Taha Radwan
Keyword(s):  
Linear Relationship ◽  
Statistical Study ◽  
Moving Average ◽  
Arima Model ◽  
Decision Makers ◽  
Accurate Data ◽  
Autoregressive Integrated Moving Average ◽  
The World ◽  
The Future

Abstract The spread of the COVID-19 started in Wuhan on December 31, 2019, and a powerful outbreak of the disease occurred there. According to the latest data, more than 165 million cases of COVID-19 infection have been detected in the world (last update May 19, 2021). In this paper, we propose a statistical study of COVID-19 pandemic in Egypt. This study will help us to understand and study the evolution of this pandemic. Moreover, documenting of accurate data and taken policies in Egypt can help other countries to deal with this epidemic, and it will also be useful in the event that other similar viruses emerge in the future. We will apply a widely used model in order to predict the number of COVID-19 cases in the coming period, which is the autoregressive integrated moving average (ARIMA) model. This model depicts the present behaviour of variables through linear relationship with their past values. The expected results will enable us to provide appropriate advice to decision-makers in Egypt on how to deal with this epidemic.

scholarly journals Estimating COVID-19 cases in Makkah region of Saudi Arabia: Space-time ARIMA modeling

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250149
Author(s):  
Fuad A. Awwad ◽  
Moataz A. Mohamoud ◽  
Mohamed R. Abonazel
Keyword(s):  
Saudi Arabia ◽  
Moving Average ◽  
Arima Model ◽  
Daily Basis ◽  
World Health ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
First Case ◽  
Novel Coronavirus ◽  
The Impact

The novel coronavirus COVID-19 is spreading across the globe. By 30 Sep 2020, the World Health Organization (WHO) announced that the number of cases worldwide had reached 34 million with more than one million deaths. The Kingdom of Saudi Arabia (KSA) registered the first case of COVID-19 on 2 Mar 2020. Since then, the number of infections has been increasing gradually on a daily basis. On 20 Sep 2020, the KSA reported 334,605 cases, with 319,154 recoveries and 4,768 deaths. The KSA has taken several measures to control the spread of COVID-19, especially during the Umrah and Hajj events of 1441, including stopping Umrah and performing this year’s Hajj in reduced numbers from within the Kingdom, and imposing a curfew on the cities of the Kingdom from 23 Mar to 28 May 2020. In this article, two statistical models were used to measure the impact of the curfew on the spread of COVID-19 in KSA. The two models are Autoregressive Integrated Moving Average (ARIMA) model and Spatial Time-Autoregressive Integrated Moving Average (STARIMA) model. We used the data obtained from 31 May to 11 October 2020 to assess the model of STARIMA for the COVID-19 confirmation cases in (Makkah, Jeddah, and Taif) in KSA. The results show that STARIMA models are more reliable in forecasting future epidemics of COVID-19 than ARIMA models. We demonstrated the preference of STARIMA models over ARIMA models during the period in which the curfew was lifted.

scholarly journals Autoregressive Integrated Moving Average (ARIMA) Sebagai Model Peramalan Kasus Demam Berdarah Dengue

2020 ◽  
Vol 10 (2) ◽  
pp. 76-80
Author(s):  
Roro Kushartanti ◽  
Maulina Latifah
Keyword(s):  
Community Health ◽  
Health Center ◽  
Moving Average ◽  
Arima Model ◽  
Community Health Center ◽  
Percentage Error ◽  
Dengue Case ◽  
Monthly Data ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Method

ARIMA is a forecasting method time series that does not require a specific data pattern. This study aims to analyze the forecasting of Semarang City DHF cases specifically in the Rowosari Community Health Center. The study used monthly data on DHF cases in the Rowosari Community Health Center in 2016, 2017, and 2019 as many as 36 dengue case data. The best ARIMA model for forecasting is a model that meets the requirements for parameter significance, white noise and has the MAPE (Mean Absolute Percentage Error Smallest) value. The results of the analysis show that the best model for predicting the number of dengue cases in the Rowosari Public Health Center Semarang is the ARIMA model (1,0,0) with a MAPE value of 43.98% and a significance coefficient of 0.353, meaning that this model is suitable and feasible to be used as a forecasting model. DHF cases in the Rowosari Community Health Center in Semarang City.

scholarly journals Seawater intrusion vulnerability evaluation and prediction: a case study of Qeshm Island, Iran

2020 ◽  
Author(s):  
Amin Zeynolabedin ◽  
Reza Ghiassi ◽  
Moharram Dolatshahi Pirooz
Keyword(s):  
Seawater Intrusion ◽  
Moving Average ◽  
Arima Model ◽  
Chloride Ion ◽  
Absolute Error ◽  
Ion Concentration ◽  
Qeshm Island ◽  
Autoregressive Integrated Moving Average ◽  
Dangerous Condition

Abstract Seawater intrusion is one of the most serious issues to threaten coastal aquifers. Tourian aquifer, which is selected as the case study, is located in Qeshm Island, Persian Gulf. In this study, first the vulnerability of the region to seawater intrusion is assessed using chloride ion concentration value, then by using the autoregressive integrated moving average (ARIMA) model, the vulnerability of the region is predicted for 14 wells in 2018. The results show that the Tourian aquifer experiences moderate vulnerability and the area affected by seawater intrusion is wide and is in danger of expanding. It is also found that 0.95 km2 of the region is in a state of high vulnerability with Cl concentration being in a dangerous condition. The prediction model shows that ARIMA (2,1,1) is the best model with mean absolute error of 13.3 mg/L and Nash–Sutcliffe value of 0.81. For fitted and predicted data, mean square error is evaluated as 235.3 and 264.3, respectively. The prediction results show that vulnerability is increasing through the years.

scholarly journals Rainfall Forecast of Merauke Using Autoregressive Integrated Moving Average Model

2018 ◽  
Vol 73 ◽  
pp. 12010 ◽  
Author(s):  
Yenni P. Pasaribu ◽  
Hariani Fitrianti ◽  
Dessy Rizki Suryani
Keyword(s):  
Global Climate ◽  
Human Life ◽  
Moving Average ◽  
Arima Model ◽  
Secondary Data ◽  
Crop Failure ◽  
Maximum Rainfall ◽  
Autoregressive Integrated Moving Average ◽  
Agriculture Sector ◽  
Rainfall Forecast

Climate is an important element for human life, one of them is to agriculture sector. Global climate change leads to increased frequency and extreme climatic intensity such as storms, floods, and droughts. Rainfall is climate factor that causes the failure of harvest in Merauke. Therefore, rainfall forecast information is very useful in anticipating the occurrence of extreme events that can lead to crop failure. The purpose of this research is to model rainfall using autoregressive integrated moving average (ARIMA) model. The ARIMA model can be used to predict future events using a set of past data, including predicting rainfall. This research was conducted by collecting secondary data from Agency of Meteorology, Climatology, and Geophysics (BMKG) from 2005 until 2017, then the data was analyzed using R.3.4.2. software. The analysis result showed that ARIMA model (2.0,2) as the right model to predict rainfall in Merauke. The result of forecasting based on ARIMA model (2.0,2) for one period ahead is 179 mm of average rainfall, 46 mm of minimum rainfall, and 295 mm of maximum rainfall. Thus it can be concluded that the intensity of rainfall in Merauke has decreased and there was a seasonal shift from the previous period.

A machine learning approach for the automatic long-term structural health monitoring

2018 ◽  
Vol 18 (3) ◽  
pp. 819-837 ◽  
Author(s):  
Giacomo Vincenzo Demarie ◽  
Donato Sabia
Keyword(s):  
Machine Learning ◽  
Data Acquisition ◽  
Structural Behavior ◽  
Practical Applications ◽  
Existing Structures ◽  
Machine Learning Approach ◽  
Modes Of Vibration ◽  
Engineering Parameters ◽  
Over Time

Measuring the response of a structure to the ambient and service loads is a source of information that can be used to estimate some important engineering parameters or, to a certain extent, to characterize the structural behavior as a whole. By repeating the data acquisition over a period of time, it is possible to check for variations in the structure’s response, which may be correlated to the appearance or growth of a damage (e.g. following some exceptional event as the earthquake, or as a consequence of materials and components aging). The complexity of some existing structures and their environment very often requires the execution of a monitoring plan in order to support analyses and decisions through the evidence of measured data. If the monitoring is implemented through a sensor network continuously acquiring over time, then the evolution of the structural behavior may be tracked continuously as well. Such approach has become a viable option for practical applications since the last decade, as a consequence of the progress in the data acquisition and storage systems. However, proper methods and algorithms are needed for managing the large amount of data and the extraction of valuable knowledge from it. This article presents a methodology aimed at making automatic the process of structural monitoring in case it is carried continuously over time. It relies on some existing methods from the machine learning and data mining fields, which are casted into a process targeted to delimit the need of the human being intervention to the training phase and the engineering judgment of the results. The methodology has been successfully applied to the real-world case of an ancient masonry bell tower, the Ghirlandina Tower (Modena, Italy), where a network made of 12 accelerometers and 3 thermocouples has been acquiring continuously since August 2012. The structural characterization is performed by identifying the first modes of vibration, whose evolution over time has been tracked.

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