scholarly journals The Forecasting of Groundwater Fluctuations using Time Series Analysis and Combination of Data-Driven Models

2021 ◽  
Author(s):  
Amirhossein Najafabadipour ◽  
Gholamreza Kamali ◽  
Hossein Nezamabadi-pour
Keyword(s):  
Iron Ore ◽  
Least Squares Method ◽  
Moving Average ◽  
Groundwater Resources ◽  
Mining Area ◽  
Data Driven ◽  
Short Term ◽  
Iron Ore Mine ◽  
Groundwater Fluctuations ◽  
Groundwater Drainage

The Forecasting of Groundwater Fluctuations is a useful tool for managing groundwater resources in the mining area. Water resources management requires identifying potential periods for groundwater drainage to prevent groundwater from entering the mine pit and imposing high costs. In this research, Auto-Regressive Integrated Moving Average (ARIMA) and Holt-Winters Exponential Smoothing (HWES) data-driven models were used for short-term modeling of the groundwater fluctuations in a piezometer around the Gohar Zamin Iron Ore Mine. For this purpose, 250 non-seasonal groundwater fluctuations data in the period 22-Nov-2018 to 29-Jul-2019, 200 data for modeling, and 50 data for prediction were used. To take advantage of all the features of the two developed models, the predictions are combined with different methods and specific weights. The results show better accuracy for the ARIMA method between the two short-term forecasts, while the HWES method requires less time for modeling. Also, among all the predictions made, the highest accuracy for the combined least-squares method is for forecasting the groundwater fluctuations in the short-term. All the forecasts show a decrease in the groundwater fluctuations, indicating pumping wells around the Gohar Zamin Iron Ore Mine area.

scholarly journals Evaluating data-driven methods for short-term forecasts of cumulative SARS-CoV2 cases

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0252147
Author(s):  
Ghufran Ahmad ◽  
Furqan Ahmed ◽  
Muhammad Suhail Rizwan ◽  
Javed Muhammad ◽  
Syeda Hira Fatima ◽  
...  
Keyword(s):  
Moving Average ◽  
Absolute Error ◽  
Data Availability ◽  
Data Driven ◽  
Percentage Error ◽  
Pictorial Representation ◽  
Short Term ◽  
Autoregressive Integrated Moving Average ◽  
Global Pandemic ◽  
Short Term Forecasting

Background The WHO announced the epidemic of SARS-CoV2 as a public health emergency of international concern on 30th January 2020. To date, it has spread to more than 200 countries and has been declared a global pandemic. For appropriate preparedness, containment, and mitigation response, the stakeholders and policymakers require prior guidance on the propagation of SARS-CoV2. Methodology This study aims to provide such guidance by forecasting the cumulative COVID-19 cases up to 4 weeks ahead for 187 countries, using four data-driven methodologies; autoregressive integrated moving average (ARIMA), exponential smoothing model (ETS), and random walk forecasts (RWF) with and without drift. For these forecasts, we evaluate the accuracy and systematic errors using the Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE), respectively. Findings The results show that the ARIMA and ETS methods outperform the other two forecasting methods. Additionally, using these forecasts, we generate heat maps to provide a pictorial representation of the countries at risk of having an increase in the cases in the coming 4 weeks of February 2021. Conclusion Due to limited data availability during the ongoing pandemic, less data-hungry short-term forecasting models, like ARIMA and ETS, can help in anticipating the future outbreaks of SARS-CoV2.

The Influence of Jidong Iron Ore Exploitation on Regional Groundwater Environment

2014 ◽  
Vol 522-524 ◽  
pp. 916-920
Author(s):  
Xin She Liu ◽  
Li Li Zhang ◽  
Shu Qun She ◽  
Bin Zhao ◽  
Da Jin Liu ◽  
...  
Keyword(s):  
Environmental Protection ◽  
Iron Ore ◽  
Groundwater Resources ◽  
Water Environment ◽  
Mining Area ◽  
Protection Measures ◽  
Ore Deposit ◽  
Aquifer System ◽  
Groundwater Environment ◽  
Regional Groundwater

Jidong Iron Ore lies in the Baimiaozi Series of Dantazi Group, Archaeozoic erathem. With a resource of 2,501.16 million tons and an average grade of over 30%, it is an important iron base of China. The mining area is located at the middle-upper part of alluvial-proluvial fan of the Luanhe River. The ore body is covered with Quaternary gravels and pebbles, in which the aquifer has a large water capacity and is rich in water; therefore it is a water ore deposit and there is great difficulty in sewer drainage of the ore deposit. Besides, sewer drainage may damage underground water environment and incur some environmental problems including land subsidence. In this article, based on the analysis of hydrogeologic condition of the mining area, put forward a hydrogeological concept model of the multilayer aquifer system in which the hydraulic affiliation is quite strong, establish a numerical simulation model describing multilayer aquifer system to predict the evolvement of groundwater flow field due to the mining, then evaluate the influence of mining on the regional groundwater environment, and finally, propose some environmental protection measures. All of the above is of great significance in the method of assessing the influence on the groundwater environment, exploitation of the groundwater resources and environmental protection management during the mine exploitation.

scholarly journals Comparison of Short-Term Load Forecasting Based on Kalimantan Data

2021 ◽  
Vol 5 (2) ◽  
pp. 243-259
Author(s):  
Syalam Ali Wira Dinata ◽  
Muhammad Azka ◽  
Primadina Hasanah ◽  
Suhartono Suhartono ◽  
Moh Danil Hendry Gamal
Keyword(s):  
Time Series ◽  
Model Building ◽  
Least Squares Method ◽  
Moving Average ◽  
Seasonal Pattern ◽  
Short Term ◽  
Sarima Model ◽  
East Kalimantan ◽  
Moving Average Model ◽  
Short Term Forecasting

This paper investigates a case study on short term forecasting for East  Kalimantan, with emphasis on special days, such as public holidays. A time series of load demand electricity  recorded at hourly intervals contains more than one seasonal pattern.  There is a great attraction in using a modelling time series method that is able to capture triple seasonalities.  The Triple SARIMA model has been adapted for this purpose and competitive for modelling load.  Using the least squares method to estimate the coefficients in a triple SARIMA model, followed by model building, model assumptions  and comparing model criteria, we propose and demonstration  the triple Seasonal Autoregressive Integrated Moving Average model  with AIC 290631.9 and SBC 290674.2 as the best model for this study. The Triple seasonal ARIMA is one of the alternative strategy to propose accurate forecasts of  electricity load Kalimantan data for planning, operation  maintenance and  market related activities.

scholarly journals Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis

2020 ◽  
Author(s):  
Tanujit Chakraborty ◽  
Indrajit Ghosh
Keyword(s):  
Risk Assessment ◽  
Real Time ◽  
Moving Average ◽  
Hybrid Approach ◽  
Regression Tree ◽  
Case Fatality ◽  
Warning System ◽  
Data Driven ◽  
Short Term ◽  
The Future

AbstractThe coronavirus disease 2019 (COVID-19) has become a public health emergency of international concern affecting 201 countries and territories around the globe. As of April 4, 2020, it has caused a pandemic outbreak with more than 11,16,643 confirmed infections and more than 59,170 reported deaths worldwide. The main focus of this paper is two-fold: (a) generating short term (real-time) forecasts of the future COVID-19 cases for multiple countries; (b) risk assessment (in terms of case fatality rate) of the novel COVID-19 for some profoundly affected countries by finding various important demographic characteristics of the countries along with some disease characteristics. To solve the first problem, we presented a hybrid approach based on autoregressive integrated moving average model and Wavelet-based forecasting model that can generate short-term (ten days ahead) forecasts of the number of daily confirmed cases for Canada, France, India, South Korea, and the UK. The predictions of the future outbreak for different countries will be useful for the effective allocation of health care resources and will act as an early-warning system for government policymakers. In the second problem, we applied an optimal regression tree algorithm to find essential causal variables that significantly affect the case fatality rates for different countries. This data-driven analysis will necessarily provide deep insights into the study of early risk assessments for 50 immensely affected countries.

scholarly journals Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 1829
Author(s):  
Davide Grande ◽  
Catherine A. Harris ◽  
Giles Thomas ◽  
Enrico Anderlini
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Moving Average ◽  
Machine Learning Algorithms ◽  
Physical Models ◽  
Data Driven ◽  
The Stability ◽  
And Control ◽  
Nonlinear Autoregressive

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control.

scholarly journals Prediction of Head Movement in 360-Degree Videos Using Attention Model

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3678
Author(s):  
Dongwon Lee ◽  
Minji Choi ◽  
Joohyun Lee
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Moving Average ◽  
The Other ◽  
Learning Models ◽  
Short Term ◽  
Term Memory ◽  
Attention Model ◽  
Long Short Term Memory ◽  
Machine Learning Models

In this paper, we propose a prediction algorithm, the combination of Long Short-Term Memory (LSTM) and attention model, based on machine learning models to predict the vision coordinates when watching 360-degree videos in a Virtual Reality (VR) or Augmented Reality (AR) system. Predicting the vision coordinates while video streaming is important when the network condition is degraded. However, the traditional prediction models such as Moving Average (MA) and Autoregression Moving Average (ARMA) are linear so they cannot consider the nonlinear relationship. Therefore, machine learning models based on deep learning are recently used for nonlinear predictions. We use the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network methods, originated in Recurrent Neural Networks (RNN), and predict the head position in the 360-degree videos. Therefore, we adopt the attention model to LSTM to make more accurate results. We also compare the performance of the proposed model with the other machine learning models such as Multi-Layer Perceptron (MLP) and RNN using the root mean squared error (RMSE) of predicted and real coordinates. We demonstrate that our model can predict the vision coordinates more accurately than the other models in various videos.

scholarly journals Action Recognition Network Using Stacked Short-Term Deep Features and Bidirectional Moving Average

2021 ◽  
Vol 11 (12) ◽  
pp. 5563
Author(s):  
Jinsol Ha ◽  
Joongchol Shin ◽  
Hasil Park ◽  
Joonki Paik
Keyword(s):  
Action Recognition ◽  
High Speed ◽  
Moving Average ◽  
Video Clip ◽  
Visual Surveillance ◽  
Temporal Information ◽  
Feature Maps ◽  
Short Term ◽  
Accurate Analysis ◽  
Difference Images

Action recognition requires the accurate analysis of action elements in the form of a video clip and a properly ordered sequence of the elements. To solve the two sub-problems, it is necessary to learn both spatio-temporal information and the temporal relationship between different action elements. Existing convolutional neural network (CNN)-based action recognition methods have focused on learning only spatial or temporal information without considering the temporal relation between action elements. In this paper, we create short-term pixel-difference images from the input video, and take the difference images as an input to a bidirectional exponential moving average sub-network to analyze the action elements and their temporal relations. The proposed method consists of: (i) generation of RGB and differential images, (ii) extraction of deep feature maps using an image classification sub-network, (iii) weight assignment to extracted feature maps using a bidirectional, exponential, moving average sub-network, and (iv) late fusion with a three-dimensional convolutional (C3D) sub-network to improve the accuracy of action recognition. Experimental results show that the proposed method achieves a higher performance level than existing baseline methods. In addition, the proposed action recognition network takes only 0.075 seconds per action class, which guarantees various high-speed or real-time applications, such as abnormal action classification, human–computer interaction, and intelligent visual surveillance.

scholarly journals Data-Driven Study on the Sustainable Log Movements: Impact of Rail Car Fleet Size on Freight Storage and Car Idling

2020 ◽  
Vol 12 (11) ◽  
pp. 4563
Author(s):  
Sangpil Ko ◽  
Pasi Lautala ◽  
Kuilin Zhang
Keyword(s):  
Service Life ◽  
Seasonal Dynamics ◽  
Lake Superior ◽  
Data Driven ◽  
Short Term ◽  
Fleet Size ◽  
Interesting Observation ◽  
The Us ◽  
Term Storage ◽  
Scale Data

Rail car availability and the challenges associated with the seasonal dynamics of log movements have received growing attentions in the Lake Superior region of the US, as a portion of rail car fleet is close to reaching the end of its service life. This paper proposes a data-driven study on the rail car peaking issue to explore the fleet of rail cars dedicated to being used for log movements in the region, and to evaluate how the number of cars affects both the storage need at the sidings and the time the cars are idled. This study is based on the actual log scale data collected from a group of forest companies in cooperation with the Lake State Shippers Association (LSSA). The results of our analysis revealed that moving the current log volumes in the region would require approximately 400–600 dedicated and shared log cars in ideal conditions, depending on the specific month. While the higher fleet size could move the logs as they arrive to the siding, the lower end would nearly eliminate the idling of rail cars and enable stable volumes throughout the year. However, this would require short-term storage and additional handling of logs at the siding, both elements that increase the costs for shippers. Another interesting observation was the fact that the reduction of a single day in the loading/unloading process (2.5 to 1.5 days) would eliminate almost 100 cars (20%) of the fleet without reduction in throughput.

MULTIFRACTAL FLUCTUATIONS OF JIUZHAIGOU TOURISTS BEFORE AND AFTER WENCHUAN EARTHQUAKE

Fractals ◽  
2013 ◽  
Vol 21 (01) ◽  
pp. 1350001 ◽  
Author(s):  
KAI SHI ◽  
WEN-YONG LI ◽  
CHUN-QIONG LIU ◽  
ZHENG-WEN HUANG
Keyword(s):  
Wenchuan Earthquake ◽  
Moving Average ◽  
Long Term Memory ◽  
Short Term ◽  
Dynamical Mechanism ◽  
Before And After ◽  
Previous State ◽  
Multifractal Behavior ◽  
Multifractal Nature

In this work, multifractal methods have been successfully used to characterize the temporal fluctuations of daily Jiuzhai Valley domestic and foreign tourists before and after Wenchuan earthquake in China. We used multifractal detrending moving average method (MF-DMA). It showed that Jiuzhai Valley tourism markets are characterized by long-term memory and multifractal nature in. Moreover, the major sources of multifractality are studied. Based on the concept of sliding window, the time evolutions of the multifractal behavior of domestic and foreign tourists were analyzed and the influence of Wenchuan earthquake on Jiuzhai Valley tourism system dynamics were evaluated quantitatively. The study indicates that the inherent dynamical mechanism of Jiuzhai Valley tourism system has not been fundamentally changed from long views, although Jiuzhai Valley tourism system was seriously affected by the Wenchuan earthquake. Jiuzhai Valley tourism system has the ability to restore to its previous state in the short term.

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