Space-Time Effect Modelling with Machine Learning: A Scalable Approach for Assessing Efficiency of Coal Resources

2019 ◽  
Author(s):  
Liming Xue ◽  
Yulin Du ◽  
Wei Li ◽  
Xinghui Zhao
Keyword(s):  
Machine Learning ◽  
Space Time ◽  
Time Effect ◽  
Coal Resources

scholarly journals Space-Time Machine Learning Models to Analyze COVID-19 Pandemic Lockdown Effects on Aerosol Optical Depth over Europe

2021 ◽  
Vol 13 (15) ◽  
pp. 3027
Author(s):  
Saleem Ibrahim ◽  
Martin Landa ◽  
Ondřej Pešek ◽  
Karel Pavelka ◽  
Lena Halounova
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Spatial Resolution ◽  
Missing Values ◽  
Space Time ◽  
Spatial Coverage ◽  
The Mean ◽  
Prediction Approach

The recent COVID-19 pandemic affected various aspects of life. Several studies established the consequences of pandemic lockdown on air quality using satellite remote sensing. However, such studies have limitations, including low spatial resolution or incomplete spatial coverage. Therefore, in this paper, we propose a machine learning-based scheme to solve the pre-mentioned limitations by training an optimized space-time extra trees model for each year of the study period. The results have shown that our trained models reach a prediction accuracy up to 95% when predicting the missing values in the MODIS MCD19A2 Aerosol Optical Depth (AOD) product. The outcome of the mentioned scheme was a geo-harmonized atmospheric dataset for aerosol optical depth at 550 nm with 1 km spatial resolution and full coverage over Europe. As an application, we used the proposed machine learning based prediction approach in AOD levels analysis. We compared the mean AOD levels between the lockdown period from March to June in 2020 and the mean AOD values of the same period for the past 5 years. We found that AOD levels dropped over most European countries in 2020 but increased in several eastern and western countries. The Netherlands had the most significant average decrease in AOD levels (19%), while Spain had the highest average increase (10%). Moreover, we analyzed the relationship between the relative percentage difference of AOD and four meteorological variables. We found a positive correlation between AOD and relative humidity and a negative correlation between AOD and wind speed. The value of the proposed prediction scheme is further emphasized by taking into consideration that the reconstructed dataset can be used for future air quality studies concerning Europe.

scholarly journals Development of the Panel ARDL by Adding Space-Time effect to Modeling Monthly Paddy Producer Price in Java

2021 ◽  
Vol 1863 (1) ◽  
pp. 012053
Author(s):  
M Ardiansyah ◽  
A Djuraidah ◽  
I M Sumertajaya ◽  
A H Wigena ◽  
A Fitrianto
Keyword(s):  
Space Time ◽  
Time Effect ◽  
Producer Price ◽  
Panel Ardl

Space-time machine learning for modelling soil organic carbon change

2020 ◽  
Author(s):  
Gerard Heuvelink ◽  
Marcos Angelini ◽  
Laura Poggio ◽  
Zhanguo Bai ◽  
Niels Batjes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Temporal Variation ◽  
Space Time ◽  
Calibration Data ◽  
Soc Stock ◽  
Soc Stocks ◽  
Over Time

<p>Spatially resolved estimates of change in soil organic carbon (SOC) stocks are necessary for supporting national and international policies aimed at achieving land degradation neutrality and climate mitigation through better land management. In this work we report on the development, implementation and application of a data-driven, statistical space-time method for mapping SOC stocks, using Argentina as a pilot area. We used the Quantile Regression Forest machine-learning algorithm to predict SOC stock at 0-30 cm depth at 250 m resolution for Argentina between 1982 and 2017, on an annual basis. The model was calibrated using over 5,000 SOC stock values from the 36-year time period and 35 environmental covariates. Most covariates were static and could only explain the spatial SOC distribution. SOC change over time was modelled using time series maps of the AVHRR NDVI vegetation index. These NDVI time series maps were pre-processed using a temporal low-pass filter to allow the SOC stock for a given year to depend on the NDVI of the current as well as preceding years. Spatial patterns of SOC stock predictions were persistent over time and comparable to baseline SOC stock maps of Argentina. Predictions had modest temporal variation with an average decrease for the entire country from 2.55 kg C m<sup>‑2</sup> to 2.48 kg C m<sup>‑2</sup> over the 36-year period (equivalent to a decline of 210.7 Gg C, 3.0% of the total 0‑30 cm SOC stock in Argentina). The Pampa region had a larger estimated SOC stock decrease from 4.62 kg C m<sup>‑2</sup> to 4.34 kg C m<sup>‑2</sup> (5.9%) during the same period. For the 2001-2015 period, predicted temporal variation was 7-fold larger than that obtained using the Tier 1 approach of the Intergovernmental Panel on Climate Change and the United Nations Convention to Combat Desertification. Prediction uncertainties turned out to be substantial, mainly due to the limited number and poor spatial and temporal distribution of the calibration data, and the limited explanatory power of the covariates. Cross-validation confirmed that SOC stock prediction accuracy was limited, with a Mean Error of 0.03 kg C m<sup>-2</sup> and a Root Mean Squared Error of 2.04 kg C m<sup>-2</sup>. The model explained 45% of the SOC stock variation. In spite of the large uncertainties, this work showed that machine learning methods can be used for space-time SOC mapping and may yield valuable information to land managers and policy makers, provided that SOC observation density in space and time is sufficiently large.</p>

scholarly journals Kinetics, Dynamics and Energy of Solid on the Example of a Tool Fixed Flexibly: Part 1 – Kinetics

2013 ◽  
Vol 15 ◽  
pp. 35-47
Author(s):  
Zdzisław Pluta ◽  
Tadeusz Hryniewicz
Keyword(s):  
Space Time ◽  
Time Effect ◽  
Essential Difference ◽  
Time And Energy ◽  
Original Approach ◽  
Work And Energy

This work investigates kinetics, dynamics and energy of solid on the example of a tool fixed flexibly under the process of cutting. The original approach to the tool kinetics was considered by the Authors earlier. This work consists with three parts referred to the kinetics, dynamics, and energy of solid. Present work is concerned on the development of kinetics problems of a solid represented by a tool fixed flexibly and is a continuation of the problem. Part 1 covers the definition and characteristics of the machining space-time and is referred generally to the kinetics. Then the kinetic and dynamic magnitudes characterizing tool in the space-time are described. The set of these magnitudes has been extended by introducing the properly understood impulse and time-effect. Part 2 of the work is to consider the dynamics of tool in the machining space-time. In Part 3, types of works in the machining space-time and energy of the tool fixed flexibly will be considered; the focus is to be put on an essential difference between work and energy.

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