Understanding Thermal Impact of Roads on Permafrost Using Normalized Spectral Entropy

Chi Zhang; Hong Zhang; Fuqiang Zhao; Jing Sun

doi:10.3390/su11247177

Understanding Thermal Impact of Roads on Permafrost Using Normalized Spectral Entropy

Sustainability ◽

10.3390/su11247177 ◽

2019 ◽

Vol 11 (24) ◽

pp. 7177 ◽

Cited By ~ 1

Author(s):

Chi Zhang ◽

Hong Zhang ◽

Fuqiang Zhao ◽

Jing Sun

Keyword(s):

Thermal Stability ◽

Time Series Data ◽

Influencing Factor ◽

Road Construction ◽

Tibet Plateau ◽

Series Data ◽

Road Maintenance ◽

Spectral Entropy ◽

Thermal Dynamics ◽

Permafrost Regions

Permafrost is characterized by low temperature, and its thermal stability is key to geohydrological cycles, energy exchange, and climate regulation. Increasing engineering activities, i.e., road construction and operations, are affecting the thermal stability in permafrost regions and have already led to the degradation of permafrost and caused environmental problems. To understand the spatiotemporal influence of road construction and operations on the thermal dynamics in permafrost regions, we conducted a study in the Ela Mountain Pass where multiple roads intersect on the Qinghai–Tibet Plateau (QTP) and calculated the thermal dynamics from 2000 to 2017 using normalized spectral entropy (measuring the disorderliness of time-series data). Our results indicate that road level is a significant influencing factor, where high-level roads (expressways) exhibit stronger thermal impacts than low-level roads (province- and county-level roads). Our results also indicate that duration of operation is the most significant factor that determines the thermal impacts of roads on permafrost: the thermal impacts of the newly paved expressway are positively related to elevation, while the thermal impacts of the old expressway are positively related to less vegetated areas. The study provides an excellent method for understanding the spatiotemporal impacts of engineering activities on the temperature dynamics in permafrost regions, thereby helping policymakers in China and other countries to better plan their infrastructure projects to avoid environmentally vulnerable regions. The study also calls for advanced techniques in road maintenance, which can reduce the accumulated disturbance of road operations on permafrost regions.

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Influence of road transport infrastructure on agricultural sector development in Nigeria

Logistics & sustainable transport ◽

10.2478/jlst-2018-0004 ◽

2018 ◽

Vol 9 (1) ◽

pp. 39-50 ◽

Cited By ~ 1

Author(s):

Olusogo Ogunleye ◽

Akinyemi Ajibola ◽

Oluwafemi Enilolobo ◽

Olufolakemi Shogunle

Keyword(s):

Agricultural Development ◽

Agricultural Sector ◽

Time Series Data ◽

Road Construction ◽

Road Transport ◽

Least Square ◽

Transport Infrastructure ◽

Series Data ◽

National Bureau ◽

Causality Test

AbstractThe study investigated the effects of road transport infrastructure on agricultural sector development in Nigeria from 1985 to 2014, using secondary annual time series data on agricultural development (proxy by gross domestic product in the Agric sector) road transport infrastructure (proxy by length of paved road per square kilometer of area) export and capital, all obtained from the Central Bank of Nigeria (CBN) [3], and National Bureau of Statistics (NBS) [16], statistical bulletins. The data were analyzed using Granger Causality test and Ordinary Least Square estimation techniques. The study concluded that a positive and statistically significant relationship exists between road transport infrastructures (LRT) also evidence was found of a unidirectional causality from agricultural sector development to transport infrastructure. The study, therefore, recommends that adequate and timely maintenance of existing roads should be carried out as well as enacting appropriate regulations that ensure proper implementation and completion of new road construction contracts in the country in order to boost agricultural sector development, reduce wastage of farm produce and increase the possibility of economic diversification.

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Exploring Environmental Kuznets Curves of Kitakyushu: 50-year Time-series Data of the OECD SDGs Pilot City

10.31219/osf.io/ynjvw ◽

2019 ◽

Author(s):

Quan-Hoang Vuong ◽

Tung Manh Ho ◽

Hong-Kong T. Nguyen ◽

NGUYỄN Minh Hoàng

Keyword(s):

Air Pollution ◽

Time Series Data ◽

Road Construction ◽

Series Data ◽

Infrastructure Development ◽

Green Growth ◽

Industrial Growth ◽

Distributed Lag ◽

Auto Regressive ◽

The City

Can green growth policies help protect the environment while keeping the industry growing and infrastructure expanding? The City of Kitakyushu, Japan, has actively implemented eco-friendly policies since 1967 and recently inspired the pursuit of sustainable development around the world, especially in the Global South region. However, empirical studies on the effects of green growth policies are still lacking. This study explores the relationship between road infrastructure development and average industrial firm size with air pollution in the city through the Environmental Kuznets Curve (EKC) hypothesis. Auto-Regressive Distributed Lag (ARDL) and Non-linear Auto-Regressive Distributed Lag (NARDL) methods were applied on nearly 50-years’ time series data, from 1967 to 2015. The results show that the shape of the EKC of industrial growth, measured by average firm size, depends on the type of air pollution: inverted N-shaped relationships with NO2 and CO, and the U-shaped relationships with falling dust particle and Ox. Regarding infrastructure development, on the one hand, our analysis shows a positive effect of road construction on alleviating the amount of falling dust and CO concentration. On the other hand, the emissions of NO2 and Ox are shown to rise when plotted against road construction. The decline of CO emission, when plotted against both industrial growth and road development, indicates that the ruthlessness of the local government in pursuing green growth policies is effective in this case. However, the story is not straightforward when it comes to other air pollutants, which hint at limits in the current policies. The case of Kitakyushu illustrates the complex dynamics of the interaction among policy, industry, infrastructure, and air pollution. It can serve as an important reference point for other cities in the Global South when policies are formed, and progress is measured in the pursuit of a green economy. Finally, as an OECD SDGs pilot city and the leading Asian green-growth city, policymakers in Kitakyushu city are recommended to revise the data policy to enhance the findability and interoperability of data as well as to invest in the application of big data.

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Prediction Framework with Kalman Filter Algorithm

Information ◽

10.3390/info11070358 ◽

2020 ◽

Vol 11 (7) ◽

pp. 358

Author(s):

Janis Peksa

Keyword(s):

Kalman Filter ◽

Time Series Data ◽

Open Data ◽

Series Data ◽

Weather Station ◽

Road Maintenance ◽

Station Data ◽

Weather Station Data ◽

Data Prediction ◽

Filter Approach

The article describes the autonomous open data prediction framework, which is in its infancy and is designed to automate predictions with a variety of data sources that are mostly external. The framework has been implemented with the Kalman filter approach, and an experiment with road maintenance weather station data is being performed. The framework was written in Python programming language; the frame is published on GitHub with all currently available results. The experiment is performed with 34 weather station data, which are time-series data, and the specific measurements that are predicted are dew points. The framework is published as a Web service to be able to integrate with ERP systems and be able to be reusable.

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Evolution Mechanism of Atmospheric Pollution Based on Phase Reconstruction Theory and Time Series Data

International Journal of Applied Evolutionary Computation ◽

10.4018/ijaec.2017100104 ◽

2017 ◽

Vol 8 (4) ◽

pp. 43-52

Author(s):

Guo-Feng Fan ◽

Meng Han ◽

Ya-Ting Wang ◽

Jing-Ru Li

Keyword(s):

Air Pollution ◽

Air Quality ◽

Phase Space ◽

Atmospheric Pollution ◽

Time Series Data ◽

Dimensional Space ◽

Influencing Factor ◽

Series Data ◽

Phase Reconstruction ◽

Evolution Mechanism

This article applies a delay method and recursive analysis to reconstruct the phase space to study the evolution mechanism of atmospheric pollution, i.e., air quality monitoring. Based on the theory of chaos, it is proven that there are chaotic characteristics of factors influencing air quality. In the meanwhile, the phase space reconstruction algorithm is employed to map the factors that affect the air quality into the high dimensional space, and then, gives its two-dimensional plane, the chaotic characteristics of each influencing factor are eventually proven. The results of the study not only analyze the evolution mechanism of air pollution in recent years, but also provide a theoretical support for the future of air pollution remediation.

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Classification of vegetation in North Tibet Plateau based on MODIS time-series data

Wuhan University Journal of Natural Sciences ◽

10.1007/s11859-008-0303-1 ◽

2008 ◽

Vol 13 (3) ◽

pp. 273-278 ◽

Cited By ~ 1

Author(s):

Yuan Lu ◽

Yan Yan ◽

Heping Tao

Keyword(s):

Time Series ◽

Time Series Data ◽

Tibet Plateau ◽

Series Data

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Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

2013 ◽

Author(s):

Stephen J. Tueller ◽

Richard A. Van Dorn ◽

Georgiy Bobashev ◽

Barry Eggleston

Keyword(s):

Time Series ◽

Data Analysis ◽

Exploratory Data Analysis ◽

Time Series Data ◽

Series Data ◽

Exploratory Data

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Some statistical and CI models to predict chaotic high-frequency financial data

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189107 ◽

2020 ◽

Vol 39 (5) ◽

pp. 6419-6430

Author(s):

Dusan Marcek

Keyword(s):

Time Series Data ◽

Moving Average ◽

Methodological Approach ◽

Back Propagation ◽

Large Data ◽

Series Data ◽

Data Set ◽

Training Time ◽

Optimal Population ◽

Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

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Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen ◽

10.32477/jrm.v1i2.72 ◽

2017 ◽

Vol 1 (2) ◽

pp. 177-191

Author(s):

Rizki Rahma Kusumadewi ◽

Wahyu Widayat

Keyword(s):

Time Series ◽

Exchange Rate ◽

Money Supply ◽

Time Series Data ◽

The United States ◽

Economic Conditions ◽

Series Data ◽

Arch Model ◽

United States Dollar ◽

The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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Economic Turmoil in Islamic Banking Investment

ETIKONOMI ◽

10.15408/etk.v19i2.17206 ◽

2020 ◽

Vol 19 (2) ◽

Author(s):

Budiandru Budiandru ◽

Sari Yuniarti

Keyword(s):

Interest Rates ◽

Stock Price ◽

Time Series Data ◽

Series Data ◽

Islamic Banking ◽

Vector Error Correction ◽

Investment Financing ◽

Vector Error ◽

Operational Activities

Investment financing is one of the operational activities of Islamic banking to encourage the real sector. This study aims to analyze the effect of economic turmoil on investment financing, analyze the response to investment financing, and analyze each variable's contribution in explaining the diversity of investment financing. This study uses monthly time series data from 2009 to 2020 using the Vector Error Correction Model (VECM) analysis. The results show that the exchange rate, inflation, and interest rates significantly affect Islamic banking investment financing in the long term. The response to investment financing is the fastest to achieve stability when it responds to shocks to the composite stock price index. Inflation is the most significant contribution in explaining diversity in investment financing. Islamic banking should increase the proportion of funding for investment. Customers can have a larger business scale to encourage economic growth, with investment financing increasing.JEL Classification: E22, G11, G24How to Cite:Budiandru., & Yuniarti, S. (2020). Economic Turmoil in Islamic Banking Investment. Etikonomi: Jurnal Ekonomi, 19(2), xx – xx. https://doi.org/10.15408/etk.v19i2.17206.

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