scholarly journals An Ecological Early Warning Indicator System for Environmental Protection of Scenic Areas

2019 ◽  
Vol 11 (8) ◽  
pp. 2344
Author(s):  
Shih-Yen Lin ◽  
Jun-Liang Lu ◽  
Yu-Lin Fan
Keyword(s):  
Environmental Management ◽  
Environmental Quality ◽  
Early Warning ◽  
Indicator System ◽  
Analytic Hierarchy ◽  
Early Warning Indicator ◽  
Early Warning Indicators ◽  
New Perspective ◽  
Warning Indicators

The aim of this research is to build an ecological early warning indicator system. Several complex and interlinked factors contribute to changes in the environmental quality of scenic areas. If changes in these factors can be monitored and controlled, the environmental quality of scenic areas can also be controlled, achieving the purpose of protecting the ecological environment. This study utilizes ecological early warning indicators to enable environmental management. Twenty-six early warning indicators are first identified through a literature review, interviews, and survey pretesting. A fuzzy analytic hierarchy process is then used to calculate the weights of different levels/hierarchies. These early warning indicators can be used both to assess the environmental quality of scenic areas, and to provide a foundation for the construction of an ecological early warning indicator system in scenic areas. This approach represents a new perspective on scenic area environmental management and ecological protection.

Adapting and Using Early Warning Indicators in Different Contexts

2020 ◽  
Vol 122 (14) ◽  
pp. 1-24
Author(s):  
Ashley Pierson ◽  
Sarah Frazelle ◽  
Christopher Mazzeo
Keyword(s):  
School Districts ◽  
Early Warning ◽  
Relevant Literature ◽  
Community Support ◽  
Indicator System ◽  
Data Availability ◽  
Similar Data ◽  
Early Warning Indicator ◽  
Early Warning Indicators ◽  
Warning Indicators

Background Research shows that educators can identify half of future high school dropouts as early as Grade 6—and three quarters or more of future dropouts by Grade 9—by monitoring readily available data on attendance, behavior, and course performance. These data have come to be known as the ABCs of dropout prevention, and the measures are some of the more commonly used early warning indicators. Purpose Developing and deploying an early warning indicator system (EWIS) is a complex endeavor for school districts, especially smaller districts without access to a large internal research team or experience implementing similar data-oriented initiatives at scale. In this chapter, we describe four key considerations that school districts and others should take account of when designing an EWIS: building broad consensus on the purpose of the system, planning around system design and data availability, validating indicators and setting thresholds, and implementing and monitoring the system. Research Design This article is based on a review of the relevant literature on designing EWISs and our experiences. We have partnered with multiple districts and states in recent years and advised these organizations on early warning indicator selection, as well as system development. References to the literature are noted where applicable. The omission of references indicates that that portion of the text is based on our experiences working directly with districts and states. Conclusions/Recommendations Developing early warning indicators—and an EWIS—is a complex technical and human undertaking, akin to implementing a large districtwide initiative at scale. Districts of any size are well served by thinking carefully about the process and going slowly, if needed, to ensure they get the details right. Planning the system and building consensus for implementation, choosing indicators that are right for the district context, and validating and setting the right thresholds for those indicators are essential for building widespread leadership, staff, and community support for the EWIS, as well as developing the conditions for effective implementation and use.

Construction of Patent Infringement Litigation Early Warning Index System for the Pharmaceutical Enterprises

2012 ◽  
Vol 268-270 ◽  
pp. 2045-2049
Author(s):  
Ying Wang
Keyword(s):  
Early Warning ◽  
Quantitative Methods ◽  
Indicator System ◽  
Patent Infringement ◽  
Qualitative And Quantitative Methods ◽  
Early Warning Indicator ◽  
Target Company ◽  
Target Layer ◽  
Warning Model ◽  
Warning Indicators

Our objective is to establish the patent infringement litigation early warning model and early warning indicator system for pharmaceutical companies. Reversely generate the warning model based on incentives of patent infringement litigation. A combination of qualitative and quantitative methods were used to determine the specific early warning indicators. According to the hierarchical analytical process, the three-tier warning indicator was established system including the target layer and factors, and three stages of the early warning indicator system: were put forward: preliminary warning phase, target company judging phase, and accurate early warning stage. Quantified statement was given to all the warning indicators, to establish a mathematical model.

scholarly journals The Research of Coal and Gas Outburst Warning Based on Logistic Regression and Geographic Information System

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Bo You ◽  
Bo Li ◽  
Shi Liang Shi ◽  
He Qing Liu ◽  
Yi Lu ◽  
...  
Keyword(s):  
Information System ◽  
Logistic Regression ◽  
Geographic Information System ◽  
Early Warning ◽  
Warning System ◽  
Indicator System ◽  
Geographic Information ◽  
Coal And Gas Outburst ◽  
Gas Outburst ◽  
Early Warning Indicator

Coal and gas outburst is one of the major disasters in the safety production of coal mine. According to the mechanism of coal and gas outburst, based on the comprehensive analysis of various influencing factors of coal and gas outburst, with the principles of selected early warning indicator, the basic information database of coal and gas outburst warning is constructed, and the information data query function is realized. The mathematical model of coal and gas outburst warning is established by the logistic regression analysis based on the gas concentration, the gas desorption index of drill cuttings, and the initial velocity of gas emission from the borehole. The multivariate information coupled warning was conducted according to the selected early warning indicator system, and the early warning level was divided with the result of early warning. The design and research of the coal and gas outburst warning system are carried out based on the geographic information system (GIS). The coal and gas outburst warning system was verified by taking the Tunliu mine of Lu’an Group as an example. The establishment of the early warning system is a new technical way to the early warning management of coal and gas outburst and can provide a guarantee for coal and gas outburst prevention.

Construction of an Indicator System for Integrated Assessment of Environmental Quality of the Dianchi Basin

2011 ◽  
Vol 356-360 ◽  
pp. 903-907
Author(s):  
Ai Jun Li ◽  
Yan Ying Guo ◽  
Feng He ◽  
Rui Jia Yuan
Keyword(s):  
Environmental Quality ◽  
Integrated Assessment ◽  
Large Scale ◽  
Environmental Changes ◽  
Indicator System ◽  
Regional Environment ◽  
Definition Of ◽  
Changes Over Time ◽  
Regional Environmental Quality

There are few indicator systems available for monitoring and assessing the environmental quality of large-scale regions. We constructed an indicator system for integrated assessment of the environmental quality of the Dianchi Basin. First, the definition of regional environmental quality is determined by both the supply of materials and energy in the region and the extent to which the region is polluted. Second, the indicator categories used for assessment mainly comprise vegetation biomass and the concentrations of various pollutants. Third, owing to spatial heterogeneity of a region, evaluation of the regional environment first requires division into sub-regions, each of which should be relatively homogeneous with regard to physical conditions (e.g. marine and terrestrial) and appearance (e.g. vegetation cover). Finally, the mathematical models for assessing regional environmental quality can be built according to the relationships between the various indicators, the sub-regions and regional environmental quality. The indicator system built using this approach can reflect environmental changes over time and identifies reasons for environmental variation.

IMPACT OF MONTANE ENVIRONMENTAL LOADS ON THE ENVIRONMENTAL QUALITY OF THE SLOVAK REPUBLIC

2021 ◽  
Author(s):  
Boutheyna Touafchia ◽  
Rached-Kanouni Malika ◽  
Kadi Zahia
Keyword(s):  
Environmental Quality ◽  
Slovak Republic ◽  
Analytic Hierarchy ◽  
Environmental Loads ◽  
Synergistic Interactions ◽  
Average Impact ◽  
Hierarchy Process ◽  
Environmental Burdens ◽  
Very High

Environmental burdens are currently a significant determinant ofenvironmental quality. The occurrence of montane environmental burdens affects the environmental quality at the local, regional and supraregional levels, mostly to a negative extent. Environmental pollution, due to montane environmental burdens is accompanied by a reduction in environmental quality in all spheres of anthropogenic existence. It is for this reason that the presented article studies the occurrence of montane environmental burdens and their impact on environmental quality in individual regions of the Slovak Republic. In the analyses Analytic Hierarchy Process a multi-criteria decision-making method based on values of the weights in terms of the principles of the Saaty matrix – a quantification of synergistic interactions of indicators. The study concentrates on selected comparative years 2008, 2012, 2017 and 2021 and includes a matrix of their elimination. The results present a categorization of montane environmental loads in the Slovak Republic into categories of the environmental load (from very low to very high) on the environmental quality. In the summary matrix of a comprehensive assessment of montane environmental loads, negative indicators (55.65%) prevails over positive ones (44.35%) with a total scoring ratio of 0.80, i.e. III. category, the average impact on environmental quality.

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