New enviromentally sensitive patch index - ESPI - for MEDALUS protocol

Duro, Anna; Piccione, Vincenzo Antonio; Ragusa, Maria Alessandra; Veneziano, Vincenzo

doi:10.1063/1.4904593

Cited by 47 publications

(37 citation statements)

References 4 publications

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“…For example, financial institutions can provide financial services to the real economy enterprises who can manufacture waste gas treatment facilities so that the air pollution control can be better realized [27]. In addition, finance itself is considered as a green industry [28], which helps to optimize the industrial structure and update economic functions and which is more conducive to the air environment protection [29][30][31]. Regional examples centered on the financial centers (Shenzhen, Shanghai, etc.)…”

Section: Impact Of Finance On Air Environmentmentioning

confidence: 99%

Measurement and Prediction: Coupling Coordination of Finance and Air Environment

Geng

Tan

2020

Discrete Dynamics in Nature and Society

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This study finds that the comprehensive development degree (CDD) of the finance subsystem is less fluctuated than that of the air environment subsystem, and both subsystems share similarities in spatial distributions. The coupling coordination degrees (CCD) keep fluctuating with varied development directions and extents in different regions; besides, the eastern regions are higher than the western ones for the coupling coordination degrees. In the next years, the coordination degrees of the regions will have different tendencies: despite of the former fluctuation trends, regions in the coordination range will have upward trends, while those in the transition range will be likely to decline. The results are useful in proposing corresponding measures to promote the coordination development between finance and air environment.

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Section: Impact Of Finance On Air Environmentmentioning

confidence: 99%

Measurement and Prediction: Coupling Coordination of Finance and Air Environment

Geng

Tan

2020

Discrete Dynamics in Nature and Society

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“…al. [1], [2] (see also [4], [12], [15], [19]- [23], [25] and reference therein) who developed the transform methods, properties of initial value problems and studied applications of these equations.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Neutral Caputo-Fractional Difference Equations With Applications to Lotka-Volterra Neutral Model

Mesmouli¹,

Ardjouni

Djoudi³

2021

FU Math Inform

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In this paper, we consider a nonlinear neutral fractional difference equations. By applying Krasnoselskii's fixed point theorem, sufficient conditions for the existence of solutions are established, also the uniqueness of solutions is given. As an application of the main theorems, we provide the existence and uniqueness of the discrete fractional Lotka-Volterra model of neutral type. Our main results extend and generalize the results that are obtained in <cite>Azabut</cite>.

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“…Forest fires are one of the important disturbance factors in the global forest ecosystem [1], and it causes various degrees of negative impacts on the ecological environment, resources, human health, economy, and so on [2][3][4][5][6][7][8]. On March 30, 2019, there was a forest fire in Muli County, Liangshan Prefecture, Sichuan Province, China.…”

Section: Introductionmentioning

confidence: 99%

Application of the Artificial Neural Network and Support Vector Machines in Forest Fire Prediction in the Guangxi Autonomous Region, China

Zhong-ke

Chen

et al. 2020

Discrete Dynamics in Nature and Society

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The study of forest fire prediction is of great environmental and scientific significance. China’s Guangxi Autonomous Region has a high incidence rate of forest fires. At present, there is little research on forest fires in this area. The application of the artificial neural network and support vector machines (SVM) in forest fire prediction in this area can provide data for forest fire prevention and control in Guangxi. In this paper, based on Guangxi’s 2010–2018 satellite monitoring hotspot data, meteorology, terrain, vegetation, infrastructure, and socioeconomic data, the researchers determined the main forest fire driving factors in Guangxi. They used feature selection and backpropagation neural networks and radial basis SVM to build forest fire prediction models. Finally, the researchers use the accuracy, precision, and area under the characteristic curve (ROC-AUC) and other indicators to evaluate the predictive performance of the two models. The results showed that the prediction accuracy of the BP neural network and SVM is 92.16% and 89.89%, respectively. As both results are over 85%, the requirements of prediction accuracy is met. These results can be used for forest fire prediction in the Guangxi Autonomous Region. Specifically, the accuracy of the BP neural network was 0.93, which was higher than that of the SVM model (0.89); the recall of the SVM model was 0.84, which was lower than the BANN model (0.92), and the AUC value of the SVM model was 0.95, which was lower than the BP neural network model. The obtained results confirm that the BP neural network model can provide more prediction accuracy than support vector machines and is therefore more suitable for forest fire prediction in Guangxi, China. This research provides the necessary theoretical basis and data support for application in the field of forestry of the Guangxi Autonomous Region, China.

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New enviromentally sensitive patch index - ESPI - for MEDALUS protocol

Cited by 47 publications

References 4 publications

Measurement and Prediction: Coupling Coordination of Finance and Air Environment

Measurement and Prediction: Coupling Coordination of Finance and Air Environment

Nonlinear Neutral Caputo-Fractional Difference Equations With Applications to Lotka-Volterra Neutral Model

Application of the Artificial Neural Network and Support Vector Machines in Forest Fire Prediction in the Guangxi Autonomous Region, China

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