scholarly journals A Simplified Climate Change Model and Extreme Weather Model Based on a Machine Learning Method

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 139 ◽  
Author(s):  
Xiaobin Ren ◽  
Lianyan Li ◽  
Yang Yu ◽  
Zhihua Xiong ◽  
Shunzhou Yang ◽  
...  
Keyword(s):  
Climate Change ◽  
Pearson Correlation ◽  
Extreme Weather ◽  
Biological Species ◽  
Convincing Evidence ◽  
Support Vector ◽  
Human Society ◽  
Change Model ◽  
Climate Change Model ◽  
The Impact

The emergence of climate change (CC) is affecting and changing the development of the natural environment, biological species, and human society. In order to better understand the influence of climate change and provide convincing evidence, the need to quantify the impact of climate change is urgent. In this paper, a climate change model is constructed by using a radial basis function (RBF) neural network. To verify the relevance between climate change and extreme weather (EW), the EW model was built using a support vector machine. In the case study of Canada, its level of climate change was calculated as being 0.2241 (“normal”), and it was found that the factors of CO2 emission, average temperature, and sea surface temperature are significant to Canada’s climate change. In 2025, the climate level of Canada will become “a little bad” based on the prediction results. Then, the Pearson correlation value is calculated as being 0.571, which confirmed the moderate positive correlation between climate change and extreme weather. This paper provides a strong reference for comprehensively understanding the influences brought about by climate change.

scholarly journals Influence of Cesaro summation and the Fejer Kernel onto the climate change model

2018 ◽  
Vol Volume-2 (Issue-3) ◽  
pp. 2466-2471
Author(s):  
Rajendra Bapurao Vhatkar ◽  
Dr. Vishwajeet S. Goswami ◽  
Keyword(s):  
Climate Change ◽  
Change Model ◽  
Fejér Kernel ◽  
Climate Change Model

scholarly journals Aggravation of Human Diseases and Climate Change Nexus

2019 ◽  
Vol 16 (15) ◽  
pp. 2799 ◽  
Author(s):  
Mohd Danish Khan ◽  
Hong Ha Thi Vu ◽  
Quang Tuan Lai ◽  
Ji Whan Ahn
Keyword(s):  
Climate Change ◽  
Infectious Diseases ◽  
Strong Relationship ◽  
Epidemiological Data ◽  
Extreme Weather ◽  
Control Measures ◽  
Extreme Weather Events ◽  
Human Beings ◽  
Impact Of Climate Change ◽  
The Impact

For decades, researchers have debated whether climate change has an adverse impact on diseases, especially infectious diseases. They have identified a strong relationship between climate variables and vector’s growth, mortality rate, reproduction, and spatiotemporal distribution. Epidemiological data further indicates the emergence and re-emergence of infectious diseases post every single extreme weather event. Based on studies conducted mostly between 1990-2018, three aspects that resemble the impact of climate change impact on diseases are: (a) emergence and re-emergence of vector-borne diseases, (b) impact of extreme weather events, and (c) social upliftment with education and adaptation. This review mainly examines and discusses the impact of climate change based on scientific evidences in published literature. Humans are highly vulnerable to diseases and other post-catastrophic effects of extreme events, as evidenced in literature. It is high time that human beings understand the adverse impacts of climate change and take proper and sustainable control measures. There is also the important requirement for allocation of effective technologies, maintenance of healthy lifestyles, and public education.

scholarly journals Pearson Correlation-Based Feature Selection for Document Classification Using Balanced Training

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6793
Author(s):  
Inzamam Mashood Nasir ◽  
Muhammad Attique Khan ◽  
Mussarat Yasmin ◽  
Jamal Hussain Shah ◽  
Marcin Gabryel ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Data Augmentation ◽  
Feature Fusion ◽  
Pearson Correlation ◽  
Document Classification ◽  
Document Image ◽  
Support Vector ◽  
Neural Network Models ◽  
The Impact

Documents are stored in a digital form across several organizations. Printing this amount of data and placing it into folders instead of storing digitally is against the practical, economical, and ecological perspective. An efficient way of retrieving data from digitally stored documents is also required. This article presents a real-time supervised learning technique for document classification based on deep convolutional neural network (DCNN), which aims to reduce the impact of adverse document image issues such as signatures, marks, logo, and handwritten notes. The proposed technique’s major steps include data augmentation, feature extraction using pre-trained neural network models, feature fusion, and feature selection. We propose a novel data augmentation technique, which normalizes the imbalanced dataset using the secondary dataset RVL-CDIP. The DCNN features are extracted using the VGG19 and AlexNet networks. The extracted features are fused, and the fused feature vector is optimized by applying a Pearson correlation coefficient-based technique to select the optimized features while removing the redundant features. The proposed technique is tested on the Tobacco3482 dataset, which gives a classification accuracy of 93.1% using a cubic support vector machine classifier, proving the validity of the proposed technique.

On the Landslide Hazard with the Impact of Climate Change in Central Taiwan

2020 ◽  
Author(s):  
Keh-Jian Shou
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Extreme Weather ◽  
Tectonic Activity ◽  
Extreme Weather Events ◽  
Weather Events ◽  
The Future ◽  
Central Taiwan ◽  
The Impact ◽  
Taiwan Area

<p>Due to active tectonic activity, the rock formations are young and highly fractured in Taiwan area. The dynamic changing of river morphology makes the highly weathered formations or colluviums prone to landslide and debris flow. For the past decade, the effect of climate change is significant and creates more and more extreme weather events. The change of rainfall behavior significantly changes the landslide behavior, which makes the large-scale landslides, like the Shiaolin landslide, possible. Therefore, it is necessary to develop the new technologies for landslide investigation, monitoring, analysis, early warning, etc.</p><p>Since the landslide hazards in Taiwan area are mainly induced by heavy rainfall, due to climate change and the subsequent extreme weather events, the probability of landslides is also increased. Focusing on the upstreams of the watersheds in Central Taiwan, this project studied the behavior and hazard of shallow and deep-seated landslides. Different types of susceptibility models in different catchment scales were tested, in which the control factors were analyzed and discussed. This study also employs rainfall frequency analysis together with the atmospheric general circulation model (AGCM) downscaling estimation to predict the extreme rainfalls in the future. Such that the future hazard of the shallow and deep-seated landslide in the study area can be predicted. The results of predictive analysis can be applied for risk prevention and management in the study area.</p>

scholarly journals On the attribution of the impacts of extreme weather events to anthropogenic climate change

2021 ◽  
Author(s):  
Sarah E Perkins-Kirkpatrick ◽  
Daithi Stone ◽  
Dann M. Mitchell ◽  
Suzanne M. Rosier ◽  
Andrew David King ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Anthropogenic Impacts ◽  
Extreme Weather ◽  
Anthropogenic Climate Change ◽  
Extreme Weather Events ◽  
Climate Change Signal ◽  
Care Needs ◽  
Weather Events ◽  
The Impact

Abstract Investigations into the role of anthropogenic climate change in extreme weather events are now starting to extend into analysis of anthropogenic impacts on non-climate (e.g. socio-economic) systems. However, care needs to be taken when making this extension, because methodological choices regarding extreme weather attribution can become crucial when considering the events’ impacts. The fraction of attributable risk (FAR) method, useful in extreme weather attribution research, has a very specific interpretation concerning a class of events, and there is potential to misinterpret results from weather event analyses as being applicable to specific events and their impact outcomes. Using two case studies of meteorological extremes and their impacts, we argue that FAR is not generally appropriate when estimating the magnitude of the anthropogenic signal behind a specific impact. Attribution assessments on impacts should always be carried out in addition to assessment of the associated meteorological event, since it cannot be assumed that the anthropogenic signal behind the weather is equivalent to the signal behind the impact because of lags and nonlinearities in the processes through which the impact system reacts to weather. Whilst there are situations where employing FAR to understand the climate change signal behind a class of impacts is useful (e.g. “system breaking” events), more useful results will generally be produced if attribution questions on specific impacts are reframed to focus on changes in the impact return value and magnitude across large samples of factual and counterfactual climate model and impact simulations. We advocate for constant interdisciplinary collaboration as essential for effective and robust impact attribution assessments.

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