Global Temperature Fuzzy Model as a Function of Carbon Emissions - A Fuzzy ‘Regression’ from Historical Data

2014 ◽  
Author(s):  
Carlos G. Gay ◽  
Bernardo O. Bastien
Keyword(s):  
Carbon Emissions ◽  
Historical Data ◽  
Fuzzy Model ◽  
Global Temperature ◽  
Fuzzy Regression

Mapping arsenic vulnerability at different spatial scales using statistical and machine learning models 

2021 ◽  
Author(s):  
Sonal Bindal
Keyword(s):  
Spatial Resolution ◽  
Spatial Data ◽  
Fuzzy Inference ◽  
Spatial Scales ◽  
Fuzzy Model ◽  
Fuzzy Regression ◽  
Percentage Error ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Predictive Algorithms

<p>In the recent years, prediction modelling techniques have been widely used for modelling groundwater arsenic contamination. Determining the accuracy, performance and suitability of these different algorithms such as univariate regression (UR), fuzzy model, adaptive fuzzy regression (AFR), logistic regression (LR), adaptive neuro-fuzzy inference system (ANFIS), and hybrid random forest (HRF) models still remains a challenging task. The spatial data which are available at different scales with different cell sizes. In the current study we have tried to optimize the spatial resolution for best performance of the model selecting the best spatial resolution by testing various predictive algorithms. The model’s performance was evaluated based of the values of determination coefficient (R<sup>2</sup>), mean absolute percentage error (MAPE) and root mean square error (RMSE). The outcomes of the study indicate that using 100m × 100m spatial resolution gives best performance in most of the models. The results also state HRF model performs the best than the commonly used ANFIS and LR models.</p>

scholarly journals Cumulative carbon as a policy framework for achieving climate stabilization

2012 ◽  
Vol 370 (1974) ◽  
pp. 4365-4379 ◽  
Author(s):  
H. Damon Matthews ◽  
Susan Solomon ◽  
Raymond Pierrehumbert
Keyword(s):  
Climate Change ◽  
Greenhouse Gas ◽  
Temperature Change ◽  
Carbon Emissions ◽  
Climate Impacts ◽  
Global Temperature ◽  
Policy Framework ◽  
Climate Mitigation ◽  
Rate Of Increase ◽  
Global Temperature Change

The primary objective of the United Nations Framework Convention on Climate Change is to stabilize greenhouse gas concentrations at a level that will avoid dangerous climate impacts. However, greenhouse gas concentration stabilization is an awkward framework within which to assess dangerous climate change on account of the significant lag between a given concentration level and the eventual equilibrium temperature change. By contrast, recent research has shown that global temperature change can be well described by a given cumulative carbon emissions budget. Here, we propose that cumulative carbon emissions represent an alternative framework that is applicable both as a tool for climate mitigation as well as for the assessment of potential climate impacts. We show first that both atmospheric CO 2 concentration at a given year and the associated temperature change are generally associated with a unique cumulative carbon emissions budget that is largely independent of the emissions scenario. The rate of global temperature change can therefore be related to first order to the rate of increase of cumulative carbon emissions. However, transient warming over the next century will also be strongly affected by emissions of shorter lived forcing agents such as aerosols and methane. Non-CO 2 emissions therefore contribute to uncertainty in the cumulative carbon budget associated with near-term temperature targets, and may suggest the need for a mitigation approach that considers separately short- and long-lived gas emissions. By contrast, long-term temperature change remains primarily associated with total cumulative carbon emissions owing to the much longer atmospheric residence time of CO 2 relative to other major climate forcing agents.

scholarly journals Fuzzy Estimate of the Development of Passenger Cars Production in the Czech Republic

2021 ◽  
Vol 91 ◽  
pp. 01005
Author(s):  
Simona Hašková
Keyword(s):  
Czech Republic ◽  
Automotive Industry ◽  
Historical Data ◽  
Fuzzy Model ◽  
Passenger Cars ◽  
The Czech Republic ◽  
Fuzzy Estimate ◽  
Almost All ◽  
Target Output

The global outbreak of the COVID-19 and the measures taken, disrupted fundamentally economies around the world. Almost all sectors were affected. The experts have long emphasised the Czech economy’s dependence on the automotive industry. Car producers and companies linked to them have been loaded by severe difficulties after the pandemic outbreak. The article shows one of the constructive ways how to forecast a change in the passenger cars production in the Czech Republic in 2020. Metodologically we lean on a procedure of the fuzzy approach. The prediction itself cannot be derived from the series of historical data of the variables that are related to the target output variable as shown in the fuzzy prediction of GDP for 2018 by this author. Due to the extreme situation caused by pandemic outbreak, the role of expert predictions come intensively into play with their outcomes becoming the set of input data to the fuzzy model. The result of the fuzzy forcast of a change in the cars production in CZ for 2020 shows a greater drop than the official statistical model claims.

MANAGING TARGET THE CASH BALANCE IN CONSTRUCTION FIRMS USING A FUZZY REGRESSION APPROACH

2009 ◽  
Vol 17 (05) ◽  
pp. 667-684 ◽  
Author(s):  
CHENG-WU CHEN ◽  
MORRIS H. L. WANG ◽  
JENG-WEN LIN
Keyword(s):  
Fuzzy Model ◽  
Fuzzy Regression ◽  
Capital Management ◽  
Working Capital ◽  
Cash Balance ◽  
Short Term ◽  
Working Capital Management ◽  
Construction Firms ◽  
Financial Strategies ◽  
S Curve

Construction firms that work on a contractual basis are generally more concerned with short-term rather than long-term financial strategies. The main focus in short-term financial strategies is on working capital management (WCM). Cash management is a major factor for achieving good liquidity and profitability. In this study we take into consideration the cash component of working capital management based on the target cash balance. We develop a practical model that should allow Taiwan construction firms to utilize the currently available cash and assets at any point in time in the most rational way. To help understand the issues involved, we first introduce a model developed by Miller and Orr. The relationship between project duration and progress towards completion is most effectively represented in practical construction management by the S-curve. Thus, in this study we plot the fuzzy S-curve regression based on the Takagi-Sugeno (T-S) fuzzy model. The practicality of the model is demonstrated using project cash flow and progress payment records from a sample project. The data are obtained from the Taipei City Government's Department of Rapid Transit Systems. Some tentative conclusions concerning the model are also given.

scholarly journals ENROLLMENT FORECASTING BASED ON LINGUISTIC TIME SERIES

2020 ◽  
Vol 36 (2) ◽  
pp. 119-137
Author(s):  
Nguyen Duy Hieu ◽  
Nguyen Cat Ho ◽  
Vu Nhu Lan
Keyword(s):  
Time Series ◽  
Fuzzy Sets ◽  
Time Series Data ◽  
Historical Data ◽  
Fuzzy Model ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Forecasting Model ◽  
Proposed Model

Dealing with the time series forecasting problem attracts much attention from the fuzzy community. Many models and methods have been proposed in the literature since the publication of the study by Song and Chissom in 1993, in which they proposed fuzzy time series together with its fuzzy forecasting model for time series data and the fuzzy formalism to handle their uncertainty. Unfortunately, the proposed method to calculate this fuzzy model was very complex. Then, in 1996, Chen proposed an efficient method to reduce the computational complexity of the mentioned formalism. Hwang et al. in 1998 proposed a new fuzzy time series forecasting model, which deals with the variations of historical data instead of these historical data themselves. Though fuzzy sets are concepts inspired by fuzzy linguistic information, there is no formal bridge to connect the fuzzy sets and the inherent quantitative semantics of linguistic words. This study proposes the so-called linguistic time series, in which words with their own semantics are used instead of fuzzy sets. By this, forecasting linguistic logical relationships can be established based on the time series variations and this is clearly useful for human users. The effect of the proposed model is justified by applying the proposed model to forecast student enrollment historical data.

A NEW VIEWPOINT OF S-CURVE REGRESSION MODEL AND ITS APPLICATION TO CONSTRUCTION MANAGEMENT

2006 ◽  
Vol 15 (02) ◽  
pp. 131-142 ◽  
Author(s):  
TING-YA HSIEH ◽  
MORRIS H. L. WANG ◽  
CHENG-WU CHEN ◽  
CHEN-YUAN CHEN ◽  
SHANG-EN YU ◽  
...  
Keyword(s):  
Regression Model ◽  
Large Scale ◽  
Fuzzy Model ◽  
Least Square Method ◽  
Least Square ◽  
City Government ◽  
Fuzzy Regression ◽  
Small Scale ◽  
Project Control ◽  
S Curve

The least square method is in generally used for curve fitting problems. We here propose a fuzzy S-curve regression model to deal with the case in which the observed data are given by fuzzy numbers. The fuzzy regression curve, obtained for project control and predicting the progress of large-scale or small-scale engineering, is smoothly connected by a Takagi-Sugeno (T-S) fuzzy model. This paper also proposes the concept that the upper bound and lower bound are given instead of the confidence interval when the observed data are not obtained exactly. Based on the project cash flow and progress payment records of an example project taken from the Department of Rapid Transit Systems, Taipei City Government, this model is demonstrated and tentative conclusions concerning the model are given. The S-curve equation developed here could be used in a variety of applications related to project control for the management of working capital for construction firms.

scholarly journals Contrasting futures for ocean and society from different anthropogenic CO2emissions scenarios

Science ◽  
2015 ◽  
Vol 349 (6243) ◽  
pp. aac4722 ◽  
Author(s):  
J.-P. Gattuso ◽  
A. Magnan ◽  
R. Billé ◽  
W. W. L. Cheung ◽  
E. L. Howes ◽  
...  
Keyword(s):  
Climate Change ◽  
Carbon Emissions ◽  
Temperature Increase ◽  
Global Temperature ◽  
Climate Regime ◽  
Management Options ◽  
Goods And Services ◽  
Emissions Scenarios ◽  
The Cost ◽  
Emissions Scenario

The ocean moderates anthropogenic climate change at the cost of profound alterations of its physics, chemistry, ecology, and services. Here, we evaluate and compare the risks of impacts on marine and coastal ecosystems—and the goods and services they provide—for growing cumulative carbon emissions under two contrasting emissions scenarios. The current emissions trajectory would rapidly and significantly alter many ecosystems and the associated services on which humans heavily depend. A reduced emissions scenario—consistent with the Copenhagen Accord’s goal of a global temperature increase of less than 2°C—is much more favorable to the ocean but still substantially alters important marine ecosystems and associated goods and services. The management options to address ocean impacts narrow as the ocean warms and acidifies. Consequently, any new climate regime that fails to minimize ocean impacts would be incomplete and inadequate.

Comments on “Why Hasn’t Earth Warmed as Much as Expected?”

2012 ◽  
Vol 25 (6) ◽  
pp. 2192-2199 ◽  
Author(s):  
Reto Knutti ◽  
Gian-Kasper Plattner
Keyword(s):  
Carbon Cycle ◽  
Time Scales ◽  
Climate Sensitivity ◽  
Carbon Emissions ◽  
Radiative Forcing ◽  
Global Temperature ◽  
Aerosol Forcing ◽  
The Earth ◽  
Best Estimate

Abstract In a recent paper, Schwartz et al. suggest that 1) over the last century the earth has warmed less than expected, and they discuss several factors that could explain the discrepancy, including climate sensitivity estimates and aerosol forcing. Schwartz et al. then continue to 2) estimate the allowed carbon emissions for stabilization of global temperature, and find that given the uncertainty in the climate sensitivity even the sign of these allowed carbon emissions is unknown, implying that past emissions may already have committed the earth to 2°C warming for a best-estimate value of climate sensitivity of 3 K. Both of these conclusions in the Schwartz et al. study are revisited herein, and it is shown that 1) in contrast to Schwartz et al., current assessments of climate sensitivity, radiative forcing, and thermal disequilibrium do not support the claim of a discrepancy between expected and observed warming; and 2) the allowed emissions estimated by Schwartz et al. are in conflict with results from a hierarchy of climate–carbon cycle models and are strongly underestimated due to erroneous assumptions about the behavior of the carbon cycle and a confusion of the relevant time scales.

scholarly journals Fuzzy reverse logistics inventory model of smart items with two warehouses of a retailer considering carbon emissions

2021 ◽  
Author(s):  
Subhash Kumar ◽  
Biswajit Sarkar ◽  
Ashok Kumar
Keyword(s):  
Carbon Emissions ◽  
Reverse Logistics ◽  
Fuzzy Model ◽  
Logistics System ◽  
Defective Items ◽  
Total Cost ◽  
Environmental Consequences ◽  
Setup Costs ◽  
The Right ◽  
Economic And Social Impacts

Running the business smoothly for protecting the environment is a significant challenge, on which industries are trying something to do at their level best. Reverse logistics play an important role in system design by reducing environmental consequences and increasing economic and social impacts. Given the recent fluctuations of the market, the production cost and ordering cost are considered triangular fuzzy numbers in this study. Customers' demand is met at the right time, and there is no shortage of items; thus, attention can be paid to two warehouses of a retailer. The setup costs and deterioration costs of this system are affected by the learning effects, which lead to a decrease in the total cost. Inflation is a significant problem in the market because manufacturing, remanufacturing, and retailers are all affected. This study proposes a reverse logistics system model so that customers can resolve their complaints about defective items and carbon emissions under two warehouses. Numerical results show that the fuzzy model is more economically beneficial than the crisp model, finds that the crisp and fuzzy model saw a difference of 0.34% in total cost. Two numerical examples illustrate this study, and a sensitivity analysis is performed using tables and graphs.

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