International Journal of Big Data Mining for Global Warming

In this study, we assess the future changes in minimum temperature (T-min), maximum temperature (T-max), and precipitation (PRCP) for the three periods the 2020s (2011–2040), the 2050s (2041–2070), and the 2080s (2071–2100), with respect to the reference period 1981–2010 over Algeria focusing on a validation of the Statistical DownScaling Model (SDSM). In this approach, to underpin our analysis, we evaluate statistically the SDSM performance by simulating the historical temperatures and precipitation. The NCEP reanalysis data and CanESM2 predictors of three future scenarios, RCP2.6, RCP4.5, and RCP8.5 are used for model calibration and future projection, respectively. The projected climate changes resulting from the application of SDSM show a convincing consistency with those unveiled in previous studies over Algeria based on dynamical regional climate model outputs conducted in the context of Middle East-North Africa region. By the end of the century, the results exhibit strong warming for both extreme temperatures under the worst-case scenario (RCP 8.5), it is more pronounced for the T-max and over the Algerian Sahara region. Under the optimistic scenario (RCP2.6), the strength of the warming is expected to increase for both extreme temperatures. The projected changes of precipitation revealed for all scenarios several discrepancies with significant decrease over the northwest region and central Sahara, while nonsignificant change is projected for the center and eastern coastal regions. Our findings corroborate previous studies using sophisticated tools by demonstrating that Algeria’s climate is expected to warm further in the future. These primary findings could give an overview of the application of the statistical modeling approach using SDSM over a semi-arid and arid vulnerable region like Algeria and would extend our knowledge in the climate-modelling field for the North Africa zone by providing an added value to the existing GCMs and regional climate projections. In addition, reliable information regarding the magnitude of future changes at local scale may be used in impact models to assess changes of other key economic sector variables such as water resources management, energy and agriculture.

This study examined both long-term and short-term trends and fluctuations in rainfall and temperature over 34 meteorological stations located at seven regions in Bangladesh. Descriptive statistical analysis and Mann–Kendall trend test were utilized to investigate the variability of the rainfall and temperature of all stations in Bangladesh. Our research gave some insights into Bangladesh’s rainfall and temperature trends and variability.

Emission by vehicles is one of the main sources causing the exceedance of the Air Pollution Index at roadside. Therefore, reducing emissions from vehicles in busy corridors has been a priority of the Government in improving roadside air quality. The existing pedestrian lights, which follow a predetermined signal timing for road crossings, are typical static traffic lights. The pushbutton has been added to improve the pedestrian crossing efficiency by rendering the green phase to the pedestrian as soon as possible. However, the current detection devices are not capable of streamlining the traffic flows and prioritizing pedestrians on the curb according to the actual needs. With the use of artificial intelligence (AI) techniques such as deep learning, this project can analyze the traffic conditions of a designated area, including the length of traffic queues and the number of pedestrians waiting at a crossroad. Our system can then manage traffic lights to facilitate efficient traffic flow between vehicles and pedestrians. In addition, the IoT technologies enable the system to collect traffic information for big data analysis and facilitate efficient traffic management. In the implementation stage of the project, a local field study of a crossroad with a school, bus stops, and shopping malls in the vicinity was conducted and relevant data for further analysis was collected. The results showed that the system could help to shorten traffic queues and pedestrian waiting time at the crossroad. The goals are to examine strategies in traffic signal control based on AI to reduce carbon footprint. The deliverables will assess how urban transport systems can reduce carbon emissions and adapt to climate change with a focus on traffic signal rationalization. It provides guidance, policy recommendations, and outlines research and information needs.

The expansion of the industrial sector is one of the prime contributors to the increase in the concentration of heat-trapping gases, mainly carbon dioxide (${\mathrm{\text{CO}}}_2$), in the atmosphere. Since the onset of the industrial revolution, industrial ${\mathrm{\text{CO}}}_2$ emissions have been contributing significantly to global warming and associated climate changes. To design strategies for the mitigation of climate changes, it is crucial to comprehend the role of industrialization in the elevation of atmospheric ${\mathrm{\text{CO}}}_2$ concentration. This paper presents a nonlinear mathematical model, comprising a set of nonlinear differential equations, to examine the impact of industrialization on the dynamics of atmospheric ${\mathrm{\text{CO}}}_2$. Analysis of the model shows that if the industrial ${\mathrm{\text{CO}}}_2$ emission rate increases beyond a critical value, the system experiences Hopf-bifurcation about the interior equilibrium and periodic solution is generated. The direction and stability of periodic solutions arising through Hopf-bifurcation are investigated. Numerical simulation is presented to demonstrate the analytical findings.