International Journal of Big Data Mining for Global Warming

The effect of four different cyclones making land fall on four different coastal regions is studied viz., Nisha (2008, Tamil Nadu), Laila (2010, Andhra Pradesh), Sidr (2007, Bangladesh) and land depression BOB 03 (2008, Orissa). Remote sensing and Geographic Information System (GIS) technique are used to detect change in Land use and Land cover (LU/LC). Change in vegetation cover by Normalized Vegetation Index (NDVI) is also investigated. Further, preparation of slope map, processing of buffer zoning map is exercised. These parameters are analyzed to find the impression of cyclones after hitting the coastal boundaries by considering the images before and after the cyclone has passed. Change detection assessment of LU/LC features provides information for monitoring the trend of change in an area. In almost every considered region, it is found that dense vegetation is changed to sparse vegetation. Also, decrease in the irrigated cropland due to heavy rainfall caused by cyclone is noted. Risk zone is created by buffer ring of cyclone track to spot the area under risk zone. The area calculation suggests the effect of cyclone at the distance of 20–50km from the cyclone path which is validated from the slope effect on LU/LC, also. Some of the common features such as dense vegetation, show decrease in the area by 71%, 17%, 67% and 60%, or settlement area also shows decrease by 38%, 15%, 57% and 17% due to Laila, BOB 03, Nisha and Sidr cyclones, respectively. Increase in shrubland mix with rangeland by 18%, 113% and 98% is also seen due to Laila, Nisha and Sidr cyclones. Other LU/LC shows changes such as, water bodies increasing by 6%, 189% due to BOB 03 and Nisha cyclones. Changes are also seen in sparsed vegetation, which is decreased in Orissa and Tamil Nadu and increased in Andhra Pradesh and Bangladesh. It is demonstrated that by preparing risk zonation map, risk assessment can be done.

Responding to climate change requires efforts from utility providers for the production of and engagement with more advanced integrated electrical distribution grids. For example, much of the smart grid effort focuses on bridging together renewable energy sources (as well as an increased level of smart monitoring and automation of electrical transmission). Within the smart grid, the smart meter also has the potential to play a role in reducing the level of carbon emissions for all residential customers, as smart meters provide the means for customers to manage and reduce their electricity usage. For example, detailed energy profiles of energy usage patterns can be constructed and reported back to the end-user. As such, this research investigates residential home environments and how the data produced by smart meters can be used to profile energy usage in homes. In particular, this project concerns the design of a system and algorithm to model and predict household behavior patterns from smart meter readings. The aim is to model the behavioral trends in homes to develop an autonomous system that can advise home users on changes that can be adopted to reduce their carbon emissions. The data used for the research were constructed from a digital simulation model of multiple smart home environments. Using a two-class boosted decision tree, the system is able to detect anomalous users with 71.7% AUC.

This study determines the suitable general circulation models (GCMs) for the prediction of future precipitation of Upper Godavari sub-basin, India. Five performance indicators (PIs) namely correlation coefficient (CC), normalized root mean square deviation (NRMSD), absolute normalized mean biased deviation (ANMBD), skill score (SS), Nash Sutcliffe efficiency (NSE), and three different combinations (Case 1: all performance indicators, Case 2: CC, SS and ANMBD, and Case 3: CC, SS, and NRMSD) were considered to evaluate the performance of 38 GCM models for the study area. The observed precipitation data for 12 grid points covering the Upper Godavari sub-basin along with eight districts of Maharashtra were used for the selection of the suitable GCMs. The weights of the indicators were determined by the entropy method. Compromise programming (CP) and the technique for order preference to the similarity to ideal solution (TOPSIS) methods were used for ranking the GCMs. The group decision-making approach was employed to make a collective decision about the rank of 38 GCMS considering all the grid points. In view of all the three combinations of PIs, the study suggests that the effect of the performance indicator NSE on the ranking of GCM models is the most significant (weights for the grid points varying in the range 22.75%–78%) followed by ANMBD, CC, NRMSD, and SS. Including the maximum number of PIs and considering their combinations is found to be much helpful to enhance the credibility of the ranking of GCMs. From the group decision-making approach, it was observed that the ensemble of MPI-ESM-P, CNRM-CM5-2, and CNRM-CM5 is suitable for the prediction of precipitation for the study area.

The Public Electricity and Heat Production (PEHP) is the most emissive sector in European economy. The characteristic features of PEHP contribute to the importance of its mitigation role. In this study, the decarbonization potential of energy sector was retrospectively analyzed. Proposed algorithm contained, at the first layer, the year-by-year data analysis and calculation while at the second graphical interpretation and pattern grouping. Despite the double-layering, the developed approach included the three following stepwise stages: the CO₂ emission alternatives scenarios pathways methodology, the CO₂ emission driving forces modes and influence weight methodology, and the transformation pattern identifying and grouping. Based on 1990–2017 CO₂ emission inventories, the investigation of long-term PEHP decarbonization in 26 European countries was performed, in year-by-year and country-by-country processing. All results and findings have become the foundation for describing achieved decarbonization goals and 27 years mitigation ways. The Energy Consumption (EC) and Emission Factor (EF) have been pointed as a key driving forces of CO₂ emission and as a key development indicator of PEHP sectoral transformation structure. Based on the developed country-by-country graphical interpretation interface, the five key patterns of PEHP decarbonization processes in European countries were observed and defined. The algorithm which has been applied and tested in 26 countries for 27 years was proved to be a universal and flexible tool to determine the lack of a utilitarian path of PEHP CO₂ emission mitigation in Europe.

The study focused on economic influence of climate change variables on net income of agribusinesses, particularly on crop production in Niger Delta region of Nigeria. It estimated and projected economic impacts of rainfall and temperature on the net income of crop farmers. Then it was guided by two research questions and two hypotheses. This study relied on secondary data from 129 previous studies sampled purposively. Data were collected using structured document review guide. Data were analysed using Least square multiple regression and ANOVA statistic to test the tasted null hypotheses. Findings of the study revealed that 69% of the variations in net income of crop production in Niger Delta is as a result of the alterations in rainfall and temperature, as a decrease in rainfall by 1mm resulted in decrease in net income by $-\text{₦}$4.39k while an increase in temperature by 1^∘C resulted in decrease in net income by $-\text{₦}$6206.40k. The expected net income of crop production in Niger Delta is projected to continually decrease, from $\text{₦}$4062.14$\pm$$\text{₦}$5986.25k in the year 2025 to $\text{₦}$631.09$\pm$5986.25k in the year 2050, based on future values of rainfall and temperature. The null hypotheses tested showed that variability in rainfall and temperature is directly proportional ($p<0.05$) to the changes in net income of crop production for both estimation and projection in the econometric analysis. Based on the findings and implications of the study, it was recommended among others that climate change impact assessment committees should be set up at rural, state and federal levels to evaluate the impacts, and propose indigenous policies for addressing present and future losses in agribusinesses.