International Journal of Big Data Mining for Global Warming

Climate change is a pervasive global challenge with far-reaching implications for both the environment and human society. A thorough comprehension of these repercussions is essential in crafting effective strategies to mitigate their effects. Climate change, a major threat to sustainable development, impacts the environment, economy, natural resources, food security, human health, and physical infrastructure globally. Bangladesh stands out among affected nations due to geographical and socio-economic vulnerabilities. Despite its vulnerability, Bangladesh has successfully mitigated the impact of extreme events over the years. This study focuses on examining climate change, its outcomes, and mitigation initiatives in Bangladesh. It endeavors to offer a comprehensive analysis of the current climate scenario in Bangladesh, delving into pivotal trends, impacts, adaptation strategies, and evaluating the effectiveness of national policies, legal frameworks, involvement of local governments, non-governmental organizations, and international collaborative efforts in developing an efficient disaster risk reduction and anticipatory action system. Despite achievements, safeguarding the livelihoods and properties of vulnerable communities from disaster-induced loss and damage remains a challenge. Additionally, addressing past adaptation failures is crucial. The paper highlights the necessity to enhance disaster risk reduction, green economy, promote economic development, implement government policies, and bolster anticipatory action systems, emphasizing the pivotal role of establishing a locally led climate-resilient system in addressing these challenges.

The principal aim of this paper is to evaluate alterations in the geographical distributions of priority crop wild relatives (CWRs) within the Fertile Crescent Region. The analysis encompasses a comprehensive set of 441 priority CWRs. To compile species distribution information, a meticulously constructed database of occurrence records was utilized. The process involved gathering current and projected climate data from WorldClim to facilitate the evaluation. This study employed MaxEnt version 3.4.1 for species distribution modeling (SDM) considering two distinct dispersal scenarios. One scenario proposed that the CWR taxa were incapable of adapting to their preferred weather conditions, while the other scenario suggested that CWR could track suitable climates to ensure their survival. The generated maps underwent processing in DIVA-GIS version 7.5. Notably, Aegilops bicornis, a wild relative of bread wheat, and Triticum monococcum, a wild relative of wheat, are anticipated to face substantial impacts due to climate change. Specifically, the dispersal of T. monococcum is predicted to shift toward the north, where new suitable areas are expected to emerge in Northern Iraq. Conversely, Cicer bijugum, a wild relative of chickpea, Vicia grandiflora, a wild relative of common vetch, and Beta corolliflora, a wild relative of sugar beet, are projected to experience a comparatively slight impact from climate change, as their potential distribution areas are estimated to diminish. Overall, this study foresees a decline in CWRs across the Fertile Crescent, particularly noting reduced habitats for the CWRs in Turkey.

Data-driven models are emerging as a brand-new approach in Arctic sea ice prediction. In this study, we perform the effectiveness comparison of different machine learning models in predicting the sea ice extent in the whole Arctic Sea and its subregions: the Bering Sea, the East Siberian Sea, the Chukchi Sea, the Beaufort Sea, and the Central Arctic Sea. The machine learning models used in this study include Ridge Regression, Lasso Regression, Decision Tree, k-Nearest Neighbors, Random Forests, XGBoost, as well as two stacked learning models. Our research reveals that due to the different features of sea ice evolution in different sea areas, these data-driven models generate significantly different prediction performances for sea ice evolution in different seas. In detail, two stacked learning models performed the best in the whole Arctic Sea, the stacked learning model LR_Random Forests performed the best in the Beaufort Sea, Bering Sea, and Chukchi Sea, the Random Forests model performed the best in the Central Arctic Sea, and the Decision Tree model performed the best in the East Siberian Sea.