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A deep analytics for prediction and forecasting of air quality in Chennai
33-53Views:16Air pollution is a global crisis with profound implications for public health and environmental sustainability. In addressing this issue in Chennai, Tamil Nadu, a novel Hadoop-based real-time air pollution prediction system is proposed. This research offers accurate air quality information for specific Chennai regions, aiding decisions and mitigating pollution risks through big data analytics and deep learning for air quality prediction. To expedite air quality prediction, a vast air pollution dataset is rigorously analyzed using a Hadoop-based analytics model. Specific locations in Chennai, including Perungudi, Royapuram, Manali, Alandur, Arumbakkam, Kodungaiyur, and Velachery, are assessed for upto- date air quality evaluations. The core of the research revolves around implementing deep learning models—Long Short-Term Memory, Convolutional Neural Network, and a hybrid Long Short-Term Memory-Convolutional Neural Network model. These models are trained to forecast AQI for selected areas over the next five years, with the hybrid model emerging as the standout performer, achieving 99% of accuracy rate and mean absolute error, mean square error, root mean square error rates of 7.95, 101.71, 9.65. This high accuracy and low error rates empowers policymakers and environmental agencies to make informed decisions, fostering healthier living conditions in Chennai. The integration of big data analytics and deep learning, promises improved air quality management in urban areas globally, addressing similar environmental challenges and enhancing overall quality of life.
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Analysis of landscape geographic impacts of potential climate change in Hungary
41-50Views:237Change of climate can be a remarkable turning point in the 21st century history of mankind. An important task of landscape geographic research is forecasting environmental, nature protection, land use demands and helping mitigation of disadvantageous processes from the aspect of society. ALADIN and REMO numeric climate models predict strong warming and lack of summer precipitation for the area of Hungary for the period between 2021 and 2100. There is a predicted growth in frequency of extreme weather events (heat waves, droughts hailstorms). Changes have been forecasted using data presented in table 1. For analyses of complex landscape geographic impacts of climate change the area of Hungary have been divided into 18 mesoregions with 5.000-10.000 km2 area each (figure 1). The main aspect of choosing the regions was that they should have homogeneous physical, geographic and land use endowments and, for this reason, they should react to climate change the same way. Relationships between landscape forming factors and meteorological elements examined by us have been taken into consideration. Results of analyses of impacts of the meteorological factors on the changes of relief through the mass movements are presented in this paper. Changes of landscape sensibility of mesoregions to mass movements have been presented in the last chapter for the periods between 2021-2050 and 2071-2100 according to numeric climate models.