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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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Prediction of industrial land use using linear regression and mola techniques: A Case Study of Siltara Industrial belt
59-70Views:404The Siltara Industrial belt is an important industrial pocket of Chattisgarh state located in the northern part of the Raipur city, which is rapidly growing. In this process spatial, cultural, political and administrative factors are controlling its rate, direction and pattern. The Simple Linear Regression (SLR) and Multi-Objective Land Allocation (MOLA) techniques, which are embedded in SPSS and Idrisi Kilimanjaro software respectively, and have been used for the estimation of future scenario of the industrial growth. In this model, a suitable platform has been prepared in which future industrialization has been estimated by integrating physical, social, cultural factors and land acquisition policy. In this article, results have revealed that industrialization has occurred very fast during last one decade. The industrial land was 6.15 km2 in 2001 and 18.725 km2 in 2011 and estimated as 31.30 km2 in 2021 and 43.87 km2 in 2031 using SLR. The rapid industrial growth is very critical issues for agrarian society and fresh environment. This model very accurately estimating (overall accuracy=95.39%, Kno=97.24%, agreement=98.63 %) the future growth of industrial land. This work will be useful to the planners and policy makers of private and government sectors to regulate the sustainable planning practices and smart decision-making.
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In-flight icing characteristics of unmanned aerial vechicles during special atmospheric condition over the Carpathian-basin
74-80Views:120The in-flight aerial icing phenomena is very important for the Unmanned Aerial Vehicles (UAV) because it causes some serious problems such as reduced lift and increased drag forces, significantly decreased angle of attack, increased weight, structural imbalances and improper radio communications. In order to increase flight safety of UAV’s we develop an integrated meteorological support system for the UAV pilots, mission controllers and decision makers, too. In our paper we show the in-flight structural icing estimation method as a part of this support system based on a simple 2D ice accretion model predictions. We point out the role of the ambient air temperature, cloud liquid water content, airfoil geometry and mainly the true airspeed in the icing process on the wings of UAVs. With the help of our model we made an estimation of geometry and amount of ice accretion on the wing of a short-range and a high-altitude and long-endurance UAVs during a hypothetical flight under a typical icy weather situation with St clouds over the Carpathian-basin (a cold-pool situation case study). Finally we point out that our icing estimation system can easily be adapted for supporting the missions of UAVs.