Climate Change/Environmental
The proliferation of efficient edge computing has enabled a paradigm shift of how we monitor and interpret urban air quality. Coupled with the dense spatiotemporal resolution realized from large-scale wireless sensor networks, we can achieve highly accurate realtime local inference of airborne pollutants. In this paper, we introduce a novel Deep Neural Network architecture targeted at latent time-series regression tasks from continuous, exogenous sensor measurements, based on the Transformer encoder scheme and designed for deployment on low-cost power-efficient edge processors.
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Tweets related to 10 different types of disasters were monitored from 28 September 2021 till 6 October 2021. 67528 rows containing 16 fields were extracted using Artificial Intelligence and Natural Language Processing Services of Microsoft.
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Please cite the following paper when using this dataset:
N. Thakur, "Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets from 2017–2022 and 100 Research Questions", Journal of Analytics, Volume 1, Issue 2, 2022, pp. 72-97, DOI: https://doi.org/10.3390/analytics1020007
Abstract
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This dataset contains actual field/experimental data for the following environmental engineering applications, namely:
- Concentration data generated from filtration systems which treat influents, having contaminant materials, via adsorption process.
- Streamflow height data collated for 50 states/cities in America for the historical period between 1900-2018.
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ABSTRACT
Europe is covered by distinct climatic zones which include semiarid, the Mediterranean, humid subtropical, marine,
humid continental, subarctic, and highland climates. Land use and land cover change have been well documented in the
past 200 years across Europe1where land cover grassland and cropland together make up 39%2. In recent years, the
agricultural sector has been affected by abnormal weather events. Climate change will continue to change weather
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As Science and technology evolve, the environment is getting affected daily. These cause major environmental issues like Global Warming, Ozone layer depletion, Natural resource depletion, etc. These are measured and regulated by local bodies. The data given by the local bodies are average values for a large area, those data might be inaccurate for a small sector or isolated zone. However, there are few techniques such as WSN (Wireless Sensor Networks), IoT (Internet of things) which measures and updates real-time data to a cloud server to overcome the trouble.
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More than 40% of energy resources are consumed in the residential buildings, and most of the energy is used for heating. Improving the energy efficiency of residential buildings is an urgent problem. The collected data is intended to study a dependence of the dynamics heat energy supply from outside temperature and houses characteristics, such as walls material, year of construction, floors amount, etc. This study will support the development of methods for comparing thermal characteristics of residential buildings and carry out recommendations for the energy efficiency increases.
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More than 40% of energy resources are consumed in the residential buildings, and most of the energy is used for heating. Improving the energy efficiency of residential buildings is an urgent problem. The collected data is intended to study a dependence of the dynamics heat energy supply from outside temperature and houses characteristics, such as walls material, year of construction, floors amount, etc. This study will support the development of methods for comparing thermal characteristics of residential buildings and carry out recommendations for the energy efficiency increases.
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