Precision long-term monitoring of local weather at household level

Ioan Florin Voicu, (), Dragos Cristian Diaconu () and Daniel Costantin Diaconu ()
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Ioan Florin Voicu,: ING Hubs, PhD Candidate University of Bucharest, Bucharest, Romania
Dragos Cristian Diaconu: Bucharest University of Economic Studies, Bucharest, Romania
Daniel Costantin Diaconu: Dr.,Interdisciplinary Center for Advanced Studies (CISA), Research Institute of Universityof Bucharest, Bucharest, RomaniaTitle: Risks and opportunities of smart education

Smart Cities International Conference (SCIC) Proceedings, 2024, vol. 12, 325-334

Abstract: This research aims to show that there are discrepancies between city-wide weather forecasts and household-level realtime conditions, which can help with disaster preparedness & mitigation, as well as improve the daily lives of residents with access to the data. Prior work: Previous studies have shown that, while weather forecasting systems are constantly increasing their precision and, due to the desire to avoid loss of lives & property, provide ever more frequent warnings for evacuation or sheltering, they still lack the precision necessary to provide forecasts for very small areas. Approach: A case study was conducted, in which a household solution for monitoring outdoor precipitation quantities, atmospheric pressure, humidity & temperature was implemented, with its results stored over a period of approximately 2 years and compared with statistical data for the city for the same interval. Results: The analyzed outcome showed that meteorological conditions at household level don’t always match the citywide forecasts, with significant differences in humidity, temperature & precipitation quantities versus the statistics recorded for the region. Implications: The extra data granularity proved that certain factors appear earlier or can linger for quite a while longer than the regional statistics show, which, if the solution would be applied on a larger scale, would improve issues like disaster response or yearly budgeting by the authorities, especially given the problems raised by climate change worldwide. Value: The results show that the sensors & software monitoring implemented at such granular level can greatly improve the precision of weather forecasting, with Machine Learning models being able to use the historical data to accurately predict the impact of regional weather patterns at household level.

Keywords: climate change; microclimate; precipitation analysis; IOT; weather patterns Decision-Making; Workforce Development (search for similar items in EconPapers)
JEL-codes: O35 (search for similar items in EconPapers)
Date: 2024
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