The Climate change is the most pressing issue confronting Humanity lack of effective prediction techniques resulting in the damage of infrastructures and resources, collective climate predictions mechanism will not help the mankind we need a selective or personalized prediction techniques which can be more beneficial in predicting the climate changes closely. Natural catastrophes, Temperature analysis is the most essential areas where a personalized prediction techniques plays vital role. Here we are focusing in climate prediction at farms by forecasting solar radiation which climate changes are directly dependent on by using meteorological conditions such as temperature, radiation, pressure, humidity, wind speeds using Machine learning to anticipate the climatic change in advance helps farmers in analyzing natural risks that arise as a result of abrupt climate change. Farmers may use the predictions to schedule seed sowing and pre harvesting according to predictions in safeguarding crop.


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    Titel :

    Climate change prediction using deep learning


    Beteiligte:
    Earshia, Diana (Autor:in) / Kumar, Anil (Autor:in) / Reddy, Devansh (Autor:in) / Praneesha (Autor:in) / Palanivel, Anand (Herausgeber:in) / Megaraj, Meikandan (Herausgeber:in) / Packirisamy, Muthukumaran (Herausgeber:in)

    Kongress:

    5TH INTERNATIONAL CONFERENCE ON INNOVATIVE DESIGN, ANALYSIS & DEVELOPMENT PRACTICES IN AEROSPACE & AUTOMOTIVE ENGINEERING: I-DAD’22 ; 2022 ; Chennai, India


    Erschienen in:

    Erscheinungsdatum :

    07.06.2023


    Format / Umfang :

    5 pages





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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