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Machine Learning Applications in Climate Science:Novel Approaches to Prediction and Monitoring

  • Unique Paper ID: 181104
  • Volume: 12
  • Issue: 1
  • PageNo: 3582-3592
  • Abstract:
  • Climate Concurrent climate change issues require new strategies in environmental prediction and live monitoring. Conventional General Circulation Models (GCMs) and Numerical Weather Prediction NWP) models provide basic insights but are hampered by computing constraints and have difficulties with the nonlinear nature of environmental information.This study assesses how climate forecasting abilities for temperature, precipitation, and humidity readings can be remapped by intricate machine learning structures in the guise of Long Short-Term Memory (LSTM) networks, Random Forest techniques, and Support Vector Machines (SVM). These ML methodologies show improved accuracy, flexibility, and scalability through the integration of heterogeneous sources of global climate data sets, IoT sensor networks, and satellite imagery. The study addresses new challenges by using explainable AI techniques, federated learning approaches, and hybrid learning and aims at real-world applications in city planning, farm optimization, and catastrophe avoidance.physical-statistical approaches. The article concludes with policy recommendations for merging machine learning technologies with broader climate resilience measures.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 12
  • Issue: 1
  • PageNo: 3582-3592

Machine Learning Applications in Climate Science:Novel Approaches to Prediction and Monitoring

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