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Weather Prediction: A comprehensive study of Machine learning techniques
1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 12 | Dec 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 569 Weather Prediction: A comprehensive study of Machine learning techniques Er. Vaishali Jamwal1, Dr. Sameru Sharma2, Dr. Ajay Abrol3 1Er. Vaishali Jamwal, P.G Student, Dept. of Electronics and Communication Engineering, Govt. College of Engineering & Technology, Jammu, India 2Dr Sameru Sharma, Professor, Dept. of Electronics and Communication Engineering, GCET, Jammu, India 3Dr. Ajay Abrol, Professor, Dept. of Electronics and Communication Engineering, GCET, Jammu, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Weather forecasting is a critical aspect of modern life, influencing various sectors such as agriculture, transportation, energy, and disastermanagement. Inresponse, machine learning mechanismshaveemergedaspowerfultools for improving weather forecasting accuracy. This comprehensive survey aims to provide an overview of various machine learning approaches applied to weather forecasting techniques which highlights their strengths, limitations, and potential applications. The survey begins by introducing fundamental machine learning concepts relevant to weather forecasting, including supervised and unsupervised learning, regression, classification, and time series analysis. The survey delves into supervised learning approaches, covering popular regression algorithms and classification methods used for extreme weather event prediction. Time series forecasting methods, such as ARIMA, LSTM networks, and Prophet, are examined to address the temporal nature of weather data. Ensemble methods and deep learning techniques, like CNNs and RNNs, are investigated to enhance forecasting accuracy. Moreover, the surveyexplores hybridapproachesthatcombine weather prediction models with machine learning to leverage observational data and improve forecasting outcomes. Potential future directions, including explainable AI and big data utilization, are outlined. Key Words: Weather forecasting, Machine learning, Supervisedlearning,Unsupervised learning,Timeseries forecasting. 1.INTRODUCTION Weather forecasting is a critical aspect of modern life, impacting various sectors such as agriculture, transportation, energy, and disaster management. Accurate and reliable weather predictions enable individuals and organizations to plan their activities, optimize resource allocation, and mitigate the impacts of extreme weather events[1]. Traditional weather forecasting methods, based on numerical weather prediction models, which made significant progress over the years, but they still face challenges in capturing the complexity of atmospheric processes and providing precise short-term forecasts[2]. With the advent of machine learning and its remarkable successes in various domains, the methods of weather forecasting has also witnessed a paradigm shift. Machine learning techniques, which involvetheuseofalgorithmsthat can learn patterns and relationships from data, offer new opportunities to enhance the accuracy and efficiency of weather predictions[3].This comprehensivesurvey explores the various machine learning mechanisms applied to weather forecasting and provides insights into the current state-of-the-art, challenges, and future directions. 1.1Historical Perspective of Weather Forecasting Weather forecasting is having a long history that dates back to ancient civilizations. Early methods of weatherprediction involved observing natural phenomena like cloud patterns, wind direction, and animal behavior. However, it was not until the 19th century that bring advancements in science and technology laid the foundation formodernmeteorology. The invention of weather instruments, such as barometers, thermometers, and anemometers, allowed for more systematic data collection[4]. 1.2 Machine Learning in Weather Forecasting: An Overview Machine learning, a subset of artificial intelligence, empowers computers to learn from data without being explicitly programmed. The flexibility and adaptability of machine learning algorithms have made them increasingly popular in various applications, including weather forecasting. Machine learning mechanisms can be broadly categorized into supervised, unsupervised, and reinforcement learning[5]. Supervised Learning:Insupervisedlearning,thealgorithmis trained on labeled data, where the input-output relationships are known. In the context of weather forecasting, historical weather data and corresponding observed outcomes (e.g., rainfall, temperature) that can be used to train the model. Once trained, the algorithm can predict the future weather conditions based on new input data[6]. Unsupervised Learning: Unsupervised learning deals with unlabeled data, and the algorithm aims to find patterns and structures within the data without explicit guidance.
2.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 12 | Dec 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 570 Clustering and dimensionality reduction are common unsupervised learning techniques that can be applied to weather data for pattern discovery and visualization. Reinforcement Learning:Inreinforcementlearning,anagent learns to make decisions byinteracting withanenvironment. The agent receives feedback in the form of rewards or penalties based on its actions, allowing it to improve its decision-makingprocessovertime.Althoughlesscommonin weather forecasting, reinforcement learning holds promise for optimizing weather-related decisions in specific scenarios. Fig-1: Weather Predictions Steps 2. Machine Learning Mechanisms in Weather Forecasting Machine learning mechanisms is applied to various aspects of weather forecasting to tackledifferentchallenges.Someof the key areas where machine learning has been utilized are: Numerical Weather Prediction (NWP): Machine learning models have been integrated into NWP models to improve the accuracy of weather simulations. Data assimilation techniques, such as the use of neural networks to combine observational data with NWP outputs, have shown promising results in refining forecasts. Weather Pattern Recognition: Machine learning algorithms that employed for weather pattern recognition, allowing meteorologists to identify and track weather systems more efficiently. Pattern recognition can aid in predicting. Short-term Forecasting: Machine learning techniques, especially ensemble methods, that are employed for short- term weather forecasting. Ensembles combine multiple models and predictions to reduce uncertaintiesand enhance forecast reliability. Extreme Weather Event Prediction: Machinelearningcanbe valuable in predicting extreme weather events, such as hurricanes, tornadoes, and heatwaves. These events can have significant socio-economic impacts, and early warning systems powered by machine learning models can help mitigate their effects. 3. CONCLUSIONS In conclusion, the integration of machine learning mechanisms into weather forecasting has opened up new possibilities for improving forecast accuracy and providing more reliable predictions.However,therearestill challenges to overcome, particularly regarding data quality, model interpretability, and computational efficiency. Addressing these challenges andcontinuedresearchinmachinelearning will lead to more robust weather forecasting systems, contributing to better preparedness and resilience in the face of weather-related challenges. Machine learning mechanisms offer promising solutions to enhance weather prediction accuracy and enable better decision-making in weather-sensitive industries. Challenges, such as data quality, model interpretability, and extreme event handling, warrant ongoing research and collaboration among meteorologists, data scientists, and machine learning experts. REFERENCES [1] Askari, M., & Askari, H. (2011). Time Series Grey System Prediction-based Models:GoldPriceForecasting. Trends in Applied Sciences Research, 6(11), 1287–1292. https://doi.org/10.3923/TASR.2011.1287.1292M. Young, The Technical Writer’s Handbook. Mill Valley, CA: University Science, 1989. [2] Bhardwaj, R., &Duhoon, V. (2019). Weather forecasting using soft computing techniques. 2018 International Conference on Computing, Power and Communication Technologies, GUCON 2018, 1111–1115. [3] Haghbin, M., Sharafati, A., Motta, D., Al-Ansari, N., &Noghani, M. H. M. (2021). Applications of soft computing models for predicting sea surface temperature: a comprehensive review and assessment. Progress in Earth and Planetary Science, 8(1). [4] Jayasingh, S. K., Gountia, D., Samal, N., &Chinara, P. K. (2021). A Novel Approach for Data Classification Using Neural Network. IETE Journal of Research.
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