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One of the biggest environmental threats for food production, water availability, forest biodiversity and livelihoods is climate change. To understand phenomena such as rainfall, humidity and temperature, time series forecasting is the very important method in numerous applications in meteorology and other environmental areas. The weather alert plays an important role for everything. To solve the problems of the bad weather conditions, scientists all over the world have made many research efforts. Therefore, weather data collection and data analysis become more important for human beings. Three main classes of utilizing method for data prediction are deep learning, machine learning and statistical. For time series monthly prediction of weather alerts in Sittway: SUNNY, CLOUDY and RAINY has been provided for human beings, plants and non-living things. According to the comparison, the MSE value of LSTM model is the best one for the error calculation.

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Monthly Weather Data Collection and Alert System Using Time Series LSTM Model

  • 会议论文

  • 发表日期：

    2025年

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  • San Nyein Khine,
  • Zaw Tun,
  • Ye Naing

摘要

One of the biggest environmental threats for food production, water availability, forest biodiversity and livelihoods is climate change. To understand phenomena such as rainfall, humidity and temperature, time series forecasting is the very important method in numerous applications in meteorology and other environmental areas. The weather alert plays an important role for everything. To solve the problems of the bad weather conditions, scientists all over the world have made many research efforts. Therefore, weather data collection and data analysis become more important for human beings. Three main classes of utilizing method for data prediction are deep learning, machine learning and statistical. For time series monthly prediction of weather alerts in Sittway: SUNNY, CLOUDY and RAINY has been provided for human beings, plants and non-living things. According to the comparison, the MSE value of LSTM model is the best one for the error calculation.