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首页> 外文会议>International Conference on Information Systems Design and Intelligence Applications >Time Series Forecasting to Predict Pollutants of Air, Water and Noise Using Deep Learning Methods
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Time Series Forecasting to Predict Pollutants of Air, Water and Noise Using Deep Learning Methods

机译：使用深度学习方法预测空气，水和噪声污染物的时间序列预测

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This paper analyzes the long-term air, water and noise pollution monitoring data using autoregressive integrated moving averages (ARIMA) modeling and an artificial neural network called long short-term memory network (LSTM). Different features of air, water and noise are predicted using these modeling techniques. This is known as time series forecasting. The accuracies of both the modeling techniques are compared with each other applied on the same datasets. We compare both the modeling techniques, and the models are evaluated with metric root-mean-squared error (RMSE) which is also called the objective function or loss function in ML lingo. The algorithm, equipped with prominent accuracy, is used to predict the future values of different features of air, water and noise. These predicted values are then compared with the health statistics, globally defined by the World Health Organization (WHO), to determine a fit and healthy life of an average human being. The harmful effects, possible disease threats and their precautions are also listed if the predicted values do not lie in the risk-free zone defined by the WHO. Most of the air, water and noise dataset is provided by the Central Pollution Control Board, Govt of India (CPCB) online. We will be using Python to execute this project.
机译：本文分析了使用自回归综合移动平均线（ARIMA）建模和称为长短期内存网络（LSTM）的人工神经网络的长期空气，水和噪声污染监测数据。使用这些建模技术预测空气，水和噪声的不同特征。这称为时间序列预测。将建模技术的精度与在同一数据集上施加的彼此进行比较。我们比较建模技术，并且使用度量根均方向误差（RMSE）评估模型，该错误也称为ML Lingo中的目标函数或损耗功能。配备突出精度的算法用于预测空气，水和噪声的不同特征的未来值。然后将这些预测值与世界卫生组织（世卫组织）全球定义的健康统计数据进行比较，以确定平均人类的适合和健康的生活。如果预测的值不在由世卫组织定义的无风险区域中，也会列出有害影响，可能的疾病威胁及其预防措施。大多数空气，水和噪声数据集由印度政府（CPCB）在线提供的中央污染控制委员会提供。我们将使用Python执行此项目。

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