开发基于机器学习的混合时间序列模型,以预测季节性调整的热浪预警
Md Mahin Uddin Qureshi1, Amrin Binte Ahmed2, Adisha Dulmini3
1Department of Statistics and Data Science, Jahangirnagar University, Dhaka, 1342, Bangladesh. mdmahin.stu2017@juniv.edu.
Scientific reports
|March 14, 2025
概括
准确的热浪预测对于公共卫生和环境可持续性至关重要. 一个新的季节调整机器学习 (ML) 模型,STL-ARIMA-LSTM,显著提高了预测热浪警告的准确性.
科学领域:
- 环境科学 环境科学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 热浪威胁着环境可持续性和公共健康,造成水资源短缺和景观干旱.
- 准确的热浪预测对于早期预警系统和灾难准备至关重要.
- 预测热浪警告涉及分析大规模的,高频的每日时间序列数据,提出独特的挑战.
研究的目的:
- 开发和评估基于先进的机器学习 (ML) 的混合模型,用于热浪预警预测.
- 为了解决高频时间序列数据在热浪预测中的复杂性.
- 将拟议模型的性能与传统和现有的ML方法进行比较.
主要方法:
- 提出了基于ML的混合模型和基于ML的季节性调整混合模型的两个算法.
- 基于LOESS (STL) 的综合季节趋势分解程序,使用时间序列和ML模型.
- 使用42年历史的每日数据,与ARIMA,ETS,TBATS,ANN,SVR,Prophet,RFR和LSTM进行了比较.
主要成果:
- 根据季节调整的基于ML的混合模型 (STL-ARIMA-LSTM) 显示出卓越的性能.
- STL-ARIMA-LSTM实现了最低的错误指标:MAE (0.8974),MAPE (2.9232),RMSE (1.1794),MASE (0.3814) 和ACF1 (0.0026),这些指标中的错误指标是最低的.
- 该模型准确预测热浪的数量和持续时间.
结论:
- 拟议的基于ML的季节性调整混合模型在热浪预测准确度方面取得了重大进展.
- 这种改进的预测可以更好地规划和实施防暑安全措施.
- 该研究强调了将STL分解与ML集成用于复杂时间序列分析的有效性.
相关概念视频
01:18Steps in Outbreak Investigation
102
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
102
01:07What is Weather?
18.0K
Overview
18.0K
02:46Quantifying Heat
53.7K
Thermal Energy
53.7K
01:03Prediction Intervals
2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.2K
01:17Precipitation and Co-precipitation
1.6K
Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
1.6K
01:14Temperature Measurement Sites
1.5K
A thermometer measures body temperature. The common sites for measuring body temperature are the oral cavity, axillary region, temporal artery, and skin surface, such as the forehead, abdomen, and axilla. True core body temperature is assessed in the rectum, tympanic membrane, pulmonary artery, esophagus, and urinary bladder.
Oral: When assessing oral temperature, the thermometer tip should be placed under the tongue in the posterior sublingual pocket. It offers accurate readings and can be...
Oral: When assessing oral temperature, the thermometer tip should be placed under the tongue in the posterior sublingual pocket. It offers accurate readings and can be...
1.5K