通过整合人口和社会经济因素来进行固体废物生成的多模型预测方法:印度普拉雅格拉杰的一个案例研究
Atul Srivastava1, Pawan Kumar Jha2
1Centre of Environmental Studies, University of Allahabad, Prayagraj, Uttar Pradesh, India, 211002.
Environmental monitoring and assessment
|May 30, 2023
概括
预测印度普拉雅格拉吉的城市固体废物 (MSW) 生产对于废物管理至关重要. 长短期记忆 (LSTM) 模型准确地预测了由人口和社会经济因素驱动的未来MSW增加.
科学领域:
- 环境科学 环境科学
- 城市规划 城市规划
- 数据科学数据科学数据科学
背景情况:
- 有效的城市固体废物 (MSW) 管理需要准确预测废物产生.
- 社会经济因素显著影响MSW生产率,需要将其纳入预测模型.
研究的目的:
- 为了在印度的普拉雅格拉杰 (Prayagraj) 项目未来的MSW产品.
- 确定和评估关键社会经济驱动因素对MSW产生的影响.
- 为了比较长短期记忆 (LSTM),自行回归集成移动平均 (ARIMA) 和增量增量模型 (IIM) 的预测准确度.
主要方法:
- 利用中央污染控制委员会 (CPCB) (1997-2015) 的历史MSW数据.
- 雇佣了LSTM,ARIMA和IIM用于废物产生预测.
- 整合了九个社会经济变量,并应用了相关性和模糊逻辑来进行影响分析.
主要成果:
- 人口,就业和家庭数量被确定为影响废物产生的重要因素.
- 与ARIMA (R2 = 0.72) 和IIM (R2 = 0.70) 相比,LSTM模型显示出更高的准确性 (R2 = 0.92).
- 根据LSTM的预测,到2031年,Prayagraj的人口将达到160万,MSW产生的预计增加70.6%.
结论:
- LSTM是预测印度普莱亚格拉吉MSW产生的最有效模型.
- 社会经济因素在MSW产生的趋势中发挥着关键作用.
- 预计的人口增长和废物产生增加需要积极的废物管理策略.
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