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将ARIMA和各种深度学习模型进行比较,用于伊朗德兹河的长期水质指数预测

Amir Reza R Niknam¹, Maryam Sabaghzadeh¹, Ali Barzkar¹

¹Department of Civil Engineering, Water Resources Management Engineering, Yazd University, Yazd, Iran.

Environmental science and pollution research international

|February 14, 2024

概括

准确的水质预测对于管理稀缺资源至关重要. 深度学习模型在预测德兹河的表现优于传统方法.

科学领域:

环境科学环境科学
水文学的水文学
数据科学数据科学数据科学

背景情况:

水资源短缺是一个关键的全球问题,特别是在发展中国家.
精确且具有成本效益的水质监测和预测是必不可少的.
伊朗的德兹河面临的挑战需要先进的水资源管理策略.

研究的目的:

利用历史数据调查和预测每月的河水质量指数 (WQI).
为了比较传统时间序列模型的性能与深度学习方法用于WQI预测.
确定最有效的方法来预测未来的水质趋势在德兹河.

主要方法:

利用来自四个Dez河站 (2010-2020) 的每日水质数据来计算WQI.
应用了香农 Entropy 方法来确定十个水质参数的权重.
使用自回归集成移动平均 (ARIMA) 模型和五种深度学习模型 (Simple_RNN,LSTM,CNN,GRU,MLP) 进行预测.
使用根平均平方误差 (RMSE),平均绝对误差 (MAE),平均平方误差 (MSE) 和平均绝对百分比误差 (MAPE) 评估模型性能.

主要成果:

与深度学习模型相比,ARIMA模型的预测准确性较低.
深度学习模型 (Simple_RNN,LSTM,CNN,GRU,MLP) 在WQI预测中表现相似且高性能.
针对ARIMA的特定错误指标包括MSE (81.66),RMSE (9.037),MAE (6.376) 和MAPE (6.749).这些错误指标包括MSE (81.66),RMSE (9.037),MAE (6.376) 和MAPE (6.749) 等.

结论:

深度学习模型为河水质量指数预测提供了比ARIMA等传统方法更高的准确性.
这些发现为水资源管理和政策制定在面临水资源短缺的地区提供了宝贵的见解.
实施先进的预测模型可以帮助采取主动措施,改善河流的淡水质量.

关键词:

在阿里马,阿里马就是阿里马.深度学习是一种深度学习.德斯河德斯河是一条河流.时间表 EWQIII 时间序列时间序列水的质量水的质量

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