机器学习在大坝水位日常预测中的应用

Q3 Environmental Science Tikrit Journal of Engineering Sciences Pub Date : 2023-11-25 DOI:10.25130/tjes.30.4.9
Mohammad Abdullah Almubaidin, A. Ahmed, Chris Aaron Anak Winston, A. El-shafie
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引用次数: 0

摘要

环境的不断变化使得提前预测水位具有挑战性。尽管物理模型是定义水文过程和实施物理系统变化的最常用方法,但它也有一些实际局限性。多项研究表明,机器学习作为一种数据驱动的水文过程预测方法,能带来更可靠的数据,而且比传统模型更高效。本研究根据大坝水位的历史数据,开发了七种机器学习算法来预测大坝的每日水位。研究了多种输入组合以提高模型的灵敏度,并使用统计指标来评估所开发模型的可靠性。对多种输入情况下的多种模型的研究表明,使用七天滞后输入训练的袋装树模型精度最高。袋装树模型的均方根误差为 0.13953,训练时间不到 10 秒。其效率和准确性使该模型在其他训练模型中脱颖而出。在实地部署该模型后,就可以对大坝水位进行预测,从而帮助缓解与供水有关的问题。
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Application of Machine Learning for Daily Forecasting Dam Water Levels
The evolving character of the environment makes it challenging to predict water levels in advance. Despite being the most common approach for defining hydrologic processes and implementing physical system changes, the physics-based model has some practical limitations. Multiple studies have shown that machine learning, a data-driven approach to forecast hydrological processes, brings about more reliable data and is more efficient than traditional models. In this study, seven machine learning algorithms were developed to predict a dam water level daily based on the historical data of the dam water level. Multiple input combinations were investigated to improve the model’s sensitivity, and statistical indicators were used to assess the reliability of the developed model. The study of multiple models with multiple input scenarios suggested that the bagged trees model trained with seven days of lagged input provided the highest accuracy. The bagged tree model achieved an RMSE of 0.13953, taking less than 10 seconds to train. Its efficiency and accuracy made this model stand out from the rest of the trained model. With the deployment of this model on the field, the dam water level predictions can be made to help mitigate issues relating to water supply.
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来源期刊
CiteScore
1.50
自引率
0.00%
发文量
56
审稿时长
8 weeks
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