{"title":"用可解释人工智能理解污水处理厂污泥的机器学习预测。","authors":"Fuad Bin Nasir, Jin Li","doi":"10.1002/wer.11136","DOIUrl":null,"url":null,"abstract":"<p><p>This study investigates the use of machine learning (ML) models for wastewater treatment plant (WWTP) sludge predictions and explainable artificial intelligence (XAI) techniques for understanding the impact of variables behind the prediction. Three ML models, random forest (RF), gradient boosting machine (GBM), and gradient boosting tree (GBT), were evaluated for their performance using statistical indicators. Input variable combinations were selected through different feature selection (FS) methods. XAI techniques were employed to enhance the interpretability and transparency of ML models. The results suggest that prediction accuracy depends on the choice of model and the number of variables. XAI techniques were found to be effective in interpreting the decisions made by each ML model. This study provides an example of using ML models in sludge production prediction and interpreting models applying XAI to understand the factors influencing it. Understandable interpretation of ML model prediction can facilitate targeted interventions for process optimization and improve the efficiency and sustainability of wastewater treatment processes. PRACTITIONER POINTS: Explainable artificial intelligence can play a crucial role in promoting trust between machine learning models and their real-world applications. Widely practiced machine learning models were used to predict sludge production of a United States wastewater treatment plant. Feature selection methods can reduce the required number of input variables without compromising model accuracy. Explainable artificial intelligence techniques can explain driving variables behind machine learning prediction.</p>","PeriodicalId":23621,"journal":{"name":"Water Environment Research","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Understanding machine learning predictions of wastewater treatment plant sludge with explainable artificial intelligence.\",\"authors\":\"Fuad Bin Nasir, Jin Li\",\"doi\":\"10.1002/wer.11136\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This study investigates the use of machine learning (ML) models for wastewater treatment plant (WWTP) sludge predictions and explainable artificial intelligence (XAI) techniques for understanding the impact of variables behind the prediction. Three ML models, random forest (RF), gradient boosting machine (GBM), and gradient boosting tree (GBT), were evaluated for their performance using statistical indicators. Input variable combinations were selected through different feature selection (FS) methods. XAI techniques were employed to enhance the interpretability and transparency of ML models. The results suggest that prediction accuracy depends on the choice of model and the number of variables. XAI techniques were found to be effective in interpreting the decisions made by each ML model. This study provides an example of using ML models in sludge production prediction and interpreting models applying XAI to understand the factors influencing it. Understandable interpretation of ML model prediction can facilitate targeted interventions for process optimization and improve the efficiency and sustainability of wastewater treatment processes. 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引用次数: 0
摘要
本研究调查了机器学习(ML)模型在污水处理厂(WWTP)污泥预测中的应用,以及可解释人工智能(XAI)技术对预测背后变量影响的理解。使用统计指标对随机森林(RF)、梯度提升机(GBM)和梯度提升树(GBT)这三种 ML 模型的性能进行了评估。通过不同的特征选择(FS)方法选择输入变量组合。采用了 XAI 技术来增强 ML 模型的可解释性和透明度。结果表明,预测精度取决于模型的选择和变量的数量。研究发现,XAI 技术可有效解释每个 ML 模型做出的决策。本研究提供了在污泥产量预测中使用 ML 模型的实例,并应用 XAI 对模型进行解释,以了解影响因素。对 ML 模型预测进行可理解的解释可促进对工艺优化进行有针对性的干预,并提高污水处理工艺的效率和可持续性。实践者观点:可解释的人工智能在促进机器学习模型与实际应用之间的信任方面发挥着至关重要的作用。广泛应用的机器学习模型被用于预测美国一家污水处理厂的污泥产量。特征选择方法可以在不影响模型准确性的情况下减少所需的输入变量数量。可解释的人工智能技术可以解释机器学习预测背后的驱动变量。
Understanding machine learning predictions of wastewater treatment plant sludge with explainable artificial intelligence.
This study investigates the use of machine learning (ML) models for wastewater treatment plant (WWTP) sludge predictions and explainable artificial intelligence (XAI) techniques for understanding the impact of variables behind the prediction. Three ML models, random forest (RF), gradient boosting machine (GBM), and gradient boosting tree (GBT), were evaluated for their performance using statistical indicators. Input variable combinations were selected through different feature selection (FS) methods. XAI techniques were employed to enhance the interpretability and transparency of ML models. The results suggest that prediction accuracy depends on the choice of model and the number of variables. XAI techniques were found to be effective in interpreting the decisions made by each ML model. This study provides an example of using ML models in sludge production prediction and interpreting models applying XAI to understand the factors influencing it. Understandable interpretation of ML model prediction can facilitate targeted interventions for process optimization and improve the efficiency and sustainability of wastewater treatment processes. PRACTITIONER POINTS: Explainable artificial intelligence can play a crucial role in promoting trust between machine learning models and their real-world applications. Widely practiced machine learning models were used to predict sludge production of a United States wastewater treatment plant. Feature selection methods can reduce the required number of input variables without compromising model accuracy. Explainable artificial intelligence techniques can explain driving variables behind machine learning prediction.
期刊介绍:
Published since 1928, Water Environment Research (WER) is an international multidisciplinary water resource management journal for the dissemination of fundamental and applied research in all scientific and technical areas related to water quality and resource recovery. WER''s goal is to foster communication and interdisciplinary research between water sciences and related fields such as environmental toxicology, agriculture, public and occupational health, microbiology, and ecology. In addition to original research articles, short communications, case studies, reviews, and perspectives are encouraged.