東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Machine Learning Models for Detectin...

Fasnacht, Angela Maria.

FindBook

Google Book

Amazon

博客來

Machine Learning Models for Detecting Pesticides in Chlorinated Drinking Water Distribution Systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Learning Models for Detecting Pesticides in Chlorinated Drinking Water Distribution Systems./
作者:	Fasnacht, Angela Maria.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	293 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-04, Section: A.
Contained By:	Dissertations Abstracts International85-04A.
標題:	Water resources management. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30688571
ISBN:	9798380609470

Machine Learning Models for Detecting Pesticides in Chlorinated Drinking Water Distribution Systems.
Fasnacht, Angela Maria.

Machine Learning Models for Detecting Pesticides in Chlorinated Drinking Water Distribution Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 293 p.

Source: Dissertations Abstracts International, Volume: 85-04, Section: A.

Thesis (Ph.D.)--Drexel University, 2023.

This item must not be sold to any third party vendors.

This dissertation encompasses anomaly detection, pesticide kinetics, and optimized machine learning models. It demonstrates the potential of machine learning techniques in predicting the occurrence of pesticides in chlorinated drinking water distribution systems. By using data from online sensors that capture water quality parameters from a prototype drinking water system, in which pesticides such as glyphosate, Dicamba, and Aldicarb were introduced, regression models were successfully created that were capable of identifying anomalies associated with pesticide presence or their byproducts in an aqueous environment rich in chlorine. In total, six regression algorithms-Decision Tree Regressor (DTR), Support Vector Regression (SVR), KNN Regressor (KNNR), Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), and Ada Boost Regressor (ABR)-were created and assessed as prediction models for leveraging historical data to uncover patterns between input features and target variables, to predict pesticide presence in the piped system ultimately. This thesis project employed data preprocessing, division into training and testing subsets, feature selection, and hyperparameter tuning methods to enhance model accuracy and efficiency. For example, the evaluation of the six regression models was assessed through metrics such as R-squared (R2 ), root mean square error (RMSE), and mean absolute error (MAE). The random forest regression (RFR) and gradient boosting regressor (GBR) algorithms demonstrate promising R2 scores for predicting glyphosate and Aldicarb. For Dicamba, meticulous hyperparameter tuning resulted in the RFR algorithm achieving an impressive R2 score of 0.87, closely followed by KNNR and GBR with R2 scores of 0.86, underscoring the critical role of hyperparameter tuning in optimizing the predictive capabilities of regression algorithms. By effectively integrating machine learning techniques with water quality data, this dissertation also offers valuable insights into water quality assessment, emphasizing the potential for early detection of pesticide contamination within drinking water distribution systems. In addition to machine learning, early logistic regression detection was used to develop a model for identifying potential pesticide events. While the models developed in this dissertation have the potential to contribute significantly to the field of innovative contamination detection, promoting public health, and advancing sustainability, it is essential to acknowledge the limitation of the datasets used to develop the models and the interpretability of results of the models, to assess their potential benefits and shortcomings.

ISBN: 9798380609470Subjects--Topical Terms:

794747
Water resources management.
Subjects--Index Terms:

Drinking water

Machine Learning Models for Detecting Pesticides in Chlorinated Drinking Water Distribution Systems.
LDR:03950nmm a2200409 4500 001 2393850
005 20240604073607.5
006 m o d
007 cr#unu||||||||
008 251215s2023 ||||||||||||||||| ||eng d
020 $a 9798380609470
035 $a (MiAaPQ)AAI30688571
035 $a AAI30688571
040 $a MiAaPQ $c MiAaPQ
100 1 $a Fasnacht, Angela Maria. $3 3763330
245 1 0 $a Machine Learning Models for Detecting Pesticides in Chlorinated Drinking Water Distribution Systems.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 293 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-04, Section: A.
500 $a Advisor: Sales, Christopher.
502 $a Thesis (Ph.D.)--Drexel University, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a This dissertation encompasses anomaly detection, pesticide kinetics, and optimized machine learning models. It demonstrates the potential of machine learning techniques in predicting the occurrence of pesticides in chlorinated drinking water distribution systems. By using data from online sensors that capture water quality parameters from a prototype drinking water system, in which pesticides such as glyphosate, Dicamba, and Aldicarb were introduced, regression models were successfully created that were capable of identifying anomalies associated with pesticide presence or their byproducts in an aqueous environment rich in chlorine. In total, six regression algorithms-Decision Tree Regressor (DTR), Support Vector Regression (SVR), KNN Regressor (KNNR), Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), and Ada Boost Regressor (ABR)-were created and assessed as prediction models for leveraging historical data to uncover patterns between input features and target variables, to predict pesticide presence in the piped system ultimately. This thesis project employed data preprocessing, division into training and testing subsets, feature selection, and hyperparameter tuning methods to enhance model accuracy and efficiency. For example, the evaluation of the six regression models was assessed through metrics such as R-squared (R2 ), root mean square error (RMSE), and mean absolute error (MAE). The random forest regression (RFR) and gradient boosting regressor (GBR) algorithms demonstrate promising R2 scores for predicting glyphosate and Aldicarb. For Dicamba, meticulous hyperparameter tuning resulted in the RFR algorithm achieving an impressive R2 score of 0.87, closely followed by KNNR and GBR with R2 scores of 0.86, underscoring the critical role of hyperparameter tuning in optimizing the predictive capabilities of regression algorithms. By effectively integrating machine learning techniques with water quality data, this dissertation also offers valuable insights into water quality assessment, emphasizing the potential for early detection of pesticide contamination within drinking water distribution systems. In addition to machine learning, early logistic regression detection was used to develop a model for identifying potential pesticide events. While the models developed in this dissertation have the potential to contribute significantly to the field of innovative contamination detection, promoting public health, and advancing sustainability, it is essential to acknowledge the limitation of the datasets used to develop the models and the interpretability of results of the models, to assess their potential benefits and shortcomings.
590 $a School code: 0065.
650 4 $a Water resources management. $3 794747
650 4 $a Environmental engineering. $3 548583
650 4 $a Sustainability. $3 1029978
653 $a Drinking water
653 $a Emergent contaminats
653 $a Machine learning
653 $a Online sensors
653 $a Pesticides
690 $a 0595
690 $a 0775
690 $a 0640
690 $a 0800
710 2 $a Drexel University. $b Environmental Engineering (College of Engineering). $3 3194359
773 0 $t Dissertations Abstracts International $g 85-04A.
790 $a 0065
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30688571