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Hou F, Liu S, Yin WX, Gan LL, Pang HT, Lv JQ, Liu Y, Wang HC. Machine learning for high-precision simulation of dissolved organic matter in sewer: Overcoming data restrictions with generative adversarial networks. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 947:174469. [PMID: 38972419 DOI: 10.1016/j.scitotenv.2024.174469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 06/29/2024] [Accepted: 07/01/2024] [Indexed: 07/09/2024]
Abstract
Understanding the transformation process of dissolved organic matter (DOM) in the sewer is imperative for comprehending material circulation and energy flow within the sewer. The machine learning (ML) model provides a feasible way to comprehend and simulate the DOM transformation process in the sewer. In contrast, the model accuracy is limited by data restriction. In this study, a novel framework by integrating generative adversarial network algorithm-machine learning models (GAN-ML) was established to overcome the drawbacks caused by the data restriction in the simulation of the DOM transformation process, and humification index (HIX) was selected as the output variable to evaluate the model performance. Results indicate that the GAN algorithm's virtual dataset could generally enhance the simulation performance of regression models, deep learning models, and ensemble models for the DOM transformation process. The highest prediction accuracy on HIX (R2 of 0.5389 and RMSE of 0.0273) was achieved by the adaptive boosting model which belongs to ensemble models trained by the virtual dataset of 1000 samples. Interpretability analysis revealed that dissolved oxygen (DO) and pH emerge as critical factors warranting attention for the future development of management strategies to regulate the DOM transformation process in sewers. The integrated framework proposed a potential approach for the comprehensive understanding and high-precision simulation of the DOM transformation process, paving the way for advancing sewer management strategy under data restriction.
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Affiliation(s)
- Feng Hou
- Xinkai Water Environment Investment Co., Lt, Beijing 101100, China
| | - Shuai Liu
- The State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China
| | - Wan-Xin Yin
- College of the Environment, Liaoning University, Shenyang 110036, China.
| | - Li-Li Gan
- Xinkai Water Environment Investment Co., Lt, Beijing 101100, China
| | - Hong-Tao Pang
- Xinkai Water Environment Investment Co., Lt, Beijing 101100, China
| | - Jia-Qiang Lv
- The State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China
| | - Ying Liu
- Xinkai Water Environment Investment Co., Lt, Beijing 101100, China
| | - Hong-Cheng Wang
- The State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China.
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2
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Liu D, Huang L, Jia L, Li S, Wang P. Evaluation of best management practices for mitigating harmful algal blooms risk in an agricultural lake basin using a watershed model integrated with Bayesian Network approach. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 364:121433. [PMID: 38878574 DOI: 10.1016/j.jenvman.2024.121433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 05/07/2024] [Accepted: 06/07/2024] [Indexed: 06/24/2024]
Abstract
Lake eutrophication caused by nitrogen and phosphorus has led to frequent harmful algal blooms (HABs), especially under the unknown challenges of climate change, which have seriously damaged human life and property. In this study, a coupled SWAT-Bayesian Network (SWAT-BN) model framework was constructed to elucidate the mechanisms between non-point source nitrogen pollution in agricultural lake watersheds and algal activities. A typical agricultural shallow lake basin, the Taihu Basin (TB), China, was chosen in this study, aiming to investigate the effectiveness of best management practices (BMPs) in controlling HABs risks in TB. By modeling total nitrogen concentration of Taihu Lake from 2007 to 2022 with four BMPs (filter strips, grassed waterway, fertilizer application reduction and no-till agriculture), the results indicated that fertilizer application reduction proved to be the most effective BMP with 0.130 of Harmful Algal Blooms Probability Reduction (HABs-PR) when reducing 40% of fertilizer, followed by filter strips with 0.01 of HABs-PR when 4815ha of filter strips were conducted, while grassed waterway and no-till agriculture showed no significant effect on preventing HABs. Furthermore, the combined practice between 40% fertilizer application reduction and 4815ha filter strips construction showed synergistic effects with HABs-PR increasing to 0.171. Precipitation and temperature data were distorted to model scenarios of extreme events. As a result, the combined approach outperformed any single BMP in terms of robustness under extreme climates. This research provides a watershed-level perspective on HABs risks mitigation and highlights the strategies to address HABs under the influence of climate change.
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Affiliation(s)
- Dingwu Liu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, China
| | - Lei Huang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, China.
| | - Ling Jia
- Faculty of Civil Engineering and Mechanics, Jiangsu University, Zhenjiang, 212013, China
| | - Shenshen Li
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China
| | - Peng Wang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, China; Faculty of Civil Engineering and Mechanics, Jiangsu University, Zhenjiang, 212013, China.
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Dong L, Liu P, Qi Z, Lin J, Duan M. Development and validation of a machine-learning model for predicting the risk of death in sepsis patients with acute kidney injury. Heliyon 2024; 10:e29985. [PMID: 38699001 PMCID: PMC11064448 DOI: 10.1016/j.heliyon.2024.e29985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 04/17/2024] [Accepted: 04/18/2024] [Indexed: 05/05/2024] Open
Abstract
The mortality rate of patients with sepsis-induced acute kidney injury (S-AKI) is notably elevated. The initial categorization of prognostic indicators has a beneficial impact on elucidating and enhancing disease outcomes. This study aimed to predict the mortality risk of S-AKI patients by employing machine learning techniques. The sample size determined by a four-step procedure yielded 1508 samples. The research design necessitated the inclusion of individuals with S-AKI from the Medical Information Mart for Intensive Care (MIMIC)-IV database. The patients were initially admitted to the Intensive Care Unit (ICU) for their hospital stay. Additionally, these patients (aged from 18 to 89 years old) had encountered S-AKI on the day of their admittance. Forty-two predictive factors were analyzed, with hospitalization death as the outcome variable. The training set (4001 cases) consisted of 70 % of the participants, and the remaining (1714 cases) participants were allocated to the validation set. Furthermore, an additional validation set (MIMIC-III) consisted of 1757 patients from the MIMIC-III database. Moreover, an external validation set from the Intensive Care Department of Beijing Friendship Hospital (BFH) comprised 72 patients. Six machine learning models were employed in the prediction, namely the logistic, lasso, rpart, random forest, xgboost, and artificial neural network models. The comparative efficacy of the newly developed model in relation to the APACHE II model for predicting mortality risk was also assessed. The XGBoost model exhibited a superior performance with the training set. With the internal validation set and the two external validation sets (MIMIC-III and BFH), the xgboost algorithm demonstrated the highest performance. Meanwhile, APACHE II performed poorly at predicting the mortality risk with the BFH validation set. The mortality risk was influenced by three primary clinical parameters: urine volume, lactate, and Glasgow Coma Scale (GCS) score. Thus, we developed a prediction model for the risk of death among S-AKI patients that has an improved performance compared to previous models and is a potentially valuable tool for S-AKI prediction and treatment in the clinic.
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Affiliation(s)
- Lei Dong
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Pei Liu
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Zhili Qi
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Jin Lin
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Meili Duan
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
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Bărbulescu A, Barbeş L, Dumitriu CȘ. Advances in Water, Air and Soil Pollution Monitoring, Modeling and Restoration. TOXICS 2024; 12:244. [PMID: 38668467 PMCID: PMC11054471 DOI: 10.3390/toxics12040244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 03/24/2024] [Indexed: 04/29/2024]
Abstract
Global pollution demands continuous attention and concerted efforts to reduce its effects [...].
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Affiliation(s)
- Alina Bărbulescu
- Department of Civil Engineering, Transilvania University of Brașov, 5 Turnului Str., 500152 Brașov, Romania;
| | - Lucica Barbeş
- Department of Chemistry and Chemical Engineering, “Ovidius” University of Constanța, 124 Mamaia Bd., 900527 Constanța, Romania
- Doctoral School of Biotechnical Systems Engineering, Politehnica University of Bucharest, 313, Splaiul Independenţei, 060042 Bucharest, Romania
| | - Cristian Ștefan Dumitriu
- Faculty of Mechanical Engineering and Robotics in Constructions, Technical University of Civil Engineering, Calea Plevnei 59, 021242 Bucharest, Romania
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Kim JH, Lee H, Byeon S, Shin JK, Lee DH, Jang J, Chon K, Park Y. Machine Learning-Based Early Warning Level Prediction for Cyanobacterial Blooms Using Environmental Variable Selection and Data Resampling. TOXICS 2023; 11:955. [PMID: 38133356 PMCID: PMC10747537 DOI: 10.3390/toxics11120955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 11/14/2023] [Accepted: 11/20/2023] [Indexed: 12/23/2023]
Abstract
Many countries have attempted to mitigate and manage issues related to harmful algal blooms (HABs) by monitoring and predicting their occurrence. The infrequency and duration of HABs occurrence pose the challenge of data imbalance when constructing machine learning models for their prediction. Furthermore, the appropriate selection of input variables is a significant issue because of the complexities between the input and output variables. Therefore, the objective of this study was to improve the predictive performance of HABs using feature selection and data resampling. Data resampling was used to address the imbalance in the minority class data. Two machine learning models were constructed to predict algal alert levels using 10 years of meteorological, hydrodynamic, and water quality data. The improvement in model accuracy due to changes in resampling methods was more noticeable than the improvement in model accuracy due to changes in feature selection methods. Models constructed using combinations of original and synthetic data across all resampling methods demonstrated higher prediction performance for the caution level (L-1) and warning level (L-2) than models constructed using the original data. In particular, the optimal artificial neural network and random forest models constructed using combinations of original and synthetic data showed significantly improved prediction accuracy for L-1 and L-2, representing the transition from normal to bloom formation states in the training and testing steps. The test results of the optimal RF model using the original data indicated prediction accuracies of 98.8% for L0, 50.0% for L1, and 50.0% for L2. In contrast, the optimal random forest model using the Synthetic Minority Oversampling Technique-Edited Nearest Neighbor (ENN) sampling method achieved accuracies of 85.0% for L0, 85.7% for L1, and 100% for L2. Therefore, applying synthetic data can address the imbalance in the observed data and improve the detection performance of machine learning models. Reliable predictions using improved models can support the design of management practices to mitigate HABs in reservoirs and ultimately ensure safe and clean water resources.
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Affiliation(s)
- Jin Hwi Kim
- School of Civil and Environmental Engineering, Konkuk University, Gwangjin-gu, Seoul 05029, Republic of Korea; (J.H.K.); (H.L.); (S.B.)
| | - Hankyu Lee
- School of Civil and Environmental Engineering, Konkuk University, Gwangjin-gu, Seoul 05029, Republic of Korea; (J.H.K.); (H.L.); (S.B.)
| | - Seohyun Byeon
- School of Civil and Environmental Engineering, Konkuk University, Gwangjin-gu, Seoul 05029, Republic of Korea; (J.H.K.); (H.L.); (S.B.)
| | - Jae-Ki Shin
- Busan Region Branch Office of the Nakdong River, Korea Water Resources Corporation (K-Water), Saha-Gu, Busan 49300, Republic of Korea;
| | - Dong Hoon Lee
- Department of Civil and Environmental Engineering, Dongguk University-Seoul, 30, Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea;
| | - Jiyi Jang
- Division of Atmospheric Sciences, Korea Polar Research Institute, 26, Songdomirae-ro, Yeonsu-gu, Incheon 21990, Republic of Korea;
| | - Kangmin Chon
- Department of Environmental Engineering, Kangwon National University, Gangwon-do, Chuncheon 24341, Republic of Korea;
- Department of Integrated Energy and Infra System, Kangwon National University, Gangwon-do, Chuncheon 24341, Republic of Korea
| | - Yongeun Park
- School of Civil and Environmental Engineering, Konkuk University, Gwangjin-gu, Seoul 05029, Republic of Korea; (J.H.K.); (H.L.); (S.B.)
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Zeinolabedini Rezaabad M, Lacey H, Marshall L, Johnson F. Influence of resampling techniques on Bayesian network performance in predicting increased algal activity. WATER RESEARCH 2023; 244:120558. [PMID: 37666153 DOI: 10.1016/j.watres.2023.120558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 08/10/2023] [Accepted: 08/30/2023] [Indexed: 09/06/2023]
Abstract
Early warning of increased algal activity is important to mitigate potential impacts on aquatic life and human health. While many methods have been developed to predict increased algal activity, an ongoing issue is that severe algal blooms often occur with low frequency in water bodies. This results in imbalanced data sets available for model specification, leading to poor predictions of the frequency of increased algal activity. One approach to address this is to resample data sets of increased algal activity to increase the prevalence of higher than normal algal activity in calibration data and ultimately improve model predictions. This study aims to investigate the use of resampling techniques to address the imbalanced dataset and determine if such methods can improve the prediction of increased algal activity. Three techniques were investigated, Kmeans under-sampling (US_Kmeans), synthetic minority over-sampling technique (SMOTE), and 'SMOTE and cluster-based under-sampling technique' (SCUT). The resampling methods were applied to a Bayesian network (BN) model of Lake Burragorang in New South Wales, Australia. The model was developed to predict chlorophyll-a (chl-a) using a range of water quality parameters as predictors. The original data and each of the balanced datasets were used for BN structures and parameter learning. The results showed that the best graphical structure was obtained by adding synthetic data from SMOTE with the highest true positive rate (TPR) and area under the curve (AUC). When compared using a fixed graphical structure for the BN, all resampling techniques increased the ability of the BN to detect events with higher probability of increased algal activity. The resampling model results can also be used to better understand the most important influences on high chl-a concentrations and suggest future data collection and model development priorities.
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Affiliation(s)
- Maryam Zeinolabedini Rezaabad
- Water Research Centre, School of Civil and Environmental Engineering, University of New South Wales, Kensington, New South Wales, Australia; ARC Training Centre Data Analytics for Resources and Environments, School of Life and Environmental Sciences, The University of Sydney, Camperdown, New South Wales, Australia.
| | | | - Lucy Marshall
- Water Research Centre, School of Civil and Environmental Engineering, University of New South Wales, Kensington, New South Wales, Australia; ARC Training Centre Data Analytics for Resources and Environments, School of Life and Environmental Sciences, The University of Sydney, Camperdown, New South Wales, Australia; Faculty of Science and Engineering, Macquarie University, North Ryde, New South Wales, Australia
| | - Fiona Johnson
- Water Research Centre, School of Civil and Environmental Engineering, University of New South Wales, Kensington, New South Wales, Australia; ARC Training Centre Data Analytics for Resources and Environments, School of Life and Environmental Sciences, The University of Sydney, Camperdown, New South Wales, Australia
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Hong X, Lv J, Li Z, Xiong Y, Zhang J, Chen HF. Sequence-based machine learning method for predicting the effects of phosphorylation on protein-protein interactions. Int J Biol Macromol 2023; 243:125233. [PMID: 37290543 DOI: 10.1016/j.ijbiomac.2023.125233] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 06/02/2023] [Accepted: 06/03/2023] [Indexed: 06/10/2023]
Abstract
Protein phosphorylation, catalyzed by kinases, is an important biochemical process, which plays an essential role in multiple cell signaling pathways. Meanwhile, protein-protein interactions (PPI) constitute the signaling pathways. Abnormal phosphorylation status on protein can regulate protein functions through PPI to evoke severe diseases, such as Cancer and Alzheimer's disease. Due to the limited experimental evidence and high costs to experimentally identify novel evidence of phosphorylation regulation on PPI, it is necessary to develop a high-accuracy and user-friendly artificial intelligence method to predict phosphorylation effect on PPI. Here, we proposed a novel sequence-based machine learning method named PhosPPI, which achieved better identification performance (Accuracy and AUC) than other competing predictive methods of Betts, HawkDock and FoldX. PhosPPI is now freely available in web server (https://phosppi.sjtu.edu.cn/). This tool can help the user to identify functional phosphorylation sites affecting PPI and explore phosphorylation-associated disease mechanism and drug development.
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Affiliation(s)
- Xiaokun Hong
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jiyang Lv
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Zhengxin Li
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jian Zhang
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200025, China.
| | - Hai-Feng Chen
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China.
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Ohara H, Ito S, Takanami Y. Binary classification of users of electronic cigarettes and smokeless tobacco through biomarkers to assess similarity with current and former smokers: machine learning applied to the population assessment of tobacco and health study. BMC Public Health 2023; 23:589. [PMID: 36991369 PMCID: PMC10061900 DOI: 10.1186/s12889-023-15511-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 03/23/2023] [Indexed: 03/31/2023] Open
Abstract
BACKGROUND Exposure to harmful and potentially harmful constituents in cigarette smoke is a risk factor for cardiovascular and respiratory diseases. Tobacco products that could reduce exposure to these constituents have been developed. However, the long-term effects of their use on health remain unclear. The Population Assessment of Tobacco and Health (PATH) study is a population-based study examining the health effects of smoking and cigarette smoking habits in the U.S. POPULATION Participants include users of tobacco products, including electronic cigarettes and smokeless tobacco. In this study, we attempted to evaluate the population-wide effects of these products, using machine learning techniques and data from the PATH study. METHODS Biomarkers of exposure (BoE) and potential harm (BoPH) in cigarette smokers and former smokers in wave 1 of PATH were used to create binary classification machine-learning models that classified participants as either current (BoE: N = 102, BoPH: N = 428) or former smokers (BoE: N = 102, BoPH: N = 428). Data on the BoE and BoPH of users of electronic cigarettes (BoE: N = 210, BoPH: N = 258) and smokeless tobacco (BoE: N = 206, BoPH: N = 242) were input into the models to investigate whether these product users were classified as current or former smokers. The disease status of individuals classified as either current or former smokers was investigated. RESULTS The classification models for BoE and BoPH both had high model accuracy. More than 60% of participants who used either one of electronic cigarettes or smokeless tobacco were classified as former smokers in the classification model for BoE. Fewer than 15% of current smokers and dual users were classified as former smokers. A similar trend was found in the classification model for BoPH. Compared with those classified as former smokers, a higher percentage of those classified as current smokers had cardiovascular disease (9.9-10.9% vs. 6.3-6.4%) and respiratory diseases (19.4-22.2% vs. 14.2-16.7%). CONCLUSIONS Users of electronic cigarettes or smokeless tobacco are likely to be similar to former smokers in their biomarkers of exposure and potential harm. This suggests that using these products helps to reduce exposure to the harmful constituents of cigarettes, and they are potentially less harmful than conventional cigarettes.
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Affiliation(s)
- Hiromi Ohara
- Scientific Product Assessment Center, Japan Tobacco Inc, 6-2 Umegaoka, Aoba-ku, Yokohama, 227-8512, Kanagawa, Japan.
| | - Shigeaki Ito
- Scientific Product Assessment Center, Japan Tobacco Inc, 6-2 Umegaoka, Aoba-ku, Yokohama, 227-8512, Kanagawa, Japan
| | - Yuichiro Takanami
- Scientific Product Assessment Center, Japan Tobacco Inc, 6-2 Umegaoka, Aoba-ku, Yokohama, 227-8512, Kanagawa, Japan
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Zhang Y, Li L, Ren Z, Yu Y, Li Y, Pan J, Lu Y, Feng L, Zhang W, Han Y. Plant-scale biogas production prediction based on multiple hybrid machine learning technique. BIORESOURCE TECHNOLOGY 2022; 363:127899. [PMID: 36075348 DOI: 10.1016/j.biortech.2022.127899] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 08/28/2022] [Accepted: 08/31/2022] [Indexed: 06/15/2023]
Abstract
The parameters from full-scale biogas plants are highly nonlinear and imbalanced, resulting in low prediction accuracy when using traditional machine learning algorithms. In this study, a hybrid extreme learning machine (ELM) model was proposed to improve prediction accuracy by solving imbalanced data. The results showed that the best ELM model had a good prediction for validation data (R2 = 0.972), and the model was developed into the software (prediction error of 2.15 %). Furthermore, two parameters within a certain range (feed volume (FV) = 23-45 m3 and total volatile fatty acids of anaerobic digestion (TVFAAD) = 1750-3000 mg/L) were identified as the most important characteristics that positively affected biogas production. This study combines machine learning with data-balancing techniques and optimization algorithms to achieve accurate predictions of plant biogas production at various loads.
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Affiliation(s)
- Yi Zhang
- State Key Laboratory of Heavy Oil Processing, Beijing Key Laboratory of Biogas Upgrading Utilization, College of New Energy and Materials, China University of Petroleum Beijing (CUPB), Beijing 102249, PR China
| | - Linhui Li
- College of Artificial Intelligence, China University of Petroleum Beijing (CUPB), Beijing 102249, PR China
| | - Zhonghao Ren
- State Key Laboratory of Heavy Oil Processing, Beijing Key Laboratory of Biogas Upgrading Utilization, College of New Energy and Materials, China University of Petroleum Beijing (CUPB), Beijing 102249, PR China
| | - Yating Yu
- State Key Laboratory of Heavy Oil Processing, Beijing Key Laboratory of Biogas Upgrading Utilization, College of New Energy and Materials, China University of Petroleum Beijing (CUPB), Beijing 102249, PR China
| | - Yeqing Li
- State Key Laboratory of Heavy Oil Processing, Beijing Key Laboratory of Biogas Upgrading Utilization, College of New Energy and Materials, China University of Petroleum Beijing (CUPB), Beijing 102249, PR China.
| | - Junting Pan
- Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China
| | - Yanjuan Lu
- Beijing Fairyland Environmental Technology Co., Ltd, Beijing 100094, PR China
| | - Lu Feng
- NIBIO, Norwegian Institute of Bioeconomy Research, P.O. Box 115, N-1431 Ås, Norway
| | - Weijin Zhang
- School of Energy Science and Engineering, Central South University, Changsha 410083, PR China
| | - Yongming Han
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, PR China
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10
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Wade MW, Fisher M, Matich P. Comparison of two machine learning frameworks for predicting aggregatory behavior of sharks. J Appl Ecol 2022. [DOI: 10.1111/1365-2664.14273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Michael W. Wade
- Data Science Institute Vanderbilt University Nashville TN USA
| | - Mark Fisher
- Texas Parks and Wildlife Department, Coastal Fisheries Division, Rockport Marine Science Laboratory Rockport TX USA
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Mori M, Gonzalez Flores R, Suzuki Y, Nukazawa K, Hiraoka T, Nonaka H. Prediction of Microcystis Occurrences and Analysis Using Machine Learning in High-Dimension, Low-Sample-Size and Imbalanced Water Quality Data. HARMFUL ALGAE 2022; 117:102273. [PMID: 35944960 DOI: 10.1016/j.hal.2022.102273] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 05/20/2022] [Accepted: 06/06/2022] [Indexed: 06/15/2023]
Abstract
Machine learning, Deep learning, and water quality data have been used in recent years to predict the outbreak of harmful algae, especially Microcystis, and analyze outbreak causes. However, for various reasons, water quality data are often High-Dimension, Low-Sample- Size (HDLSS), meaning the sample size is lower than the number of dimensions. Moreover, imbalance problems may arise due to bias in the occurrence frequency of Microcystis. These problems make predicting the occurrence of Microcystis and analyzing its causes with machine learning difficult. In this study, a machine learning model that applies Feature Engineering (FE) and Feature Selection (FS) algorithms are used to predict outbreaks of Microcystis and analyze the outbreak factors from imbalanced HDLSS water quality data. The prediction performance was verified with binary classification to determine whether Microcystis would occur in the future by applying three machine learning models to four data patterns. The cause analysis of Microcystis occurrence was performed by visualizing the results of applying FE and FS. For the test data, the predictive performance of FE and FS methods was significantly better than that of the conventional method, with an accuracy of .108 points and an F-value of .691 points higher than the conventional method. A prediction performance increase was observed with a smaller model capacity. Data-driven analysis suggested that total nitrogen, chemical oxygen demand, chlorophyll-a, dissolved oxygen saturation, and water temperature are associated with Microcystis occurrences. The results also indicated that basic statistics of the water quality distribution (especially mean, standard deviation, and skewness) over a year, not the concentrations of water components, are related to the occurrence of Microcystis. These are new findings not found in previous studies and are expected to contribute significantly to future studies of algae. This study provides a method for analyzing water quality data with high-dimensionality and small samples, imbalance problems, or both.
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Affiliation(s)
- Masaya Mori
- Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka-shi, 940-2188, Niigata, Japan.
| | - Roberto Gonzalez Flores
- Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka-shi, 940-2188, Niigata, Japan
| | - Yoshihiro Suzuki
- University of Miyazaki, 1-1, Gakuen Kibanadai-nishi, Miyazaki-shi, 889-2192, Miyazaki, Japan
| | - Kei Nukazawa
- University of Miyazaki, 1-1, Gakuen Kibanadai-nishi, Miyazaki-shi, 889-2192, Miyazaki, Japan
| | - Toru Hiraoka
- University of Nagasaki, 1-1-1, Manabino, Nagayo, Nishi-Sonogi, 851-2130, Nagasaki, Japan
| | - Hirofumi Nonaka
- Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka-shi, 940-2188, Niigata, Japan
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Zhang S, Mi T, Wu Q, Luo Y, Grieneisen ML, Shi G, Yang F, Zhan Y. A data-augmentation approach to deriving long-term surface SO 2 across Northern China: Implications for interpretable machine learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 827:154278. [PMID: 35248628 DOI: 10.1016/j.scitotenv.2022.154278] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 02/22/2022] [Accepted: 02/27/2022] [Indexed: 06/14/2023]
Abstract
Until recently, Northern China was one of the most SO2 polluted regions in the world. The lack of long-term and spatially resolved surface SO2 data hinders retrospective evaluation of relevant environmental policies and human health effects. This study aims to derive the spatiotemporal distribution of surface SO2 across Northern China during 2005-2019. As "concept drift" causes substantial estimation bias in back-extrapolation, we propose a new approach named the robust back-extrapolation via data augmentation approach (RBE-DA) to model the long-term surface SO2. The results show that the population-weighted regional SO2 ([SO2]pw) increased from 2005 to 2007 and decreased steadily afterwards. The [SO2]pw decreased by 80.4% from 74.2 ± 28.4 μg/m3 in 2007 to 14.6 ± 4.8 μg/m3 in 2019. The predicted spatial distributions for each year show that the SO2 pollution was severe (more than 20 μg/m3) in most areas of Northern China until 2017. By using model interpretation methods, we visually reveal the mechanism of estimation bias in the back-extrapolation. Specifically, the training data is severely imbalanced with respect to the satellite-retrieved SO2 column densities (i.e., it is short on high-value samples), so the benchmark model is unable to extrapolate the effects of this important predictor. This study provides long-term surface SO2 data for post hoc evaluation and human exposure assessment in Northern China, while demonstrating that the interpretable machine learning approach is critical for model diagnostics and refinement. Leveraging satellite retrievals, the RBE-DA approach can be applied worldwide to back-extrapolate various measures of air quality.
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Affiliation(s)
- Shifu Zhang
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan 610065, China
| | - Tan Mi
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan 610065, China
| | - Qinhuizi Wu
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan 610065, China
| | - Yuzhou Luo
- Department of Land, Air, and Water Resources, University of California, Davis, CA 95616, United States
| | - Michael L Grieneisen
- Department of Land, Air, and Water Resources, University of California, Davis, CA 95616, United States
| | - Guangming Shi
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan 610065, China; National Engineering Research Center for Flue Gas Desulfurization, Chengdu, Sichuan 610065, China
| | - Fumo Yang
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan 610065, China; National Engineering Research Center for Flue Gas Desulfurization, Chengdu, Sichuan 610065, China
| | - Yu Zhan
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan 610065, China; National Engineering Research Center for Flue Gas Desulfurization, Chengdu, Sichuan 610065, China.
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Nafsin N, Li J. Prediction of 5-day biochemical oxygen demand in the Buriganga River of Bangladesh using novel hybrid machine learning algorithms. WATER ENVIRONMENT RESEARCH : A RESEARCH PUBLICATION OF THE WATER ENVIRONMENT FEDERATION 2022; 94:e10718. [PMID: 35502725 DOI: 10.1002/wer.10718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 04/05/2022] [Accepted: 04/08/2022] [Indexed: 06/14/2023]
Abstract
Biochemical oxygen demand (BOD) is one of the most important variables indicating stream pollution with a severe condition of organic loading and maintaining aquatic life in ecosystems. Advanced monitoring techniques such as machine learning (ML) methods have been developed for an accurate, reliable, and cost-effective prediction of BOD. This study investigated the effectiveness of four stand-alone ML algorithms, namely, artificial neural network (ANN), support vector machine (SVM), random forest (RF), and gradient boosting machine (GBM), and six novel hybrid algorithms, namely, RF-SVM, ANN-SVM, GBM-SVM, RF-ANN, GBM-ANN, and RF-GBM, in predicting BOD of the Buriganga river system of Bangladesh. The feature importance analysis of RF algorithm indicated that chemical oxygen demand (COD), total dissolved solids (TDS), conductivity, total solids (TS), suspended solids (SS), and turbidity are the most influential parameters for predicting BOD5 . The significance of this study is the application of the novel hybrid models that resulted in higher prediction success; RF-SVM with the highest R2 value (0.908). The employed novel hybrid ML models can be particularly useful for efficient and systematic data management, water pollution control, and prevention in developing countries such as Bangladesh. PRACTITIONER POINTS: Investigated the efficiency of four stand-alone and six novel hybrid ML models for predicting BOD in a river of Bangladesh. The significance of this study is the application of the six novel hybrid models that resulted in higher prediction success. The best three prediction models were RF-SVM, ANN-SVM, and GBM-SVM with a prediction success of 91%, 89.6%, and 88.8% respectively. ML models indicated COD, conductivity, TDS, TS, SS, and turbidity as the most influential variables for predicting BOD. The novel hybrid models can be useful for developing countries for efficient systematic data management, pollution control, and prevention strategies.
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Affiliation(s)
- Nabila Nafsin
- Department of Civil and Environmental Engineering, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA
| | - Jin Li
- Department of Civil and Environmental Engineering, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA
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14
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Huang R, Ma C, Ma J, Huangfu X, He Q. Machine learning in natural and engineered water systems. WATER RESEARCH 2021; 205:117666. [PMID: 34560616 DOI: 10.1016/j.watres.2021.117666] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 09/01/2021] [Accepted: 09/11/2021] [Indexed: 06/13/2023]
Abstract
Water resources of desired quality and quantity are the foundation for human survival and sustainable development. To better protect the water environment and conserve water resources, efficient water management, purification, and transportation are of critical importance. In recent years, machine learning (ML) has exhibited its practicability, reliability, and high efficiency in numerous applications; furthermore, it has solved conventional and emerging problems in both natural and engineered water systems. For example, ML can predict various water quality indicators in situ and real-time by considering the complex interactions among water-related variables. ML approaches can also solve emerging pollution problems with proven rules or universal mechanisms summarized from the related research. Moreover, by applying image recognition technology to analyze the relationships between image information and physicochemical properties of the research object, ML can effectively identify and characterize specific contaminants. In view of the bright prospects of ML, this review comprehensively summarizes the development of ML applications in natural and engineered water systems. First, the concept and modeling steps of ML are briefly introduced, including data preparation, algorithm selection and model evaluation. In addition, comprehensive applications of ML in recent studies, including predicting water quality, mapping groundwater contaminants, classifying water resources, tracing contaminant sources, and evaluating pollutant toxicity in natural water systems, as well as modeling treatment techniques, assisting characterization analysis, purifying and distributing drinking water, and collecting and treating sewage water in engineered water systems, are summarized. Finally, the advantages and disadvantages of commonly used algorithms are analyzed according to their structures and mechanisms, and recommendations on the selection of ML algorithms for different studies, as well as prospects on the application and development of ML in water science are proposed. This review provides references for solving a wider range of water-related problems and brings further insights into the intelligent development of water science.
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Affiliation(s)
- Ruixing Huang
- Key Laboratory of Eco-environments in the Three Gorges Reservoir Region, Ministry of Education, College of Environmental and Ecology, Chongqing University, Chongqing 400044, China; State Key Laboratory of Urban Water Resource and Environment, School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin 150090, China
| | - Chengxue Ma
- Key Laboratory of Eco-environments in the Three Gorges Reservoir Region, Ministry of Education, College of Environmental and Ecology, Chongqing University, Chongqing 400044, China; State Key Laboratory of Urban Water Resource and Environment, School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin 150090, China
| | - Jun Ma
- State Key Laboratory of Urban Water Resource and Environment, School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin 150090, China
| | - Xiaoliu Huangfu
- Key Laboratory of Eco-environments in the Three Gorges Reservoir Region, Ministry of Education, College of Environmental and Ecology, Chongqing University, Chongqing 400044, China.
| | - Qiang He
- Key Laboratory of Eco-environments in the Three Gorges Reservoir Region, Ministry of Education, College of Environmental and Ecology, Chongqing University, Chongqing 400044, China
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