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Integrating machine learning algorithm with sewer process model to realize swift prediction and real-time control of H2S pollution in sewer systems

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WATER RESEARCH X 2024年 23卷 100230页

作者： Liang, Zhensheng Xie, Wenlang Li, Hao Li, Yu Jiang, Feng Sun Yat Sen Univ Sch Environm Sci & Engn Guangdong Prov Key Lab Environm Pollut Control & R Guangzhou 510275 Peoples R China Sun Yat Sen Univ Guangdong Prov Int Joint Res Ctr Urban Water Manag Guangzhou 510275 Peoples R China South China Normal Univ Sch Environm Guangdong Prov Key Lab Chem Pollut & Environm Safe Guangzhou 510006 Peoples R China South China Normal Univ MOE Key Lab Theoret Chem Environm Guangzhou 510006 Peoples R China

The frequent occurrence of safety incidents in sewer systems due to the emergency toxicity of hydrogen sulfide (H2S) necessitate timely and efficient prediction, early warning and real-time control. However, various factors influencing H2S generation and emission leads to a substantial computational burden for the existing dynamic sewer process models and fails to timely control the H2S exposure risk. The present study proposed a swift prediction model (SPM) that combined the validated dynamic sewer process model (the biofilm-initiated sewer process model, BISM) with a high-speed machine learning algorithm (MLA), achieving accurately and swiftly predict the dissolved sulfide (DS) concentration and H2S concentration in a specific sewer network. Based on Gradient Boosting Decision Tree -based SPM, the simulated concentrations of DS and H2S are 1.95 mg S/L and 214 ppm, respectively, which are closely to the field -measured values of 1.82 mg S/L and 219 ppm. Notably, SPM achieved a computation time of less than 0.3 s, and a significant improvement over BISM ( > 5000 s) for the same task. Moreover, the real-time and dynamic dosing scheme facilitated by SPM outperformed the conventional constant dosing scheme provided by dynamic sewer process model, which significantly improved the H2S control completion rate from 69 % to 100 %, and achieved a significant reduction in chemical dosage. In conclusion, the integration of dynamic sewer process models with MLA addresses the inadequacy of monitoring data for MLA training, and thus pursues swift prediction of H2S generation and emission, and achieving real-time, effective, and economic control of H2S in complex sewer networks.

关键词： Sulfide pollution in sewers Validated dynamic sewer process model machine learning algorithm Swift prediction model Real-time control

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Detection of Unfocused EEG Epochs by the Application of machine learning algorithm

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SENSORS 2024年第15期24卷 4829页

作者： Akhter, Rafia Beyette, Fred R. Univ Georgia Coll Engn Dept ECE Athens GA 30602 USA

Electroencephalography (EEG) is a non-invasive method used to track human brain activity over time. The time-locked EEG to an external event is known as event-related potential (ERP). ERP can be a biomarker of human perception and other cognitive processes. The success of ERP research depends on the laboratory conditions and attentiveness of the test subjects. Specifically, the inability to control experimental variables has reduced ERP research in the real world. This study collected EEG data under various experimental circumstances within an auditory oddball paradigm experiment to enable the use of ERP as an active biomarker in normal laboratory conditions. Then, ERP epochs were analyzed to identify unfocused epochs, affected by typical artifacts and external distortion. For the initial comparison, the ability of four unsupervised machine learning algorithms (MLAs) was evaluated to identify unfocused epochs. Then, their accuracy was compared with the human inspection and a current EEG analysis tool (EEGLab). All four MLAs were typically 95-100% accurate. In summary, our analysis finds that humans might miss subtle differences in the regular ERP patterns, but MLAs could efficiently identify those. Thus, our analysis suggests that unsupervised MLAs perform better for detecting unfocused ERP epochs compared with the other two standard methods.

关键词： electroencephalography distraction artifacts machine learning algorithm oddball paradigm human visual inspection EEGLab

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Research on wind turbine icing prediction data processing and accuracy of machine learning algorithm

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RENEWABLE ENERGY 2024年 237卷

作者： Zhang, Lidong Zhao, Yuze Guo, Yunfeng Hu, Tianyu Xu, Xiandong Zhang, Duanmei Song, Changpeng Guo, Yuanjun Ma, Yuanchi Northeast Elect Power Univ Sch Energy & Power Engn Jilin 132012 Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Northeast Elect Power Univ Sch Elect Engn Jilin 132012 Peoples R China Univ Manchester Sch Engn Manchester M13 9PL England Key Lab Smart Energy & Informat Technol Tianjin Mu Tianjin 300072 Peoples R China Changchun Inst Technol Coll Jilin Emergency Management Changchun 130021 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Guangdong Peoples R China

Studying the icing problem of wind turbine blades is crucial for optimizing wind farm operation and maintenance. Traditional machine learning algorithms like Random Forest and Support Vector machines have limitations in handling complex time series data and capturing long-term dependencies, leading to insufficient accuracy and generalization. To address these gaps, this paper proposes a comprehensive approach involving advanced machine learning frameworks and feature engineering techniques. This paper based on the original SACDA dataset, four distinct types of processing are performed on the prediction data via PCA dimensionality reduction and the introduction of new features;meanwhile, the four types mentioned above of datasets are trained and predicted using the Gated Recycling Unit model (GRU), Random Forest (RF), GA-BP Neural Network (BP), and Extreme learning machine (ELM) models. The results indicate that the prediction accuracies of all four types of models are more satisfactory, with the RF model having the highest prediction accuracy and the overall accuracy remaining above 99 percent, the dataset + RF model after dimensionality reduction of the original sensitive features having the highest prediction accuracy and speed.

关键词： Wind turbine Icing prediction PCA dimension reduction machine learning algorithm

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Reliability Analysis and Optimization of Computer Communication Network Based on machine learning algorithm

Reliability Analysis and Optimization of Computer Communicat...

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International Conference on Frontiers of Artificial Intelligence and machine learning (FAIML)

作者： Liu, Dai-xiong Wuhan Polytech Coll Comp Technol & Software Engn Wuhan 430074 Hubei Peoples R China

ISBN: (数字)9781665473644

ISBN: (纸本)9781665473644

machine learning is to find laws from observed data and use these laws to predict future data or unobservable data. Network measurement and routing optimization strategy are critical components in the routing optimization problem. Due to the continuous progress of information technology, computer information technology is widely used in various fields, so its security and reliability will be paid more and more attention. The unsupervised learning classification is carried out through the fast density clustering algorithm to classify the importance of nodes, which can be effectively applied to the important evaluation of communication network nodes and support the planning of the communication network. Given the progress of communication technology, optical fiber technology and computer internet technology, the network's functions have been strengthened daily, and the research on the reliability of computer communication networks has been promoted to develop in depth. Furthermore, optimization theory can realize the bandwidth allocation of a communication network. The important is that computer communication network reliability based on machine learning algorithm has great economic value, social value and social benefit.

关键词： machine learning algorithm Computer communication network Reliability Optimization

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Prediction of FBC Boiler Efficiency using machine learning algorithm 3

Prediction of FBC Boiler Efficiency using Machine Learning A...

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3rd IEEE International Power and Renewable Energy Conference (IPRECON)

作者： Pawar, Raunak Khandekar, M. A. Agashe, S. D. Makwana, Pratap COEP Technol Univ Dept Instrumentat & Control Engn Pune India Forbes Marshall Pune India

ISBN: (纸本)9781665491754

It is now clear that a fluidized bed combustion boiler is a feasible choice with many benefits over a traditional firing system. These boilers burn coal, washery waste, rice husk, bagasse, and other agricultural wastes as fuels. Calculations of boiler efficiency using the conventional method is time-consuming and very expensive. The work presented in this paper deals with establishing the statistical model for boiler efficiency using boiler parameters. Different machine learning algorithm like, Multilinear regression, support vector regression, K-nearest neighbors and decision tree regression have been used to build the boiler efficiency model. K-nearest neighbors algorithm shows the best model accuracy. So we can predict Fluidized bed combustion boiler efficiency using real time boiler parameters.

关键词： FBC Boiler machine learning algorithm K-nearest Neighbor Efficiency prediction

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Smart Tourism Route Planning System Based on machine learning algorithm

Smart Tourism Route Planning System Based on Machine Learnin...

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International Conference on Computers and Artificial Intelligence Technologies (CAIT)

作者： Yu, Zhenzhen Wang, Shan Shandong Inst Commerce & Technol Jinan 250103 Shandong Peoples R China

ISBN: (纸本)9798350396171

With the development of economy and the improvement of life quality, people have a deeper pursuit. Therefore, tourism has developed into one of the pillar industries of China's economy. Tourism has gradually become an important part of people's life and entertainment, but people's tourism consumption behavior has undergone significant changes, and they have a higher standard for tourism quality, which is the background of the birth of the concept of smart tourism. This paper analyzes the current situation and existing problems of tourism market route planning, and constructs an intelligent tourism route planning system based on machine learning algorithm to meet the diversified needs of tourists and promote the high-quality development of tourism Through the analysis of the tourism situation of a province from 2016 to 2020, the response time of the system was tested after the intelligent tourism route planning system was put into use. It was found that the response time of the system would increase 0.0005 seconds for every 100000 additional passengers. The increase of people flow will lead to system interface congestion, but the time is very short and can be basically ignored. The research shows that the design of the system can still bring convenience to passengers.

关键词： machine learning algorithm smart tourism route planning system tourism industry

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Sentimental analysis over twitter data using clustering based machine learning algorithm

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年 1-12页

作者： Jacob, Sharon Susan Vijayakumar, R. Mahatma Gandhi Univ Sch Comp Sci Kottayam Kerala India

Using Internet technologies, people often share their thoughts, feedback, news and information with others. Owing to social media sites, speed and ease of contact improved. User posts their views on public sentiment web sites like Facebook, Instagram, Twitter, blogs, WhatsApp, Snapchat, LinkedIn, etc. Thousands of posts, millions of tweets and thousands of letters are posted each day. Twitter is one of them that is now being widely popular in these social media sites. It offers an easy and quick way to evaluate the opinions of consumers on a product or service. The approach to be used to mark customer's impressions or opinions of a product is to establish an sentimental analysis system. Twitter is a microblogging platform where users can submit feedback to a community of followers in the form of ratings or tweets in bigdata. A tweet may be defined as positive, negative or neutral depending on the viewpoint shared. Here we investigate the sentiment of Twitter messages in this paper using the clustering approach based on machine learning (ML) algorithm. Our tests are carried out in a qualified and test collection composed of large data from tweets from one lakh of results, showing our work to determine if a tweet is positive or negative.

关键词： Twitter Sentiment analysis Clustering Big data machine learning algorithm

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machine learning algorithm for activity-aware demand response considering energy savings and comfort requirements

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IET SMART GRID 2020年第5期3卷 730-737页

作者： Zhang, Yue Srivastava, Anurag. K. Cook, Diane Washington State Univ Sch Elect Engn & & Comp Sci Pullman WA 99164 USA

Due to the high cost of peak hour power generation and a push towards sustainability, the need for demand response (DR) is increasing. Compared to commercial-level DR, residential-level DR is more challenging. Residents are reluctant to participate, and DR controllers lack sufficient real-time activity information to balance energy savings with residents' need for comfort and convenience. To address the above challenges, we propose a sensor data-driven activity-based controller for heating, ventilation, and air conditioning devices. Using our proposed novel strategy, resident activities are recognized in real-time through a random forest machine learning approach. Integrating activity information and forecasted electricity pricing, the proposed controller can simultaneously reduce energy consumption for sustainability and maintain resident constraints for comfort based on recognized activities. Results demonstrate the superiority of the proposed approach.

关键词： building management systems power engineering computing energy conservation demand side management learning (artificial intelligence) power consumption HVAC power generation control machine learning algorithm activity-aware demand response energy savings comfort requirements peak hour power generation commercial-level DR residential-level DR DR controllers real-time activity information sensor data-driven activity-based controller air conditioning devices random forest machine energy consumption resident constraints heating-ventilation-air conditioning devices

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DDoS Attack Detection Model using machine learning algorithm in Next Generation Firewall

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Procedia Computer Science 2024年 233卷 175-183页

作者： Manthan Patel P.P. Amritha Vinay B Sudheer M. Sethumadhavan TIFAC - CORE in Cyber Security Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

Next generation firewall is taking major part to secure network environment in the industry. This device will monitor all the traffic which is coming inside the network or going outside of the network. With all these security devices attackers can still perform various kind of attacks on the network. DDoS attack is one of the hardest attack to identify which will send packets to the network and which will look like normal traffic but it will act as a DDoS traffic. In this paper we used binary decision tree, XGBoost and support vector machine to identify DDoS attack traffic pattern from the different features in the packet header. Data will be fetched from the packet header and among them standard deviation of the packet bytes and packet flows are the features considered. We have applied this data on the trained dataset. algorithm will predict whether the traffic coming inside the network is trusted or not. Out of the three algorithms Binary Decision tree algorithm is giving 99 percentage of accuracy and will predict the data as fast as possible. Here priority is to filter DDos attacks of any security level in the line speed of the NIDS or any other appliances. This method of DDoS attack detection will add extra layer of security in the next generation firewall which will make firewall more robust.

关键词： Next Generation Firewall Distributed Denial-of-Service Attack machine learning algorithm

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Flood susceptibility assessment of the Agartala Urban Watershed, India, using machine learning algorithm

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2024年第2期196卷 110-110页

作者： Debnath, Jatan Debbarma, Jimmi Debnath, Amal Meraj, Gowhar Chand, Kesar Singh, Suraj Kumar Kanga, Shruti Kumar, Pankaj Sahariah, Dhrubajyoti Saikia, Anup Gauhati Univ Dept Geog Gauhati 781014 Assam India Tripura Univ Dept Geog & Disaster Management Agartala Tripura India Tripura Univ Dept Forestry & Biodivers Agartala Tripura India Univ Tokyo Dept Ecosyst Studies Bunkyo City Tokyo Japan GB Pant Natl Inst Himalayan Environm NIHE Ctr Environm Assessment & Climate Change Himachal Reg Ctr Himachal Pradesh Kulu India Suresh Gyan Vihar Univ Ctr Sustainable Dev Jaipur Rajasthan India Cent Univ Punjab Dept Geog Bathinda India Inst Global Environm Strategies Hayama Japan

Frequent floods are a severe threat to the well-being of people the world over. This is particularly severe in developing countries like India where tropical monsoon climate prevails. Recently, flood hazard susceptibility mapping has become a popular tool to mitigate the effects of this threat. Therefore, the present study utilized four distinctive machine learning algorithms i.e., K-Nearest Neighbor, Decision Tree, Naive Bayes, and Random Forest to estimate flood susceptibility zones in the Agartala Urban Watershed of Tripura, India. The latter experiences debilitating floods during the monsoon season. A multicollinearity test was conducted to examine the collinearity of the chosen flood conditioning factors, and it was seen that none of the factors were compromised by multicollinearity. Results showed that around three-fourths of the AUW area was classified as moderate to very high flood-prone zones, while over 20 percent was between low and very low flood-prone zones. The models applied performed well with ROC-AUC scores greater than 70 percent and MAE, MSE, and RMSE scores less than 30 percent. DT and RF algorithms were suggested for places with similar physical characteristics based on their outstanding performance and the training datasets. The study provides valuable insights to policymakers, administrative authorities, and local stakeholders to cope with floods and enhance flood prevention measures as a climate change adaptation strategy in the AUW.

关键词： Agartala urban watershed Decision tree Flood susceptibility machine learning algorithm Random forest

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