![hypothesis space in machine learning python Youtube](https://static.javatpoint.com/images/youtube-32.png)
- Send your Feedback to [email protected]
Help Others, Please Share
![hypothesis space in machine learning python facebook](https://www.javatpoint.com/images/facebook32.png)
Learn Latest Tutorials
![hypothesis space in machine learning python Splunk tutorial](https://static.javatpoint.com/images/homeicon/splunk.png)
Transact-SQL
![hypothesis space in machine learning python Tumblr tutorial](https://static.javatpoint.com/images/homeicon/tumblr.png)
Reinforcement Learning
![hypothesis space in machine learning python R Programming tutorial](https://static.javatpoint.com/images/homeicon/r-programming.png)
R Programming
![hypothesis space in machine learning python RxJS tutorial](https://static.javatpoint.com/images/homeicon/rxjs.png)
React Native
![hypothesis space in machine learning python Python Design Patterns](https://static.javatpoint.com/images/homeicon/python-design-patterns.png)
Python Design Patterns
![hypothesis space in machine learning python Python Pillow tutorial](https://static.javatpoint.com/images/homeicon/python-pillow.png)
Python Pillow
![hypothesis space in machine learning python Python Turtle tutorial](https://static.javatpoint.com/images/homeicon/python-turtle.png)
Python Turtle
![hypothesis space in machine learning python Keras tutorial](https://static.javatpoint.com/images/homeicon/keras.png)
Preparation
![hypothesis space in machine learning python Aptitude](https://static.javatpoint.com/aptitude/images/quantitative-aptitude-home.png)
Verbal Ability
![hypothesis space in machine learning python Interview Questions](https://static.javatpoint.com/images/logo/interviewhome.png)
Company Questions
Trending Technologies
![hypothesis space in machine learning python Artificial Intelligence](https://static.javatpoint.com/images/homeicon/artificial-intelligence.png)
Cloud Computing
![hypothesis space in machine learning python Hadoop tutorial](https://static.javatpoint.com/images/homeicon/hadoop.png)
Data Science
![hypothesis space in machine learning python Angular 7 Tutorial](https://static.javatpoint.com/images/homeicon/angular7.png)
B.Tech / MCA
![hypothesis space in machine learning python DBMS tutorial](https://static.javatpoint.com/images/homeicon/dbms.png)
Data Structures
![hypothesis space in machine learning python DAA tutorial](https://static.javatpoint.com/images/homeicon/daa.png)
Operating System
![hypothesis space in machine learning python Computer Network tutorial](https://static.javatpoint.com/images/homeicon/computer-network.png)
Computer Network
![hypothesis space in machine learning python Compiler Design tutorial](https://static.javatpoint.com/images/homeicon/compiler-design.png)
Compiler Design
![hypothesis space in machine learning python Computer Organization and Architecture](https://static.javatpoint.com/images/homeicon/computer-organization.png)
Computer Organization
![hypothesis space in machine learning python Discrete Mathematics Tutorial](https://static.javatpoint.com/images/homeicon/discrete-mathematics.png)
Discrete Mathematics
![hypothesis space in machine learning python Ethical Hacking](https://static.javatpoint.com/images/homeicon/ethical-hacking.png)
Ethical Hacking
![hypothesis space in machine learning python Computer Graphics Tutorial](https://static.javatpoint.com/images/homeicon/computer-graphics.png)
Computer Graphics
![hypothesis space in machine learning python Software Engineering](https://static.javatpoint.com/images/homeicon/software-engineering.png)
Software Engineering
![hypothesis space in machine learning python html tutorial](https://static.javatpoint.com/images/homeicon/web-technology.png)
Web Technology
![hypothesis space in machine learning python Cyber Security tutorial](https://static.javatpoint.com/images/homeicon/cyber-security.png)
Cyber Security
![hypothesis space in machine learning python Automata Tutorial](https://static.javatpoint.com/images/homeicon/automata.png)
C Programming
![hypothesis space in machine learning python C++ tutorial](https://static.javatpoint.com/images/homeicon/cpp.png)
Data Mining
![hypothesis space in machine learning python Data Warehouse Tutorial](https://static.javatpoint.com/images/homeicon/data-warehouse.png)
Data Warehouse
![hypothesis space in machine learning python RSS Feed](https://static.javatpoint.com/images/social/rss1.png)
Information
Initiatives
You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .
Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.
Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.
Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.
Original Submission Date Received: .
- Active Journals
- Find a Journal
- Proceedings Series
- For Authors
- For Reviewers
- For Editors
- For Librarians
- For Publishers
- For Societies
- For Conference Organizers
- Open Access Policy
- Institutional Open Access Program
- Special Issues Guidelines
- Editorial Process
- Research and Publication Ethics
- Article Processing Charges
- Testimonials
- Preprints.org
- SciProfiles
- Encyclopedia
![Sustainability sustainability-logo](https://pub.mdpi-res.com/img/journals/sustainability-logo.png?8600e93ff98dbf14)
Article Menu
![hypothesis space in machine learning python hypothesis space in machine learning python](https://www.mdpi.com/bundles/mdpisciprofileslink/img/unknown-user.png)
- Subscribe SciFeed
- Recommended Articles
- Google Scholar
- on Google Scholar
- Table of Contents
Find support for a specific problem in the support section of our website.
Please let us know what you think of our products and services.
Visit our dedicated information section to learn more about MDPI.
JSmol Viewer
Assessing the impact of straw burning on pm 2.5 using explainable machine learning: a case study in heilongjiang province, china.
![ORCID hypothesis space in machine learning python](https://pub.mdpi-res.com/img/design/orcid.png?0465bc3812adeb52?1724334283)
1. Introduction
2. materials and methods, 2.1. fengyun-3 series global active fire products, 2.2. land cover, 2.3. auxiliary data, 2.3.1. climate-related variables, 2.3.2. dem and aod data, 2.3.3. chinahighpm 2.5, 2.4. feature selection, 2.5. random forest model, 2.6. interpretable analysis, 2.7. data preparation for temporal and spatial models, 3. results and discussion, 3.1. comparison with modis fire points, 3.2. spatial distribution of fire points, 3.3. temporal patterns and variations, 3.4. monthly variations in crop fire points, 3.5. correlation and collinearity analyses of input features, 3.6. accuracy of the temporal and spatial models, 3.7. impacts of straw burning and other influencing factors on pm 2.5, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.
- Shi, Y.; Gong, S.; Zang, S.; Zhao, Y.; Wang, W.; Lv, Z.; Matsunaga, T.; Yamaguchi, Y.; Bai, Y. High-resolution and multi-year estimation of emissions from open biomass burning in Northeast China during 2001–2017. J. Clean. Prod. 2021 , 310 , 127496. [ Google Scholar ] [ CrossRef ]
- Yin, S.; Guo, M.; Wang, X.; Yamamoto, H.; Ou, W. Spatiotemporal variation and distribution characteristics of crop residue burning in China from 2001 to 2018. Environ. Pollut. 2021 , 268 , 115849. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Huang, L.; Zhu, Y.; Wang, Q.; Zhu, A.; Liu, Z.; Wang, Y.; Allen, D.T.; Li, L. Assessment of the effects of straw burning bans in China: Emissions, air quality, and health impacts. Sci. Total Environ. 2021 , 789 , 147935. [ Google Scholar ] [ CrossRef ]
- Cheng, Z.; Wang, S.; Fu, X.; Watson, J.G.; Jiang, J.; Fu, Q.; Chen, C.; Xu, B.; Yu, J.; Chow, J.C.; et al. Impact of biomass burning on haze pollution in the Yangtze River delta, China: A case study in summer 2011. Atmos. Chem. Phys. 2014 , 14 , 4573–4585. [ Google Scholar ] [ CrossRef ]
- Singh, J. Paddy and wheat stubble blazing in Haryana and Punjab states of India: A menace for environmental health. Environ. Qual. Manag. 2018 , 28 , 47–53. [ Google Scholar ] [ CrossRef ]
- Wu, X.; Chen, W.; Zhang, S.; Li, R.; Zhang, M.; Liu, J.; Jiang, Y.; Liu, Y. Temporal variation and chemical components of rural ambient PM2.5 during main agricultural activity periods in the black soil region of Northeast China. Atmosphere 2019 , 10 , 510. [ Google Scholar ] [ CrossRef ]
- Zha, S.; Zhang, S.; Cheng, T.; Chen, J.; Huang, G.; Li, X.; Wang, Q. Agricultural fires and their potential impacts on regional air quality over China. Aerosol Air Qual. Res. 2013 , 13 , 992–1001. [ Google Scholar ] [ CrossRef ]
- Qiu, X.; Duan, L.; Chai, F.; Wang, S.; Yu, Q.; Wang, S. Deriving high-resolution emission inventory of open biomass burning in China based on satellite observations. Environ. Sci. Technol. 2016 , 50 , 11779–11786. [ Google Scholar ] [ CrossRef ]
- Wu, J.; Kong, S.; Wu, F.; Cheng, Y.; Zheng, S.; Qin, S.; Liu, X.; Yan, Q.; Zheng, H.; Zheng, M.; et al. The moving of high emission for biomass burning in China: View from multi-year emission estimation and human-driven forces. Environ. Int. 2020 , 142 , 105812. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Zhou, Y.; Xing, X.; Lang, J.; Chen, D.; Cheng, S.; Wei, L.; Wei, X.; Liu, C. A comprehensive biomass burning emission inventory with high spatial and temporal resolution in China. Atmos. Chem. Phys. 2017 , 17 , 2839–2864. [ Google Scholar ] [ CrossRef ]
- Wu, J.; Kong, S.; Wu, F.; Cheng, Y.; Zheng, S.; Yan, Q.; Zheng, H.; Yang, G.; Zheng, M.; Liu, D.; et al. Estimating the open biomass burning emissions in central and eastern China from 2003 to 2015 based on satellite observation. Atmos. Chem. Phys. 2018 , 18 , 11623–11646. [ Google Scholar ] [ CrossRef ]
- Chen, J.; Yao, Q.; Chen, Z.; Li, M.; Hao, Z.; Liu, C.; Zheng, W.; Xu, M.; Chen, X.; Yang, J.; et al. The Fengyun-3D (FY-3D) global active fire product: Principle, methodology and validation. Earth Syst. Sci. Data 2022 , 14 , 3489–3508. [ Google Scholar ] [ CrossRef ]
- Lin, Z.; Chen, F.; Niu, Z.; Li, B.; Yu, B.; Jia, H.; Zhang, M. An active fire detection algorithm based on multi-temporal FengYun-3C VIRR data. Remote Sens. Environ. 2018 , 211 , 376–387. [ Google Scholar ] [ CrossRef ]
- Zhang, L.; Liu, Y.; Hao, L. Contributions of open crop straw burning emissions to PM 2.5 concentrations in China. Environ. Res. Lett. 2016 , 11 , 014014. [ Google Scholar ] [ CrossRef ]
- He, G.; Liu, T.; Zhou, M. Straw burning, PM 2.5 , and death: Evidence from China. J. Dev. Econ. 2020 , 145 , 102468. [ Google Scholar ] [ CrossRef ]
- Mehmood, K.; Bao, Y.; Saifullah; Bibi, S.; Dahlawi, S.; Yaseen, M.; Abrar, M.M.; Srivastava, P.; Fahad, S.; Faraj, T.K. Contributions of open biomass burning and crop straw burning to air quality: Current research paradigm and future outlooks. Front. Environ. Sci. 2022 , 10 , 852492. [ Google Scholar ] [ CrossRef ]
- Chen, W.; Li, J.; Bao, Q.; Gao, Z.; Cheng, T.; Yu, Y. Evaluation of straw open burning prohibition effect on provincial air quality during October and November 2018 in Jilin Province. Atmosphere 2019 , 10 , 375. [ Google Scholar ] [ CrossRef ]
- Zhang, H.; Hu, J.; Qi, Y.; Li, C.; Chen, J.; Wang, X.; He, J.; Wang, S.; Hao, J.; Zhang, L.; et al. Emission characterization, environmental impact, and control measure of PM 2.5 emitted from agricultural crop residue burning in China. J. Clean. Prod. 2017 , 149 , 629–635. [ Google Scholar ] [ CrossRef ]
- Mehmood, K.; Wu, Y.; Wang, L.; Yu, S.; Li, P.; Chen, X.; Li, Z.; Zhang, Y.; Li, M.; Liu, W.; et al. Relative effects of open biomass burning and open crop straw burning on haze formation over central and eastern China: Modeling study driven by constrained emissions. Atmos. Chem. Phys. 2020 , 20 , 2419–2443. [ Google Scholar ] [ CrossRef ]
- Cui, S.; Song, Z.; Zhang, L.; Shen, Z.; Hough, R.; Zhang, Z.; An, L.; Fu, Q.; Zhao, Y.; Jia, Z. Spatial and temporal variations of open straw burning based on fire spots in northeast China from 2013 to 2017. Atmos. Environ. 2021 , 244 , 117962. [ Google Scholar ] [ CrossRef ]
- Wang, Z.; Zhao, J.; Xu, J.; Jia, M.; Li, H.; Wang, S. Influence of straw burning on urban air pollutant concentrations in Northeast China. Int. J. Environ. Res. Public Health 2019 , 16 , 1379. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Grange, S.K.; Lee, J.D.; Drysdale, W.S.; Lewis, A.C.; Hueglin, C.; Emmenegger, L.; Carslaw, D.C. COVID-19 lockdowns highlight a risk of increasing ozone pollution in European urban areas. Atmos. Chem. Phys. 2021 , 21 , 4169–4185. [ Google Scholar ] [ CrossRef ]
- Zhang, Z.; Xu, B.; Xu, W.; Wang, F.; Gao, J.; Li, Y.; Li, M.; Feng, Y.; Shi, G. Machine learning combined with the PMF model reveal the synergistic effects of sources and meteorological factors on PM 2.5 pollution. Environ. Res. 2022 , 212 , 113322. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Cheng, Y.; Huang, X.-F.; Peng, Y.; Tang, M.-X.; Zhu, B.; Xia, S.-Y.; He, L.-Y. A novel machine learning method for evaluating the impact of emission sources on ozone formation. Environ. Pollut. 2023 , 316 , 120685. [ Google Scholar ] [ CrossRef ]
- Zhang, L.; Wang, L.; Ji, D.; Xia, Z.; Nan, P.; Zhang, J.; Li, K.; Qi, B.; Du, R.; Sun, Y.; et al. Explainable ensemble machine learning revealing the effect of meteorology and sources on ozone formation in megacity Hangzhou, China. Sci. Total Environ. 2024 , 922 , 171295. [ Google Scholar ] [ CrossRef ]
- García, M.V.; Aznarte, J.L. Shapley additive explanations for NO 2 forecasting. Ecol. Inform. 2020 , 56 , 101039. [ Google Scholar ] [ CrossRef ]
- Zheng, W.; Chen, J.; Yan, H.; Liu, C.; Tang, S.H. Global fire monitoring products of FY-3D/MERSI-II and their applications. J. Remote Sens. 2020 , 24 , 521–530. [ Google Scholar ] [ CrossRef ]
- Lin, Z.; Chen, F.; Li, B.; Yu, B.; Shirazi, Z.; Wu, Q.; Wu, W. FengYun-3C VIRR active fire monitoring: Algorithm description and initial assessment using MODIS and Landsat data. IEEE Trans. Geosci. Remote Sens. 2017 , 55 , 6420–6430. [ Google Scholar ] [ CrossRef ]
- Buchhorn, M.; Lesiv, M.; Tsendbazar, N.-E.; Herold, M.; Bertels, L.; Smets, B. Copernicus global land cover layers—Collection 2. Remote Sens. 2020 , 12 , 1044. [ Google Scholar ] [ CrossRef ]
- Muñoz Sabater, J. ERA5-Land monthly averaged data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). 2019. [ Google Scholar ]
- Terblanche, D.; Lynch, A.; Chen, Z.; Sinclair, S. ERA5-Derived Precipitation: Insights from Historical Rainfall Networks in Southern Africa. J. Appl. Meteorol. Climatol. 2022 , 61 , 1473–1484. [ Google Scholar ] [ CrossRef ]
- Farr, T.G.; Rosen, P.A.; Caro, E.; Crippen, R.; Duren, R.; Hensley, S.; Kobrick, M.; Paller, M.; Rodriguez, E.; Roth, L.; et al. The shuttle radar topography mission. Rev. Geophys. 2007 , 45 . [ Google Scholar ] [ CrossRef ]
- Bai, K.; Li, K.; Shao, L.; Li, X.; Liu, C.; Li, Z.; Ma, M.; Han, D.; Sun, Y.; Zheng, Z.; et al. LGHAP v2: A global gap-free aerosol optical depth and PM 2.5 concentration dataset since 2000 derived via big earth data analytics. Earth Syst. Sci. Data Discuss. 2024 , 2024 , 1–29. [ Google Scholar ] [ CrossRef ]
- Wei, J.; Li, Z.; Lyapustin, A.; Sun, L.; Peng, Y.; Xue, W.; Su, T.; Cribb, M. Reconstructing 1-km-resolution high-quality PM 2.5 data records from 2000 to 2018 in China: Spatiotemporal variations and policy implications. Remote Sens. Environ. 2021 , 252 , 112136. [ Google Scholar ] [ CrossRef ]
- Wei, J.; Li, Z.; Cribb, M.; Huang, W.; Xue, W.; Sun, L.; Guo, J.; Peng, Y.; Li, J.; Lyapustin, A.; et al. Improved 1 km resolution PM 2.5 estimates across China using enhanced space–time extremely randomized trees. Atmos. Chem. Phys. 2020 , 20 , 3273–3289. [ Google Scholar ] [ CrossRef ]
- Islam, A.R.M.T.; Al Awadh, M.; Mallick, J.; Pal, S.C.; Chakraborty, R.; Fattah, M.A.; Ghose, B.; Kakoli, M.K.A.; Islam, M.A.; Naqvi, H.R.; et al. Estimating ground-level PM 2.5 using subset regression model and machine learning algorithms in Asian megacity, Dhaka, Bangladesh. Air Qual. Atmos. Health 2023 , 16 , 1117–1139. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Chen, B.; Hu, J.; Wang, Y. Synergistic observation of FY-4A&4B to estimate CO concentration in China: Combining interpretable machine learning to reveal the influencing mechanisms of CO variations. npj Clim. Atmos. Sci. 2024 , 7 , 9. [ Google Scholar ]
- Xu, Z.; Wang, Z.; Zhang, X. Mapping the forest litterfall mercury deposition in China. Sci. Total Environ. 2022 , 839 , 156288. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Li, S.; Yuan, W.; Ciais, P.; Viovy, N.; Ito, A.; Jia, B.; Zhu, D. Benchmark estimates for aboveground litterfall data derived from ecosystem models. Environ. Res. Lett. 2019 , 14 , 084020. [ Google Scholar ] [ CrossRef ]
- Yang, C.; Wang, Y.; Zhang, A.; Fan, H.; Guo, L. A Random Forest Algorithm Combined with Bayesian Optimization for Atmospheric Duct Estimation. Remote Sens. 2023 , 15 , 4296. [ Google Scholar ] [ CrossRef ]
- Berdugo, M.; Gaitán, J.J.; Delgado-Baquerizo, M.; Crowther, T.W.; Dakos, V. Prevalence and drivers of abrupt vegetation shifts in global drylands. Proc. Natl. Acad. Sci. USA 2022 , 119 , e2123393119. [ Google Scholar ] [ CrossRef ]
- Qian, S.; Qiao, X.; Zhang, W.; Yu, Z.; Dong, S.; Feng, J. Machine learning-based prediction for settling velocity of microplastics with various shapes. Water Res. 2024 , 249 , 121001. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Zhou, Y.; Zhang, Y.; Zhao, B.; Lang, J.; Xia, X.; Chen, D.; Cheng, S. Estimating air pollutant emissions from crop residue open burning through a calculation of open burning proportion based on satellite-derived fire radiative energy. Environ. Pollut. 2021 , 286 , 117477. [ Google Scholar ] [ CrossRef ]
- Chen, D.; Pereira, J.M.; Masiero, A.; Pirotti, F. Mapping fire regimes in China using MODIS active fire and burned area data. Appl. Geogr. 2017 , 85 , 14–26. [ Google Scholar ] [ CrossRef ]
- Lundberg, S.M.; Erion, G.G.; Lee, S.-I. Consistent individualized feature attribution for tree ensembles. arXiv 2018 , arXiv:1802.03888. [ Google Scholar ]
- Prasad, A.M.; Iverson, L.R.; Liaw, A. Newer classification and regression tree techniques: Bagging and random forests for ecological prediction. Ecosystems 2006 , 9 , 181–199. [ Google Scholar ] [ CrossRef ]
- Cui, L.; Wang, S. Mapping the daily nitrous acid (HONO) concentrations across China during 2006–2017 through ensemble machine-learning algorithm. Sci. Total Environ. 2021 , 785 , 147325. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Gu, X.; Wu, Z.; Liu, X.; Qiao, R.; Jiang, Q. Exploring the Nonlinear Interplay between Urban Morphology and Nighttime Thermal Environment. Sustain. Cities Soc. 2024 , 101 , 105176. [ Google Scholar ] [ CrossRef ]
- Logothetis, S.A.; Salamalikis, V.; Gkikas, A.; Kazadzis, S.; Amiridis, V.; Kazantzidis, A. 15-year variability of desert dust optical depth on global and regional scales. Atmos. Chem. Phys. 2021 , 21 , 16499–16529. [ Google Scholar ] [ CrossRef ]
- Zhai, S.; Jacob, D.J.; Brewer, J.F.; Li, K.; Moch, J.M.; Kim, J.; Lee, S.; Lim, H.; Lee, H.C.; Kuk, S.K.; et al. Relating geostationary satellite measurements of aerosol optical depth (AOD) over East Asia to fine particulate matter (PM 2.5 ): Insights from the KORUS-AQ aircraft campaign and GEOS-Chem model simulations. Atmos. Chem. Phys. 2021 , 21 , 16775–16791. [ Google Scholar ] [ CrossRef ]
- Zhang, F. Factors Influencing the Spatio–Temporal Variability of Aerosol Optical Depth over the Arid Region of Northwest China. Atmosphere 2023 , 15 , 54. [ Google Scholar ] [ CrossRef ]
- Wang, Y.; Yang, L.; Xie, D.; Hu, Y.; Cao, D.; Huang, H.; Zhao, D. Investigation of Spatiotemporal Variation and Drivers of Aerosol Optical Depth in China from 2010 to 2020. Atmosphere 2023 , 14 , 477. [ Google Scholar ] [ CrossRef ]
- Wen, Y.; Zhang, S.; Wang, Y.; Yang, J.; He, L.; Wu, Y.; Hao, J. Dynamic Traffic Data in Machine-Learning Air Quality Mapping Improves Environmental Justice Assessment. Environ. Sci. Technol. 2024 , 58 , 3118–3128. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Zhao, N.; Liu, Y.; Cao, G.; Samson, E.L.; Zhang, J. Forecasting China’s GDP at the pixel level using nighttime lights time series and population images. GISci. Remote Sens. 2017 , 54 , 407–425. [ Google Scholar ] [ CrossRef ]
- Bondarenko, M. Individual Countries 1 km Population Density (2000–2020). 2020. [ Google Scholar ] [ CrossRef ]
Click here to enlarge figure
| The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
Share and Cite
Xu, Z.; Liu, B.; Wang, W.; Zhang, Z.; Qiu, W. Assessing the Impact of Straw Burning on PM 2.5 Using Explainable Machine Learning: A Case Study in Heilongjiang Province, China. Sustainability 2024 , 16 , 7315. https://doi.org/10.3390/su16177315
Xu Z, Liu B, Wang W, Zhang Z, Qiu W. Assessing the Impact of Straw Burning on PM 2.5 Using Explainable Machine Learning: A Case Study in Heilongjiang Province, China. Sustainability . 2024; 16(17):7315. https://doi.org/10.3390/su16177315
Xu, Zehua, Baiyin Liu, Wei Wang, Zhimiao Zhang, and Wenting Qiu. 2024. "Assessing the Impact of Straw Burning on PM 2.5 Using Explainable Machine Learning: A Case Study in Heilongjiang Province, China" Sustainability 16, no. 17: 7315. https://doi.org/10.3390/su16177315
Article Metrics
Article access statistics, further information, mdpi initiatives, follow mdpi.
![MDPI Open Access Journals MDPI](https://pub.mdpi-res.com/img/design/mdpi-pub-logo-white-small.png?71d18e5f805839ab?1724334283)
Subscribe to receive issue release notifications and newsletters from MDPI journals
- Data Science
- Data Analysis
- Data Visualization
- Machine Learning
- Deep Learning
- Computer Vision
- Artificial Intelligence
- AI ML DS Interview Series
- AI ML DS Projects series
- Data Engineering
- Web Scrapping
ML – Candidate Elimination Algorithm
The candidate elimination algorithm incrementally builds the version space given a hypothesis space H and a set E of examples. The examples are added one by one; each example possibly shrinks the version space by removing the hypotheses that are inconsistent with the example. The candidate elimination algorithm does this by updating the general and specific boundary for each new example.
- You can consider this as an extended form of the Find-S algorithm.
- Consider both positive and negative examples.
- Actually, positive examples are used here as the Find-S algorithm (Basically they are generalizing from the specification).
- While the negative example is specified in the generalizing form.
Terms Used:
- Concept learning: Concept learning is basically the learning task of the machine (Learn by Train data)
- General Hypothesis: Not Specifying features to learn the machine.
- G = {‘?’, ‘?’,’?’,’?’…}: Number of attributes
- Specific Hypothesis: Specifying features to learn machine (Specific feature)
- S= {‘pi’,’pi’,’pi’…}: The number of pi depends on a number of attributes.
- Version Space: It is an intermediate of general hypothesis and Specific hypothesis. It not only just writes one hypothesis but a set of all possible hypotheses based on training data-set.
Consider the dataset given below:
![hypothesis space in machine learning python hypothesis space in machine learning python](https://media.geeksforgeeks.org/wp-content/uploads/20200825164952/dataset.PNG)
Algorithmic steps:
The Candidate Elimination Algorithm (CEA) is an improvement over the Find-S algorithm for classification tasks. While CEA shares some similarities with Find-S, it also has some essential differences that offer advantages and disadvantages. Here are some advantages and disadvantages of CEA in comparison with Find-S:
Advantages of CEA over Find-S:
- Improved accuracy: CEA considers both positive and negative examples to generate the hypothesis, which can result in higher accuracy when dealing with noisy or incomplete data.
- Flexibility: CEA can handle more complex classification tasks, such as those with multiple classes or non-linear decision boundaries.
- More efficient: CEA reduces the number of hypotheses by generating a set of general hypotheses and then eliminating them one by one. This can result in faster processing and improved efficiency.
- Better handling of continuous attributes: CEA can handle continuous attributes by creating boundaries for each attribute, which makes it more suitable for a wider range of datasets.
Disadvantages of CEA in comparison with Find-S:
- More complex: CEA is a more complex algorithm than Find-S, which may make it more difficult for beginners or those without a strong background in machine learning to use and understand.
- Higher memory requirements: CEA requires more memory to store the set of hypotheses and boundaries, which may make it less suitable for memory-constrained environments.
- Slower processing for large datasets: CEA may become slower for larger datasets due to the increased number of hypotheses generated.
- Higher potential for overfitting: The increased complexity of CEA may make it more prone to overfitting on the training data, especially if the dataset is small or has a high degree of noise.
Please Login to comment...
Similar reads.
- How to Get a Free SSL Certificate
- Best SSL Certificates Provider in India
- Elon Musk's xAI releases Grok-2 AI assistant
- What is OpenAI SearchGPT? How it works and How to Get it?
- Content Improvement League 2024: From Good To A Great Article
Improve your Coding Skills with Practice
![hypothesis space in machine learning python alt=](https://www.geeksforgeeks.org/)
What kind of Experience do you want to share?
![](//nandemo.space/777/templates/cheerup1/res/banner1.gif)
IMAGES
COMMENTS
A hypothesis is a function that best describes the target in supervised machine learning. The hypothesis that an algorithm would come up depends upon the data and also depends upon the restrictions and bias that we have imposed on the data. The Hypothesis can be calculated as: y = mx + b y =mx+b. Where, y = range. m = slope of the lines.
To get a better idea: The input space is in the above given example 24 2 4, its the number of possible inputs. The hypothesis space is 224 = 65536 2 2 4 = 65536 because for each set of features of the input space two outcomes ( 0 and 1) are possible. The ML algorithm helps us to find one function, sometimes also referred as hypothesis, from the ...
Supervised machine learning is often described as the problem of approximating a target function that maps inputs to outputs. This description is characterized as searching through and evaluating candidate hypothesis from hypothesis spaces. The discussion of hypotheses in machine learning can be confusing for a beginner, especially when "hypothesis" has a distinct, but related meaning […]
It tests the null hypothesis that the population variances are equal (called homogeneity of variance or homoscedasticity). Suppose the resulting p-value of Levene's test is less than the significance level (typically 0.05).In that case, the obtained differences in sample variances are unlikely to have occurred based on random sampling from a population with equal variances.
Our goal is to find a model that classifies objects as positive or negative. Applying Logistic Regression, we can get the models of the form: (1) which estimate the probability that the object at hand is positive. Each such model is called a hypothesis, while the set of all the hypotheses an algorithm can learn is known as its hypothesis space ...
Hypothesis testing is a statistical method that is used in making statistical decisions using experimental data. Hypothesis Testing is basically an assumption that we make about the population parameter. Ex : you say avg student in class is 40 or a boy is taller than girls.
To calculate the Hypothesis Space: if we have the given image above we can then figure it out the following way. Count the number of attributes or features. In this case, we have four features or (4). Analyze or if given what are the values corresponding to each feature (e.g. binary, or many different inputs).
Hypothesis Space Search by ID3: ID3 climbs the hill of knowledge acquisition by searching the space of feasible decision trees. It looks for all finite discrete-valued functions in the whole space. Every function is represented by at least one tree. It only holds one theory (unlike Candidate-Elimination).
In this post, you will discover a cheat sheet for the most popular statistical hypothesis tests for a machine learning project with examples using the Python API. Each statistical test is presented in a consistent way, including: The name of the test. What the test is checking. The key assumptions of the test. How the test result is interpreted.
Decision trees can represent any Boolean (binary) function, and the hypothesis space being searched is the entire space of Boolean functions1; however, we need to keep in mind that a critical challenge in machine learning is whether an algorithm can learn/ nd the \right" function or a good approximation within that subspace being searched.
Reference. General-To-Specific Ordering of Hypothesis. The theories can be sorted from the most specific to the most general. This will allow the machine learning algorithm to thoroughly investigate the hypothesis space without having to enumerate each and every hypothesis in it, which is impossible when the hypothesis space is infinitely vast.
A learning rate or step-size parameter used by gradient-based methods. h() A hypothesis map that reads in features x of a data point and delivers a prediction ^y= h(x) for its label y. H A hypothesis space or model used by a ML method. The hypothesis space consists of di erent hypothesis maps h: X!Ybetween which the ML method has to choose. 8
In Machine Learning, concept learning can be termed as "a problem of searching through a predefined space of potential hypothesis for the hypothesis that best fits the training examples" - Tom Mitchell. In this article, we will go through one such concept learning algorithm known as the Find-S algorithm. If you want to go beyond this article and really want the level of expertise in you ...
Generalization Bound: 1st Attempt. In order for the entire hypothesis space to have a generalization gap bigger than ϵ, at least one of its hypothesis: h 1 or h 2 or h 3 or … etc should have. This can be expressed formally by stating that: P[ sup h ∈ H | R(h) − Remp(h) | > ϵ] = P[ ⋃ h ∈ H | R(h) − Remp(h) | > ϵ]
The steps involved in the hypothesis testing are as follow: Assume a null hypothesis, usually in machine learning algorithms we consider that there is no anomaly between the target and independent variable. Collect a sample. Calculate test statistics. Decide either to accept or reject the null hypothesis.
This paper introduces a hypothesis space for deep learning that employs deep neural networks (DNNs). By treating a DNN as a function of two variables, the physical variable and parameter variable, we consider the primitive set of the DNNs for the parameter variable located in a set of the weight matrices and biases determined by a prescribed depth and widths of the DNNs. We then complete the ...
Creating a Python example that connects the Riemann Hypothesis directly with feature space analysis in machine learning, particularly with a synthetic dataset, is relatively abstract.
The main goal of this reading is to understand enough statistical methodology to be able to leverage the machine learning algorithms in Python's scikit-learn library and then apply this knowledge to solve a classic machine learning problem. The first stop of our journey will take us through a brief history of machine learning.
Modules / Lectures. Lecture 02: Different Types of Learning. Lecture 03: Hypothesis Space and Inductive Bias. Lecture 04: Evaluation and Cross-Validation. Tutorial I. Lecture 32 : Neural Network and Backpropagation Algorithm. Lecture 36: Introduction to Computational Learning Theory. Lecture 37: Sample Complexity : Finite Hypothesis Space.
In supervised machine learning, a hypothesis is a function that best characterizes the target. ... Python Boot Camp. Email * Single Line Text * Enroll Now. Machine Learning - Tutorial ... Hypothesis space search; Machine Learning- Genetic programming; Machine Learning- GENETIC ALGORITHM: MODELS OF EVOLUTION ...
The find-S algorithm is a basic concept learning algorithm in machine learning. The find-S algorithm finds the most specific hypothesis that fits all the positive examples. We have to note here that the algorithm considers only those positive training example. The find-S algorithm starts with the most specific hypothesis and generalizes this ...
The hypothesis is one of the commonly used concepts of statistics in Machine Learning. It is specifically used in Supervised Machine learning, where an ML model learns a function that best maps the input to corresponding outputs with the help of an available dataset. In supervised learning techniques, the main aim is to determine the possible ...
Straw burning is recognized as a significant contributor to deteriorating air quality, but its specific impacts, particularly on PM2.5 concentrations, are still not fully understood or quantified. In this study, we conducted a detailed examination of the spatial and temporal patterns of straw burning in Heilongjiang Province, China—a key agricultural area—utilizing high-resolution fire ...
The candidate elimination algorithm incrementally builds the version space given a hypothesis space H and a set E of examples. The examples are added one by one; each example possibly shrinks the version space by removing the hypotheses that are inconsistent with the example. The candidate elimination algorithm does this by updating the general ...