Support vector machines for oil classification link with polyaromatic hydrocarbon contamination in the environment

Ismail, Azimah; Juahir, Hafizan; Mohamed, Saiful Bahri; Toriman, Mohd Ekhwan; Kassim, Azlina Md.; Zain, Sharifuddin M.; Monajemi, Hadieh; Ahmad, Wan Kamaruzaman Wan; Zali, Munirah Abdul; Retnam, Ananthy; Taib, Mohd. Zaki Mohd.; Mokhtar, Mazlin; Abdullah, Siti Nor Fazillah

doi:10.2166/wst.2021.038

Cited by 4 publications

(3 citation statements)

References 33 publications

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“…Machine learning algorithms, such as fuzzy classifier (FC) (Trimble Germany GmbH, 2014a; Cai and Kwan, 1998), k-nearest neighbor classifier (KNN) (Bai et al, 2021;, Bayes classifier (Bayes) (Han et al, 2012;Brunner et al, 2021), classification and regression tree (CART) Zhang and Yang, 2020), support vector machine (SVM) (He et al, 2007;Ismail et al, 2021), and random forest (RF) Dobrinić et al, 2021) algorithms, have been widely used in land surface automatic classification, especially in land use/cover classification. For example, Zhang and Yang (2020) improved land cover classification based on the CART method; Dobrinić et al (2021) built an accurate vegetation map using a RF algorithm.…”

Section: Introductionmentioning

confidence: 99%

Nature-Based Solutions for Urban Water Management

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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Section: Introductionmentioning

confidence: 99%

Nature-Based Solutions for Urban Water Management

2024

Frontiers Research Topics

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“…Support vector machine (SVM) is a well-known machine learning technology that is frequently employed for data-driven modelling in engineering applications, natural behavior, and Frontiers in Environmental Science frontiersin.org water quality research (Ling et al, 2019;Yahya et al, 2019;Ismail et al, 2021;Leong et al, 2021). Solano Meza et al (2019) estimated the generation of municipal waste in the city of Bogota using the SVM model.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of water quality index prediction using support vector machine with sensitivity analysis

Mamat

Razali

Hamzah

2023

Front. Environ. Sci.

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For more than 25 years, the Department of Environment (DOE) of Malaysia has implemented a water quality index (WQI) that uses six key water quality parameters: dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), pH, ammoniacal nitrogen (AN), and suspended solids (SS). Water quality analysis is an essential component of water resources management that must be properly managed to prevent ecological damage from pollution and to ensure compliance with environmental regulations. This increases the need to define an efficient method for WQI analysis. One of the major challenges with the current calculation of the WQI is that it requires a series of sub-index calculations that are time consuming, complex, and prone to error. In addition, the WQI cannot be calculated if one or more water quality parameters are missing. In this study, the optimization method of WQI was developed to address the complexity of the current process. The potential of data-driven modeling, i.e., Support Vector Machine (SVM) based on Nu-Radial basis function with 10-fold cross-validation, was developed and explored to improve the prediction of WQI in Langat watershed. A thorough sensitivity analysis under six scenarios was also conducted to determine the efficiency of the model in WQI prediction. In the first scenario, the model SVM-WQI showed exceptional ability to replicate the DOE-WQI and obtained statistical results at a very high level (correlation coefficient, r > 0.95, Nash Sutcliffe efficiency, NSE >0.88, Willmott’s index of agreement, WI > 0.96). In the second scenario, the modeling process showed that the WQI can be estimated without any of the six parameters. It can be seen that the parameter DO is the most important factor in determining the WQI. The pH is the factor that affects the WQI the least. Moreover, scenarios three to six show the efficiency of the model in terms of time and cost by minimizing the number of variables in the input combination of the model (r > 0.6, NSE >0.5 (good), WI > 0.7 (very good)). In summary, the model will greatly improve and accelerate data-driven decision making in water quality management by making data more accessible and attractive without human intervention.

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“…Machine learning algorithms, such as fuzzy classifier (FC) (Trimble Germany GmbH, 2014a;Cai and Kwan, 1998), k-nearest neighbor classifier (KNN) (Bai et al, 2021;Li et al, 2016), Bayes classifier (Bayes) (Han et al, 2012;Brunner et al, 2021), classification and regression tree (CART) (Li et al, 2016;Zhang and Yang, 2020), support vector machine (SVM) (He et al, 2007;Ismail et al, 2021), and random forest (RF) (Li et al, 2016;Dobrinić et al, 2021) algorithms, have been widely used in land surface automatic classification, especially in land use/cover classification. For example, Zhang and Yang (2020) improved land cover classification based on the CART method; Dobrinić et al (2021) built an accurate vegetation map using a RF algorithm.…”

Section: Introductionmentioning

confidence: 99%

Urban Catchment-Scale Blue-Green-Gray Infrastructure Classification with Unmanned Aerial Vehicle Images and Machine Learning Algorithms

Jia

Cui

Liu

2022

Front. Environ. Sci.

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Green infrastructure (GI), such as green roofs, is now widely used in sustainable urban development. An accurate mapping of GI is important to provide surface parameterization for model development. However, the accuracy and precision of mapping GI is still a challenge in identifying GI at the small catchment scale. We proposed a framework for blue-green-gray infrastructure classification using machine learning algorithms and unmanned aerial vehicle (UAV) images that contained digital surface model (DSM) information. We used the campus of the Southern University of Science and Technology in Shenzhen, China, as a study case for our classification method. The UAV was a DJI Phantom 4 Multispectral, which measures the blue, green, red, red-edge, and near-infrared bands and DSM information. Six machine learning algorithms, i.e., fuzzy classifier, k-nearest neighbor classifier, Bayes classifier, classification and regression tree, support vector machine (SVM), and random forest (RF), were used to classify blue (including water), green (including green roofs, grass, trees (shrubs), bare land), and gray (including buildings, roads) infrastructure. The highest kappa coefficient was observed for RF and the lowest was observed for SVM, with coefficients of 0.807 and 0.381, respectively. We optimized the sampling method based on a chessboard grid and got the optimal sampling interval of 11.6 m to increase the classification efficiency. We also analyzed the effects of weather conditions, seasons, and different image layers, and found that images in overcast days or winter days could improve the classification accuracy. In particular, the DSM layer was crucial for distinguishing green roofs and grass, and buildings and roads. Our study demonstrates the feasibility of using UAV images in urban blue-green-gray infrastructure classification, and our infrastructure classification framework based on machine learning algorithms is effective. Our results could provide the basis for the future urban stormwater management model development and aid sustainable urban planning.

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Support vector machines for oil classification link with polyaromatic hydrocarbon contamination in the environment

Cited by 4 publications

References 33 publications

Nature-Based Solutions for Urban Water Management

Nature-Based Solutions for Urban Water Management

Enhancement of water quality index prediction using support vector machine with sensitivity analysis

Urban Catchment-Scale Blue-Green-Gray Infrastructure Classification with Unmanned Aerial Vehicle Images and Machine Learning Algorithms

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