Land-use regression model and machine learning algorithms in assessing outdoor noise exposure in Hong Kong

This paper uses machine learning to refine a Land-use Regression (LUR) model and to estimate the spatial–temporal variation in BTEX concentrations in Kaohsiung, Taiwan. Using the Taiwanese Environmental Protection Agency (EPA) data of BTEX (benzene, toluene, ethylbenzene, and xylenes) concentrations from 2015 to 2018, which includes local emission sources as a result of Asian cultural characteristics, a new LUR model is developed. The 2019 data was then used as external data to verify the reliability of the model. We used hybrid Kriging-land-use regression (Hybrid Kriging-LUR) models, geographically weighted regression (GWR), and two machine learning algorithms—random forest (RF) and extreme gradient boosting (XGBoost)—for model development. Initially, the proposed Hybrid Kriging-LUR models explained each variation in BTEX from 37% to 52%. Using machine learning algorithms (XGBoost) increased the explanatory power of the models for each BTEX, between 61% and 79%. This study compared each combination of the Hybrid Kriging-LUR model and (i) GWR, (ii) RF, and (iii) XGBoost algorithm to estimate the spatiotemporal variation in BTEX concentration. It is shown that a combination of Hybrid Kriging-LUR and the XGBoost algorithm gives better performance than other integrated methods.

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Artificial Intelligence, Machine Learning and Calculation of Intraocular Lens Power

Klinische Monatsblätter für Augenheilkunde ◽

10.1055/a-1298-8121 ◽

2020 ◽

Vol 237 (12) ◽

pp. 1430-1437

Author(s):

Achim Langenbucher ◽

Nóra Szentmáry ◽

Jascha Wendelstein ◽

Peter Hoffmann

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Regression Model ◽

Prediction Error ◽

Learning Algorithms ◽

Anterior Chamber Depth ◽

Gaussian Process Regression ◽

Machine Learning Algorithms ◽

Effect Sizes ◽

Axial Position

Abstract Background and Purpose In the last decade, artificial intelligence and machine learning algorithms have been more and more established for the screening and detection of diseases and pathologies, as well as for describing interactions between measures where classical methods are too complex or fail. The purpose of this paper is to model the measured postoperative position of an intraocular lens implant after cataract surgery, based on preoperatively assessed biometric effect sizes using techniques of machine learning. Patients and Methods In this study, we enrolled 249 eyes of patients who underwent elective cataract surgery at Augenklinik Castrop-Rauxel. Eyes were measured preoperatively with the IOLMaster 700 (Carl Zeiss Meditec), as well as preoperatively and postoperatively with the Casia 2 OCT (Tomey). Based on preoperative effect sizes axial length, corneal thickness, internal anterior chamber depth, thickness of the crystalline lens, mean corneal radius and corneal diameter a selection of 17 machine learning algorithms were tested for prediction performance for calculation of internal anterior chamber depth (AQD_post) and axial position of equatorial plane of the lens in the pseudophakic eye (LEQ_post). Results The 17 machine learning algorithms (out of 4 families) varied in root mean squared/mean absolute prediction error between 0.187/0.139 mm and 0.255/0.204 mm (AQD_post) and 0.183/0.135 mm and 0.253/0.206 mm (LEQ_post), using 5-fold cross validation techniques. The Gaussian Process Regression Model using an exponential kernel showed the best performance in terms of root mean squared error for prediction of AQDpost and LEQpost. If the entire dataset is used (without splitting for training and validation data), comparison of a simple multivariate linear regression model vs. the algorithm with the best performance showed a root mean squared prediction error for AQD_post/LEQ_post with 0.188/0.187 mm vs. the best performance Gaussian Process Regression Model with 0.166/0.159 mm. Conclusion In this paper we wanted to show the principles of supervised machine learning applied to prediction of the measured physical postoperative axial position of the intraocular lenses. Based on our limited data pool and the algorithms used in our setting, the benefit of machine learning algorithms seems to be limited compared to a standard multivariate regression model.

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Land Use/Land Cover Mapping from Airborne Hyperspectral Images with Machine Learning Algorithms and Contextual Information

Geocarto International ◽

10.1080/10106049.2021.1945149 ◽

2021 ◽

pp. 1-40

Author(s):

Ozlem Akar ◽

Esra Tunc Gormus

Keyword(s):

Machine Learning ◽

Land Use ◽

Land Cover ◽

Contextual Information ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Hyperspectral Images ◽

Land Cover Mapping ◽

Land Use Land Cover

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Machine-learning algorithms for land use dynamics in Lake Haramaya Watershed, Ethiopia

Modeling Earth Systems and Environment ◽

10.1007/s40808-021-01296-0 ◽

2021 ◽

Author(s):

Gezahegn Weldu Woldemariam ◽

Degefie Tibebe ◽

Tesfamariam Engida Mengesha ◽

Tadele Bedo Gelete

Keyword(s):

Machine Learning ◽

Land Use ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Land Use Dynamics

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Land-use and land-cover classification using Sentinel-2 data and machine-learning algorithms: operational method and its implementation for a mountainous area of Nepal

Journal of Applied Remote Sensing ◽

10.1117/1.jrs.13.014530 ◽

2019 ◽

Vol 13 (01) ◽

pp. 1 ◽

Cited By ~ 4

Author(s):

Marie Delalay ◽

Varun Tiwari ◽

Alan D. Ziegler ◽

Vik Gopal ◽

Paul Passy

Keyword(s):

Machine Learning ◽

Land Use ◽

Land Cover ◽

Learning Algorithms ◽

Land Cover Classification ◽

Machine Learning Algorithms ◽

Mountainous Area ◽

Operational Method ◽

Sentinel 2

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Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

Remote Sensing ◽

10.3390/rs12071135 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1135 ◽

Cited By ~ 15

Author(s):

Swapan Talukdar ◽

Pankaj Singha ◽

Susanta Mahato ◽

Shahfahad ◽

Swades Pal ◽

...

Keyword(s):

Machine Learning ◽

Land Use ◽

Land Cover ◽

Learning Algorithms ◽

Kappa Coefficient ◽

Machine Learning Algorithms ◽

The Other ◽

Land Use Land Cover ◽

Accuracy Level ◽

Parametric Classifier

Rapid and uncontrolled population growth along with economic and industrial development, especially in developing countries during the late twentieth and early twenty-first centuries, have increased the rate of land-use/land-cover (LULC) change many times. Since quantitative assessment of changes in LULC is one of the most efficient means to understand and manage the land transformation, there is a need to examine the accuracy of different algorithms for LULC mapping in order to identify the best classifier for further applications of earth observations. In this article, six machine-learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM) and Mahalanobis distance (MD) were examined. Accuracy assessment was performed by using Kappa coefficient, receiver operational curve (RoC), index-based validation and root mean square error (RMSE). Results of Kappa coefficient show that all the classifiers have a similar accuracy level with minor variation, but the RF algorithm has the highest accuracy of 0.89 and the MD algorithm (parametric classifier) has the least accuracy of 0.82. In addition, the index-based LULC and visual cross-validation show that the RF algorithm (correlations between RF and normalised differentiation water index, normalised differentiation vegetation index and normalised differentiation built-up index are 0.96, 0.99 and 1, respectively, at 0.05 level of significance) has the highest accuracy level in comparison to the other classifiers adopted. Findings from the literature also proved that ANN and RF algorithms are the best LULC classifiers, although a non-parametric classifier like SAM (Kappa coefficient 0.84; area under curve (AUC) 0.85) has a better and consistent accuracy level than the other machine-learning algorithms. Finally, this review concludes that the RF algorithm is the best machine-learning LULC classifier, among the six examined algorithms although it is necessary to further test the RF algorithm in different morphoclimatic conditions in the future.

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