scholarly journals Ensemble Machine Learning Methods to Estimate the Sugarcane Yield Based on Remote Sensing Information

2020 ◽  
Vol 34 (6) ◽  
pp. 731-743
Author(s):  
Sandeep Kumar Singla ◽  
Rahul Dev Garg ◽  
Om Prakash Dubey
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Ensemble Machine Learning ◽  
Sugarcane Yield

Detecting a keystone species European aspen in boreal forests with airborne hyperspectral, LiDAR and UAV data with machine learning methods

2021 ◽  
Author(s):  
Timo Kumpula ◽  
Janne Mäyrä ◽  
Anton Kuzmin ◽  
Arto Viinikka ◽  
Sonja Kivinen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Boreal Forests ◽  
Tree Level ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
European Aspen

<p>Sustainable forest management increasingly highlights the maintenance of biological diversity and requires up-to-date information on the occurrence and distribution of key ecological features in forest environments. Different proxy variables indicating species richness and quality of the sites are essential for efficient detecting and monitoring forest biodiversity. European aspen (Populus tremula L.) is a minor deciduous tree species with a high importance in maintaining biodiversity in boreal forests. Large aspen trees host hundreds of species, many of them classified as threatened. However, accurate fine-scale spatial data on aspen occurrence remains scarce and incomprehensive.</p><p> </p><p>We studied detection of aspen using different remote sensing techniques in Evo, southern Finland. Our study area of 83 km<sup>2</sup> contains both managed and protected southern boreal forests characterized by Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst), and birch (Betula pendula and pubescens L.), whereas European aspen has a relatively sparse and scattered occurrence in the area. We collected high-resolution airborne hyperspectral and airborne laser scanning data covering the whole study area and ultra-high resolution unmanned aerial vehicle (UAV) data with RGB and multispectral sensors from selected parts of the area. We tested the discrimination of aspen from other species at tree level using different machine learning methods (Support Vector Machines, Random Forest, Gradient Boosting Machine) and deep learning methods (3D convolutional neural networks).</p><p> </p><p>Airborne hyperspectral and lidar data gave excellent results with machine learning and deep learning classification methods The highest classification accuracies for aspen varied between 91-92% (F1-score). The most important wavelengths for discriminating aspen from other species included reflectance bands of red edge range (724–727 nm) and shortwave infrared (1520–1564 nm and 1684–1706 nm) (Viinikka et al. 2020; Mäyrä et al 2021). Aspen detection using RGB and multispectral data also gave good results (highest F1-score of aspen = 87%) (Kuzmin et al 2021). Different remote sensing data enabled production of a spatially explicit map of aspen occurrence in the study area. Information on aspen occurrence and abundance can significantly contribute to biodiversity management and conservation efforts in boreal forests. Our results can be further utilized in upscaling efforts aiming at aspen detection over larger geographical areas using satellite images.</p>

scholarly journals Prediction of Concrete Properties Using Ensemble Machine Learning Methods

2020 ◽  
Vol 1625 ◽  
pp. 012024
Author(s):  
D Prayogo ◽  
D I Santoso ◽  
D Wijaya ◽  
T Gunawan ◽  
J A Widjaja
Keyword(s):  
Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Concrete Properties ◽  
Ensemble Machine Learning

scholarly journals Investigating Algorithmic Stock Market Trading using Ensemble Machine Learning Methods

Informatica ◽  
2020 ◽  
Vol 44 (3) ◽  
Author(s):  
Ramzi Saifan ◽  
Khaled Sharif ◽  
Mohammad Abu-Ghazaleh ◽  
Mohammad Abdel-Majeed
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Ensemble Machine Learning ◽  
Stock Market Trading

scholarly journals Modelling and Forecasting Temporal PM2.5 Concentration Using Ensemble Machine Learning Methods

Buildings ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 46
Author(s):  
Obuks Augustine Ejohwomu ◽  
Olakekan Shamsideen Oshodi ◽  
Majeed Oladokun ◽  
Oyegoke Teslim Bukoye ◽  
Nwabueze Emekwuru ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Pollutants ◽  
Data Science ◽  
Climatic Factors ◽  
Extreme Weather Events ◽  
Learning Methods ◽  
Rapid Urbanization ◽  
Machine Learning Methods ◽  
Ensemble Machine Learning ◽  
Pm2.5 Concentration

Exposure of humans to high concentrations of PM2.5 has adverse effects on their health. Researchers estimate that exposure to particulate matter from fossil fuel emissions accounted for 18% of deaths in 2018—a challenge policymakers argue is being exacerbated by the increase in the number of extreme weather events and rapid urbanization as they tinker with strategies for reducing air pollutants. Drawing on a number of ensemble machine learning methods that have emerged as a result of advancements in data science, this study examines the effectiveness of using ensemble models for forecasting the concentrations of air pollutants, using PM2.5 as a representative case. A comprehensive evaluation of the ensemble methods was carried out by comparing their predictive performance with that of other standalone algorithms. The findings suggest that hybrid models provide useful tools for PM2.5 concentration forecasting. The developed models show that machine learning models are efficient in predicting air particulate concentrations, and can be used for air pollution forecasting. This study also provides insights into how climatic factors influence the concentrations of pollutants found in the air.

scholarly journals APPLICATION OF ENSEMBLE MACHINE LEARNING METHODS FOR PREDICTING THE TECHNICAL STATE OF AN OBJECT

2021 ◽  
Vol 23 (1) ◽  
pp. 111-114
Author(s):  
Yu.E. Kuvayskova ◽  
Keyword(s):  
Machine Learning ◽  
The State ◽  
Technical State ◽  
Supervised Machine Learning ◽  
Time Interval ◽  
Technical Documentation ◽  
Technical Object ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Ensemble Machine Learning

To ensure the reliable functioning of a technical object, it is necessary to predict its state for the upcoming time interval. Let the technical state of the object be characterized at a certain point in time by a set of parameters established by the technical documentation for the object. It is assumed that for certain values of these parameters, the object may be in a good or faulty state. It is required by the values of these parameters to estimate the state of the object in the upcoming time interval. Supervised machine learning methods can be applied to solve this problem. However, to obtain good results in predicting the state of an object, it is necessary to choose the correct training model. One of the disadvantages of machine learning models is high bias and too much scatter. In this paper, to reduce the scatter of the model, it is proposed to use ensemble machine learning methods, namely, the bagging procedure. The main idea of the ensemble of methods is that with the right combination of weak models, more accurate and robust models can be obtained. The purpose of bagging is to create an ensemble model that is more reliable than the individual models that make up it. One of the big advantages of bagging is its concurrency, since different ensemble models are trained independently of each other. The effectiveness of the proposed approach is shown by the example of predicting the technical state of an object by eight parameters of its functioning. To assess the effectiveness of the application of ensemble machine learning methods for predicting the technical state of an object, the quality criteria of binary classification are used: accuracy, completeness, and F-measure. It is shown that the use of ensemble machine learning methods can improve the accuracy of predicting the state of a technical object by 4% –9% in comparison with basic machine learning methods. This approach can be used by specialists to predict the technical condition of objects in many technical applications, in particular, in aviation.

Forecasting monthly fluctuations of lake surface areas using remote sensing techniques and novel machine learning methods

2020 ◽  
Vol 143 (1-2) ◽  
pp. 713-735 ◽  
Author(s):  
Keyvan Soltani ◽  
Afshin Amiri ◽  
Mohammad Zeynoddin ◽  
Isa Ebtehaj ◽  
Bahram Gharabaghi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Lake Surface ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Surface Areas ◽  
Remote Sensing Techniques

scholarly journals Spatial Prediction of Agrochemical Properties on the Scale of a Single Field Using Machine Learning Methods Based on Remote Sensing Data

Agronomy ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2266
Author(s):  
Ilnas Sahabiev ◽  
Elena Smirnova ◽  
Kamil Giniyatullin
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Remote Sensing Data ◽  
Spatial Prediction ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Agrochemical Properties ◽  
Sentinel 2

Creating accurate digital maps of the agrochemical properties of soils on a field scale with a limited data set is a problem that slows down the introduction of precision farming. The use of machine learning methods based on the use of direct and indirect predictors of spatial changes in the agrochemical properties of soils is promising. Spectral indicators of open soil based on remote sensing data, as well as soil properties, were used to create digital maps of available forms of nitrogen, phosphorus, and potassium. It was shown that machine learning methods based on support vectors (SVMr) and random forest (RF) using spectral reflectance data are similarly accurate at spatial prediction. An acceptable prediction was obtained for available nitrogen and available potassium; the variability of available phosphorus was modeled less accurately. The coefficient of determination (R2) of the best model for nitrogen is R2SVMr = 0.90 (Landsat 8 OLI) and R2SVMr = 0.79 (Sentinel 2), for potassium—R2SVMr = 0.82 (Landsat 8 OLI) and R2SVMr = 0.77 (Sentinel 2), for phosphorus—R2SVMr = 0.68 (Landsat 8 OLI), R2SVMr = 0.64 (Sentinel 2). The models based on remote sensing data were refined when soil organic matter (SOC) and fractions of texture (Silt, Clay) were included as predictors. The SVMr models were the most accurate. For Landsat 8 OLI, the SVMr model has a R2 value: nitrogen—R2 = 0.95, potassium—R2 = 0.89 and phosphorus—R2 = 0.65. Based on Sentinel 2, nitrogen—R2 = 0.92, potassium—R2 = 0.88, phosphorus—R2 = 0.72. The spatial prediction of nitrogen content is influenced by SOC, potassium—by SOC and texture, phosphorus—by texture. The validation of the final models was carried out on an independent sample on soils from a chernozem zone. For nitrogen based on Landsat 8 OLI R2 = 0.88, for potassium R2 = 0.65, and for phosphorus R2 = 0.31. Based on Sentinel 2, for nitrogen R2 = 0.85, for potassium R2 = 0.62, and for phosphorus R2 = 0.71. The inclusion of SOC and texture in remote sensing-based machine learning models makes it possible to improve the spatial prediction of nitrogen, phosphorus and potassium availability of soils in chernozem zones and can potentially be widely used to create digital agrochemical maps on the scale of a single field.

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