Forecasting frost risk in forest plantations by the combination of spatial data and machine learning algorithms

2021 ◽  
Vol 306 ◽  
pp. 108450
Author(s):  
Écio Souza Diniz ◽  
Alexandre Simões Lorenzon ◽  
Nero Lemos Martins de Castro ◽  
Gustavo Eduardo Marcatti ◽  
Osmarino Pires dos Santos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spatial Data ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Forest Plantations ◽  
Frost Risk

scholarly journals Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data

2019 ◽  
Vol 406 ◽  
pp. 109-120 ◽  
Author(s):  
Patrick Schratz ◽  
Jannes Muenchow ◽  
Eugenia Iturritxa ◽  
Jakob Richter ◽  
Alexander Brenning
Keyword(s):  
Machine Learning ◽  
Performance Assessment ◽  
Spatial Data ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
And Performance

scholarly journals Using remote sensing data for environmental monitoring of water objects using GIS and machine learning

2021 ◽  
Vol 937 (2) ◽  
pp. 022051
Author(s):  
D Krivoguz ◽  
A Semenova ◽  
S Mal’ko
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Spatial Data ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Remote Sensing Data ◽  
Environmental Variable ◽  
Machine Learning Algorithms ◽  
Sensing Data ◽  
Water Ecosystems

Abstract The main way to understand variability of any spatial data using remote sensing is calculating spectral indices. For now, some difficulties have receiving water surface temperature due to specific properties for satellite sensors and low spatial resolution. The main sources of receiving salinity data are remote sensing data from ESA SMOS, NASA Aquarius and SMAP satellites. Using different machine learning algorithms, we can get models or equations, representing dependency between studied environmental variable and different spectral channels of remote monitoring data. After receiving and collecting remote sensing data in database this system uses machine learning algorithms to find dependency between collected field data and different spectral bands of the remote sensing data. Our goal was to form an analytical system based on remote sensors and machine learning algorithm to analyse, predict and evaluate water ecosystems for fisheries and environmental protection.

scholarly journals Supervised Machine Learning for Regionalization of Environmental Data: Distribution of Uranium in Groundwater in Ukraine

2018 ◽  
Vol 1 ◽  
pp. 1-4
Author(s):  
Michael Govorov ◽  
Gennady Gienko ◽  
Viktor Putrenko
Keyword(s):  
Machine Learning ◽  
Spatial Data ◽  
Learning Algorithms ◽  
Environmental Parameters ◽  
Unsupervised Classification ◽  
Machine Learning Algorithms ◽  
Environmental Data ◽  
Supervised Machine Learning ◽  
Ann Models ◽  
Homogeneous Regions

In this paper, several supervised machine learning algorithms were explored to define homogeneous regions of con-centration of uranium in surface waters in Ukraine using multiple environmental parameters. The previous study was focused on finding the primary environmental parameters related to uranium in ground waters using several methods of spatial statistics and unsupervised classification. At this step, we refined the regionalization using Artifi-cial Neural Networks (ANN) techniques including Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Convolutional Neural Network (CNN). The study is focused on building local ANN models which may significantly improve the prediction results of machine learning algorithms by taking into considerations non-stationarity and autocorrelation in spatial data.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

The Unlearnable Checkerboard Pattern

2019 ◽  
Vol 1 (2) ◽  
pp. 78-80
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Checkerboard Pattern ◽  
Simple Task

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms.  Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.

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