scholarly journals Machine Learning Applied to the Oxygen-18 Isotopic Composition, Salinity and Temperature/Potential Temperature in the Mediterranean Sea

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2523
Author(s):  
Gonzalo Astray ◽  
Benedicto Soto ◽  
Enrique Barreiro ◽  
Juan F. Gálvez ◽  
Juan C. Mejuto
Keyword(s):  
Random Forest ◽  
Mediterranean Sea ◽  
Isotope Composition ◽  
Potential Temperature ◽  
Percentage Error ◽  
Support Vector ◽  
Temperature Potential ◽  
Random Forest Models ◽  
The Mediterranean ◽  
Artificial Neural Network Ann

This study proposed different techniques to estimate the isotope composition (δ18O), salinity and temperature/potential temperature in the Mediterranean Sea using five different variables: (i–ii) geographic coordinates (Longitude, Latitude), (iii) year, (iv) month and (v) depth. Three kinds of models based on artificial neural network (ANN), random forest (RF) and support vector machine (SVM) were developed. According to the results, the random forest models presents the best prediction accuracy for the querying phase and can be used to predict the isotope composition (mean absolute percentage error (MAPE) around 4.98%), salinity (MAPE below 0.20%) and temperature (MAPE around 2.44%). These models could be useful for research works that require the use of past data for these variables.

scholarly journals Machine Learning-Based Prediction of Air Quality

2020 ◽  
Vol 10 (24) ◽  
pp. 9151
Author(s):  
Yun-Chia Liang ◽  
Yona Maimury ◽  
Angela Hsiang-Ling Chen ◽  
Josue Rodolfo Cuevas Juarez
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Random Forest ◽  
Prediction Models ◽  
Superior Performance ◽  
Support Vector ◽  
Economic Activities ◽  
Adaptive Boosting ◽  
Series Of Experiments ◽  
Artificial Neural Network Ann

Air, an essential natural resource, has been compromised in terms of quality by economic activities. Considerable research has been devoted to predicting instances of poor air quality, but most studies are limited by insufficient longitudinal data, making it difficult to account for seasonal and other factors. Several prediction models have been developed using an 11-year dataset collected by Taiwan’s Environmental Protection Administration (EPA). Machine learning methods, including adaptive boosting (AdaBoost), artificial neural network (ANN), random forest, stacking ensemble, and support vector machine (SVM), produce promising results for air quality index (AQI) level predictions. A series of experiments, using datasets for three different regions to obtain the best prediction performance from the stacking ensemble, AdaBoost, and random forest, found the stacking ensemble delivers consistently superior performance for R2 and RMSE, while AdaBoost provides best results for MAE.

scholarly journals Glass Classification based on Machine Learning Algorithms

2020 ◽  
Vol 9 (11) ◽  
pp. 139-142
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Amorphous Solid ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
X Ray ◽  
Svm Algorithm ◽  
Artificial Neural Network Ann ◽  
Logistic Regression Algorithm

Glass Industry is considered one of the most important industries in the world. The Glass is used everywhere, from water bottles to X-Ray and Gamma Rays protection. This is a non-crystalline, amorphous solid that is most often transparent. There are lots of uses of glass, and during investigation in a crime scene, the investigators need to know what is type of glass in a scene. To find out the type of glass, we will use the online dataset and machine learning to solve the above problem. We will be using ML algorithms such as Artificial Neural Network (ANN), K-nearest neighbors (KNN) algorithm, Support Vector Machine (SVM) algorithm, Random Forest algorithm, and Logistic Regression algorithm. By comparing all the algorithm Random Forest did the best in glass classification.

scholarly journals Determination of the copper isotope composition of seawater revisited: A case study from the Mediterranean Sea

2019 ◽  
Vol 511 ◽  
pp. 465-480 ◽  
Author(s):  
Isabelle Baconnais ◽  
Olivier Rouxel ◽  
Gabriel Dulaquais ◽  
Marie Boye
Keyword(s):  
Mediterranean Sea ◽  
Isotope Composition ◽  
Copper Isotope ◽  
The Mediterranean

Isotope composition of air moisture over the Mediterranean Sea: an index of the air-sea interaction pattern

Tellus B ◽  
2003 ◽  
Vol 55 (5) ◽  
pp. 953-965 ◽  
Author(s):  
J. R. GAT ◽  
B. KLEIN ◽  
Y. KUSHNIR ◽  
W. ROETHER ◽  
H. WERNLI ◽  
...  
Keyword(s):  
Mediterranean Sea ◽  
Isotope Composition ◽  
Interaction Pattern ◽  
The Mediterranean

scholarly journals A Combination of Feature Selection and Random Forest Techniques to Solve a Problem Related to Blast-Induced Ground Vibration

2020 ◽  
Vol 10 (3) ◽  
pp. 869 ◽  
Author(s):  
Hong Zhang ◽  
Jian Zhou ◽  
Danial Jahed Armaghani ◽  
M. M. Tahir ◽  
Binh Thai Pham ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Regression Models ◽  
Process Analysis ◽  
Ground Vibration ◽  
Predictive Performance ◽  
Support Vector ◽  
Interaction Detection ◽  
Artificial Neural Network Ann

In mining and civil engineering applications, a reliable and proper analysis of ground vibration due to quarry blasting is an extremely important task. While advances in machine learning led to numerous powerful regression models, the usefulness of these models for modeling the peak particle velocity (PPV) remains largely unexplored. Using an extensive database comprising quarry site datasets enriched with vibration variables, this article compares the predictive performance of five selected machine learning classifiers, including classification and regression trees (CART), chi-squared automatic interaction detection (CHAID), random forest (RF), artificial neural network (ANN), and support vector machine (SVM) for PPV analysis. Before conducting these model developments, feature selection was applied in order to select the most important input parameters for PPV. The results of this study show that RF performed substantially better than any of the other investigated regression models, including the frequently used SVM and ANN models. The results and process analysis of this study can be utilized by other researchers/designers in similar fields.

scholarly journals Physicochemical Habitat Traits Preferred by Small Indigenous Fish (Chanda Nama ) in Indian River Discerning through Machine Learning

2021 ◽  
Author(s):  
Rohan Kumar Raman ◽  
Archan Kanti Das ◽  
Ranjan Kumar Manna ◽  
Sanjeev Kumar Sahu ◽  
Basanta Kumar Das
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
River System ◽  
Fish Distribution ◽  
Support Vector ◽  
Operating Characteristics ◽  
K Nearest Neighbors ◽  
Peninsular India ◽  
Preferred Habitat ◽  
Artificial Neural Network Ann

Abstract Physicochemical traits of river influence the habitat of fish species in aquatic ecosystems. Fish showed a complex relationship with aquatic factors in river. Machine learning (ML) modeling is a useful tool to established relationship between complex systems. This study identified the preferred habitat indicators of Chanda nama (a small indigenous fish), in the Krishna River, of peninsular India, using machine learning modeling. Data were observed on Chanda nama fish distribution (presence/absence) and associated ten physical and chemical parameters of water at 22 sampling sites on the river during year 2001-02. Machine learning models such as random forest (RF), artificial neural network (ANN), support vector machine (SVM), k-nearest neighbors (KNN) used for the classification of Chanda nama distribution in the river. The ML model efficiency was evaluated using classification accuracy (CCI), Cohen’s kappa coefficient (k), sensitivity, specificity and receiver-operating-characteristics (ROC). Results showed that random forest is the best model with 82% accuracy, CCI (0.82), k (0.55), sensitivity (0.57), specificity (0.76) and ROC (0.72) for Chanda nama distribution (presence/absence) in the Krishna River. Random Forest model identified three preferred physicochemical habitat traits like altitude, temperature and depth for Chanda nama distribution in the Krishna River, India. This study will be helpful for researcher and policy maker to understand the important habitat physicochemical traits for sustainable management of small indigenous fish (Chanda nama ) in the river system.

scholarly journals Feature selection for improving Indian spoken language identification in utterance duration mismatch condition

2021 ◽  
Vol 10 (5) ◽  
pp. 2578-2587
Author(s):  
Aarti Bakshi ◽  
Sunil Kumar Kopparapu
Keyword(s):  
Random Forest ◽  
Spoken Language ◽  
Relative Increase ◽  
Optimal Number ◽  
Language Identification ◽  
Training Data ◽  
Support Vector ◽  
Indian Languages ◽  
Artificial Neural Network Ann ◽  
Mismatch Condition

In spoken language identification (SLID) systems, the test data may be of a sufficiently shorter duration than training data, known as duration mismatch condition. Duration normalized features are used to identify a spoken language for nine Indian languages in duration mismatch conditions. Random forest-based importance vectors of 1582 OpenSMILE features are calculated for each utterance in different duration datasets. The feature importance vectors are normalized across each dataset and later across different duration datasets. The optimal number of duration normalized features is selected to maximize SLID system accuracy. Three classifiers, artificial neural network (ANN), support vector machine (SVM), and random forest (RF), and their fusion, weights optimized using logistic regression, are used. The speech material comprised utterances, each of 30 sec, extracted from the All India Radio dataset with nine Indian languages. Seven new datasets of smaller utterance durations were generated by carefully splitting each utterance. Experimental results showed that 150 most important duration normalized features were optimal with a relative increase in 18-80% accuracy for mismatch conditions. The accuracy decreased with increased duration mismatch.

Sign in / Sign up

Export Citation Format

Share Document