Sedimentary environment prediction of grain-size data based on machine learning approach

2020 ◽  
Vol 8 (3) ◽  
pp. SL71-SL78
Author(s):  
Qiao Su ◽  
Yanhui Zhu ◽  
Fang Hu ◽  
Xingyong Xu
Keyword(s):  
Machine Learning ◽  
Grain Size ◽  
Prediction Model ◽  
Sedimentary Environment ◽  
Machine Learning Algorithms ◽  
Grain Size Analysis ◽  
Size Analysis ◽  
Data Set ◽  
Sedimentary Environments ◽  
Size Data

Grain size is one of the most important records for sedimentary environment, and researchers have made remarkable progress in the interpretation of sedimentary environments by grain size analysis in the past few decades. However, these advances often depend on the personal experience of the scholars and combination with other methods used together. Here, we constructed a prediction model using the K-nearest neighbors algorithm, one of the machine learning methods, which can predict the sedimentary environments of one core through a known core. Compared to the results of other studies based on the comprehensive data set of grain size and four other indicators, this model achieved a high precision value only using the grain size data. We have also compared our prediction model with other mainstream machine learning algorithms, and the experimental results of six evaluation metrics shed light on that this prediction model can achieve the higher precision. The main errors of the model reflect the length of the conversation area of sedimentary environment, which is controlled by the sedimentary dynamics. This model can provide a quick comparison method of the cores in a similar environment; thus, it may point out the preliminary guidance for further study.

Analysis on Grain-Size Characteristics and Sedimentary Environment of Conglomerate Reservoirs in Ying-4 Section of Xingcheng Gas Field of Songliao Basin

2013 ◽  
Vol 734-737 ◽  
pp. 286-289
Author(s):  
Yu Sun ◽  
Xiu Li Zhang ◽  
Jin Yan Zhang ◽  
Chen Chen
Keyword(s):  
Grain Size ◽  
Sedimentary Environment ◽  
Songliao Basin ◽  
Grain Size Analysis ◽  
Size Analysis ◽  
Gas Field ◽  
Environment Analysis ◽  
Two Dimension ◽  
Sedimentary Characteristics ◽  
Fan Deltas

Aimed at difficulty of conglomerate in Ying-4 Section of Xingcheng Gas Field, Songliao Basin, which it is impossible to be carried out, using routine granularity analysis methods, and the iconic grain-size analysis is carried out. According to iconic two-dimension grain-size analysis, and contacting to fundamental sedimentary characteristics of conglomerate, sedimentary environment analysis is carried out. It is thought that this area is Shallow gravel-bed braided fluvial fan deltas, to braided fluvial delta transition, which supported an important geological foundation for the next disposition of exploration in Xingcheng Gas Field.

scholarly journals Predicting Loan Approval of Bank Direct Marketing Data Using Ensemble Machine Learning Algorithms

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Ensemble Machine Learning ◽  
Marketing Data ◽  
Loan Approval

The Bank Marketing data set at Kaggle is mostly used in predicting if bank clients will subscribe a long-term deposit. We believe that this data set could provide more useful information such as predicting whether a bank client could be approved for a loan. This is a critical choice that has to be made by decision makers at the bank. Building a prediction model for such high-stakes decision does not only require high model prediction accuracy, but also needs a reasonable prediction interpretation. In this research, different ensemble machine learning techniques have been deployed such as Bagging and Boosting. Our research results showed that the loan approval prediction model has an accuracy of 83.97%, which is approximately 25% better than most state-of-the-art other loan prediction models found in the literature. As well, the model interpretation efforts done in this research was able to explain a few critical cases that the bank decision makers may encounter; therefore, the high accuracy of the designed models was accompanied with a trust in prediction. We believe that the achieved model accuracy accompanied with the provided interpretation information are vitally needed for decision makers to understand how to maintain balance between security and reliability of their financial lending system, while providing fair credit opportunities to their clients.

scholarly journals Prediction of Cardiac Arrest in the Emergency Department Based on Machine Learning and Sequential Characteristics: Model Development and Retrospective Clinical Validation Study (Preprint)

2019 ◽  
Author(s):  
Sungjun Hong ◽  
Sungjoo Lee ◽  
Jeonghoon Lee ◽  
Won Chul Cha ◽  
Kyunga Kim
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Cardiac Arrest ◽  
Prediction Model ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Clinical Usefulness ◽  
Class Imbalance Problem ◽  
Data Set

BACKGROUND The development and application of clinical prediction models using machine learning in clinical decision support systems is attracting increasing attention. OBJECTIVE The aims of this study were to develop a prediction model for cardiac arrest in the emergency department (ED) using machine learning and sequential characteristics and to validate its clinical usefulness. METHODS This retrospective study was conducted with ED patients at a tertiary academic hospital who suffered cardiac arrest. To resolve the class imbalance problem, sampling was performed using propensity score matching. The data set was chronologically allocated to a development cohort (years 2013 to 2016) and a validation cohort (year 2017). We trained three machine learning algorithms with repeated 10-fold cross-validation. RESULTS The main performance parameters were the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). The random forest algorithm (AUROC 0.97; AUPRC 0.86) outperformed the recurrent neural network (AUROC 0.95; AUPRC 0.82) and the logistic regression algorithm (AUROC 0.92; AUPRC=0.72). The performance of the model was maintained over time, with the AUROC remaining at least 80% across the monitored time points during the 24 hours before event occurrence. CONCLUSIONS We developed a prediction model of cardiac arrest in the ED using machine learning and sequential characteristics. The model was validated for clinical usefulness by chronological visualization focused on clinical usability.

scholarly journals Prediction of Cardiac Arrest in the Emergency Department Based on Machine Learning and Sequential Characteristics: Model Development and Retrospective Clinical Validation Study

10.2196/15932 ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. e15932
Author(s):  
Sungjun Hong ◽  
Sungjoo Lee ◽  
Jeonghoon Lee ◽  
Won Chul Cha ◽  
Kyunga Kim
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Cardiac Arrest ◽  
Prediction Model ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Clinical Usefulness ◽  
Class Imbalance Problem ◽  
Data Set

Background The development and application of clinical prediction models using machine learning in clinical decision support systems is attracting increasing attention. Objective The aims of this study were to develop a prediction model for cardiac arrest in the emergency department (ED) using machine learning and sequential characteristics and to validate its clinical usefulness. Methods This retrospective study was conducted with ED patients at a tertiary academic hospital who suffered cardiac arrest. To resolve the class imbalance problem, sampling was performed using propensity score matching. The data set was chronologically allocated to a development cohort (years 2013 to 2016) and a validation cohort (year 2017). We trained three machine learning algorithms with repeated 10-fold cross-validation. Results The main performance parameters were the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). The random forest algorithm (AUROC 0.97; AUPRC 0.86) outperformed the recurrent neural network (AUROC 0.95; AUPRC 0.82) and the logistic regression algorithm (AUROC 0.92; AUPRC=0.72). The performance of the model was maintained over time, with the AUROC remaining at least 80% across the monitored time points during the 24 hours before event occurrence. Conclusions We developed a prediction model of cardiac arrest in the ED using machine learning and sequential characteristics. The model was validated for clinical usefulness by chronological visualization focused on clinical usability.

scholarly journals Malaria Prediction Model Using Machine Learning Algorithms

2021 ◽  
Vol 12 (10) ◽  
pp. 7488-7496
Author(s):  
Yusuf Aliyu Adamu, Et. al.
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Prediction Model ◽  
Public Awareness ◽  
Health Sector ◽  
Weather Condition ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
African Countries ◽  
Data Set

Measures have been taking to ensure the safety of individuals from the burden of vector-borne disease but it remains the causative agent of death than any other diseases in Africa. Many human lives are lost particularly of children below five years regardless of the efforts made. The effect of malaria is much more challenging mostly in developing countries. In 2019, 51% of malaria fatality happen in Africa which it increased by 20% in 2020 due to the covid-19 pandemic. The majority of African countries lack a proper or a sound health care system, proper environmental settlement, economic hardship, limited funding in the health sector, and absence of good policies to ensure the safety of individuals. Information has to become available to the peoples on the effect of malaria by making public awareness program to make sure people become acquainted with the disease so that certain measure can be maintained. The prediction model can help the policymakers to know more about the expected time of the malaria occurrence based on the existing features so that people will get to know the information regarding the disease on time, health equipment and medication to be made available by government through it policy. In this research weather condition, non-climatic features, and malaria cases are considered in designing the model for prediction purposes and also the performance of six different machine learning classifiers for instance Support Vector Machine, K-Nearest Neighbour, Random Forest, Decision Tree, Logistic Regression, and Naïve Bayes is identified and found that Random Forest is the best with accuracy (97.72%), AUC (98%) AUC, and (100%) precision based on the data set used in the analysis.  

scholarly journals Holocene Sedimentary Environment in HP1 Core at Duong Kinh, Haiphong

2020 ◽  
Vol 36 (2) ◽  
Author(s):  
Nguyen Thi Thu Cuc ◽  
Nguyen Thuy Duong ◽  
Nguyen Thi Minh Phuong ◽  
Doan Dinh Lam ◽  
Vu Van Loi ◽  
...  
Keyword(s):  
Grain Size ◽  
Coastal Area ◽  
Sedimentary Environment ◽  
Grain Size Analysis ◽  
Size Analysis ◽  
Diatom Species ◽  
Delta Front ◽  
Environment Change ◽  
Delta Plain

Holocene environment change in Hai Phong coastal area was reconstructed based on diatom and grain-size analysis in the HP1 core at Duong Kinh, Hai Phong. 52 diatom species were identified and divided in five diatom ecozones by changing of four diatom groups including marine planktonic, brackish planktonic, brackish benthic and freshwater one. The sedimentary environment at the Hai Phong coastal area was estuary- bay condition in the Flandrian trangression (Z1, Z2 and Z3 Unit). Deltaic environment changed from prodelta (Z4), delta front (Z5) to delta plain (Z6 and Z7) corresponding to the Flandrian regression.

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