Determination of the most important meteorological parameters affecting the yield and biomass of barley and winter wheat using the random forest algorithm

2020 ◽  
Author(s):  
Abdol Rassoul Zarei ◽  
Mohammad Reza Mahmoudi ◽  
Ali Shabani ◽  
Mohammed Achite
Keyword(s):  
Random Forest ◽  
Winter Wheat ◽  
Meteorological Parameters ◽  
Random Forest Algorithm

Robust well-log based determination of rock thermal conductivity through machine learning

2020 ◽  
Vol 222 (2) ◽  
pp. 978-988
Author(s):  
Yury Meshalkin ◽  
Anuar Shakirov ◽  
Evgeniy Popov ◽  
Dmitry Koroteev ◽  
Irina Gurbatova
Keyword(s):  
Machine Learning ◽  
Thermal Conductivity ◽  
Random Forest ◽  
Well Logging ◽  
Machine Learning Algorithms ◽  
System Modelling ◽  
Well Log ◽  
Random Forest Algorithm ◽  
Rock Thermal Conductivity

SUMMARY Rock thermal conductivity is an essential input parameter for enhanced oil recovery methods design and optimization and for basin and petroleum system modelling. Absence of any effective technique for direct in situ measurements of rock thermal conductivity makes the development of well-log based methods for rock thermal conductivity determination highly desirable. A major part of the existing problem solutions is regression model-based approaches. Literature review revealed that there are only several studies performed to assess the applicability of neural network-based algorithms to predict rock thermal conductivity from well-logging data. In this research, we aim to define the most effective machine-learning algorithms for well-log based determination of rock thermal conductivity. Well-logging data acquired at a heavy oil reservoir together with results of thermal logging on cores extracted from two wells were the basis for our research. Eight different regression models were developed and tested to predict vertical variations of rock conductivity from well-logging data. Additionally, rock thermal conductivity was determined based on Lichtenecker–Asaad model. Comparison study of regression-based and theoretical-based approaches was performed. Among considered machine learning techniques Random Forest algorithm was found to be the most accurate at well-log based determination of rock thermal conductivity. From a comparison of the thermal conductivity—depth profile predicted from well-logging data with the experimental data, and it can be concluded that thermal conductivity can be determined with a total relative error of 12.54 per cent. The obtained results prove that rock thermal conductivity can be inferred from well-logging data for wells that are drilled in a similar geological setting based on the Random Forest algorithm with an accuracy sufficient for industrial needs.

scholarly journals Feature Comparison and Optimization for 30-M Winter Wheat Mapping Based on Landsat-8 and Sentinel-2 Data Using Random Forest Algorithm

2019 ◽  
Vol 11 (5) ◽  
pp. 535 ◽  
Author(s):  
Yuanhuizi He ◽  
Changlin Wang ◽  
Fang Chen ◽  
Huicong Jia ◽  
Dong Liang ◽  
...  
Keyword(s):  
Random Forest ◽  
Winter Wheat ◽  
High Precision ◽  
Agricultural Land ◽  
Global Climate ◽  
Machine Learning Algorithms ◽  
Landsat 8 ◽  
Spectral Indices ◽  
Random Forest Algorithm ◽  
Sentinel 2

Winter wheat cropland is one of the most important agricultural land-cover types affected by the global climate and human activity. Mapping 30-m winter wheat cropland can provide beneficial reference information that is necessary for understanding food security. To date, machine learning algorithms have become an effective tool for the rapid identification of winter wheat at regional scales. Algorithm implementation is based on constructing and selecting many features, which makes feature set optimization an important issue worthy of discussion. In this study, the accurate mapping of winter wheat at 30-m resolution was realized using Landsat-8 Operational Land Imager (OLI), Sentinel-2 Multispectral Imager (MSI) data, and a random forest algorithm. This paper also discusses the optimal combination of features suitable for cropland extraction. The results revealed that: (1) the random forest algorithm provided robust performance using multi-features (MFs), multi-feature subsets (MFSs), and multi-patterns (MPs) as input parameters. Moreover, the highest accuracy (94%) for winter wheat extraction occurred in three zones, including: pure farmland, urban mixed areas, and forest areas. (2) Spectral reflectance and the crop growth period were the most essential features for crop extraction. The MFSs combined with the three to four feature types enabled the high-precision extraction of 30-m winter wheat plots. (3) The extraction accuracy of winter wheat in three zones with multiple geographical environments was affected by certain dominant features, including spectral bands (B), spectral indices (S), and time-phase characteristics (D). Therefore, we can improve the winter wheat mapping accuracy of the three regional types by improving the spectral resolution, constructing effective spectral indices, and enriching vegetation information. The results of this paper can help effectively construct feature sets using the random forest algorithm, thus simplifying the feature construction workload and ensuring high-precision extraction results in future winter wheat mapping research.

scholarly journals Sarcasm Detection For Sentiment Analysis in Indonesian Tweets

2019 ◽  
Vol 13 (1) ◽  
pp. 53 ◽  
Author(s):  
Yessi Yunitasari ◽  
Aina Musdholifah ◽  
Anny Kartika Sari
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Random Forest Algorithm ◽  
Feature Sets ◽  
Syntactic Features ◽  
The Social ◽  
Bayes Algorithm ◽  
The Moment

Twitter is one of the social medias that are widely used at the moment. Tweet conversations can be classified according to their sentiments. The existence of sarcasm contained in a tweet sometimes causes incorrect determination of the tweet’s sentiment because sarcasm is difficult to analyze automatically, even by humans. Hence, sarcasm detection needs to be conducted, which is expected to improve the results of sentiment analysis. The effect of sarcasm detection on sentiment analysis can be seen in terms of accuracy, precision and recall. In this paper, detection of sarcasm is applied to Indonesian tweets. The feature extraction of sarcasm detection uses unigram and 4 Boazizi feature sets which consist of sentiment-relate features, punctuation-relate features, lexical and syntactic features, and top word features. Detection of sarcasm uses the Random Forest algorithm. The feature extraction of sentiment analysis uses TF-IDF, while the classification uses Naïve Bayes algorithm. The evaluation shows that sentiment analysis with sarcasm detection improves the  accuracy of sentiment analysis about 5.49%. The accuracy of the model is 80.4%, while the precision is 83.2%, and the recall is 91.3%.

scholarly journals Random Forest Algorithm to Investigate the Case of Acute Coronary Syndrome

2021 ◽  
Vol 5 (2) ◽  
pp. 369-378
Author(s):  
Eka Pandu Cynthia ◽  
M. Afif Rizky A. ◽  
Alwis Nazir ◽  
Fadhilah Syafria
Keyword(s):  
Machine Learning ◽  
Acute Coronary Syndrome ◽  
Random Forest ◽  
Data Science ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Random Forest Algorithm ◽  
Coronary Syndrome ◽  
Use Of Data

This paper explains the use of the Random Forest Algorithm to investigate the Case of Acute Coronary Syndrome (ACS). The objectives of this study are to review the evaluation of the use of data science techniques and machine learning algorithms in creating a model that can classify whether or not cases of acute coronary syndrome occur. The research method used in this study refers to the IBM Foundational Methodology for Data Science, include: i) inventorying dataset about ACS, ii) preprocessing for the data into four sub-processes, i.e. requirements, collection, understanding, and preparation, iii) determination of RFA, i.e. the "n" of the tree which will form a forest and forming trees from the random forest that has been created, and iv) determination of the model evaluation and result in analysis based on Python programming language. Based on the experiments that the learning have been conducted using a random forest machine-learning algorithm with an n-estimator value of 100 and each tree's depth (max depth) with a value of 4, learning scenarios of 70:30, 80:20, and 90:10 on 444 cases of acute coronary syndrome data. The results show that the 70:30 scenario model has the best results, with an accuracy value of 83.45%, a precision value of 85%, and a recall value of 92.4%. Conclusions obtained from the experiment results were evaluated with various statistical metrics (accuracy, precision, and recall) in each learning scenario on 444 cases of acute coronary syndrome data with a cross-validation value of 10 fold.

Influence of a chemical preparation of triazole50% cs on sowing the winter wheat against rust

2020 ◽  
Vol 3 (9) ◽  
pp. 231-233
Author(s):  
AliyevSh.K. ◽  
TuychiyevI.U ◽  
Karimov N ◽  
Umaraliev.M.I
Keyword(s):  
Winter Wheat ◽  
Field Experiments ◽  
Yellow Rust ◽  
Biological Efficiency ◽  
Chemical Preparation ◽  
Seed Growth ◽  
Biological Farming ◽  
The Given ◽  
Farming Efficiency

The article is focused on the data of the carried works on studying biological efficiency of fungi Triazole 50% on sowing the winter wheat against yellow rust as well as on the height, development and fertility of the wheat. On May 5, 2019 from 9 to 10 o’clock under the temperature 21-23 field experiments of Triazol 50% CS manufactured by the firm “Agroximstar” (Uzbekistan) were carried out on winter wheat as a protector of seeds of winter wheat of Pervitsa sort against the disease of yellow rustin the irrigated conditions in an experimental field of the Institute “Istiklal” of Andijan district of Andijan region. The aim of the given research is to study biological-farming efficiency and determination of optimal norms of preparation expenses and to study the influence of fungicide on the height and development as well as on the fertility of the wheat. The received data showed that the preparation Triazole 50% CS effected on the pathogen of yellow rust favorably and besides that it didn’t effect on seed growth and energy of growth negatively.

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