Ground-level Ozone Prediction Using Machine Learning Techniques: A Case Study in Amman, Jordan

2020 ◽  
Vol 17 (5) ◽  
pp. 667-677
Author(s):  
Maryam Aljanabi ◽  
Mohammad Shkoukani ◽  
Mohammad Hijjawi
Keyword(s):  
Machine Learning ◽  
Ground Level ◽  
Machine Learning Techniques ◽  
Ground Level Ozone ◽  
Ozone Prediction ◽  
Learning Techniques

Flood-prone area mapping using machine learning techniques: a case study of Quang Binh province, Vietnam

2021 ◽  
Author(s):  
Chinh Luu ◽  
Quynh Duy Bui ◽  
Romulus Costache ◽  
Luan Thanh Nguyen ◽  
Thu Thuy Nguyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Prone Area ◽  
Flood Prone Area

Machine Learning Assisted Interpretation: Integrated Fault Prediction Extraction Case Study from the Groningen Gas Field, Netherlands

2021 ◽  
pp. 1-67
Author(s):  
Stewart Smith ◽  
Olesya Zimina ◽  
Surender Manral ◽  
Michael Nickel
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Seismic Data ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Gas Field ◽  
Seismic Fault ◽  
Learning Techniques ◽  
Groningen Gas Field

Seismic fault detection using machine learning techniques, in particular the convolution neural network (CNN), is becoming a widely accepted practice in the field of seismic interpretation. Machine learning algorithms are trained to mimic the capabilities of an experienced interpreter by recognizing patterns within seismic data and classifying them. Regardless of the method of seismic fault detection, interpretation or extraction of 3D fault representations from edge evidence or fault probability volumes is routine. Extracted fault representations are important to the understanding of the subsurface geology and are a critical input to upstream workflows including structural framework definition, static reservoir and petroleum system modeling, and well planning and de-risking activities. Efforts to automate the detection and extraction of geological features from seismic data have evolved in line with advances in computer algorithms, hardware, and machine learning techniques. We have developed an assisted fault interpretation workflow for seismic fault detection and extraction, demonstrated through a case study from the Groningen gas field of the Upper Permian, Dutch Rotliegend; a heavily faulted, subsalt gas field located onshore, NE Netherlands. Supervised using interpreter-led labeling, we apply a 2D multi-CNN to detect faults within a 3D pre-stack depth migrated seismic dataset. After prediction, we apply a geometric evaluation of predicted faults, using a principal component analysis (PCA) to produce geometric attribute representations (strike azimuth and planarity) of the fault prediction. Strike azimuth and planarity attributes are used to validate and automatically extract consistent 3D fault geometries, providing geological context to the interpreter and input to dependent workflows more efficiently.

scholarly journals Leveraging Business Data Analytics and Machine Learning Techniques for Competitive Advantage: Case Study Evidence from Small Businesses

2021 ◽  
Vol 2 (1) ◽  
pp. 73-87
Author(s):  
Rathimala Kannan ◽  
Intan Soraya Rosdi ◽  
Kannan Ramakrishna ◽  
Haziq Riza Abdul Rasid ◽  
Mohamed Haryz Izzudin Mohamed Rafy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Competitive Advantage ◽  
Small Businesses ◽  
Data Analytics ◽  
Research Question ◽  
Machine Learning Techniques ◽  
Multiple Sources ◽  
Learning Techniques ◽  
Business Data

Data analytics is the essential component in deriving insights from data obtained from multiple sources. It represents the technology, methods and techniques used to obtain insights from massive datasets. As data increases, companies are looking for ways to gain relevant business insights underneath layers of data and information, to help them better understand new business ventures, opportunities, business trends and complex challenges. However, to date, while the extensive benefits of business data analytics to large organizations are widely published, micro, small, and medium sized organisations have not fully grasped the potential benefits to be gained from data analytics using machine learning techniques. This study is guided by the research question of how data analytics using machine learning techniques can benefit small businesses. Using the case study method, this paper outlines how small businesses in two different industries i.e. healthcare and retail can leverage data analytics and machine learning techniques to gain competitive advantage from the data. Details on the respective benefits gained by the small business owners featured in the two case studies provide important answers to the research question.

scholarly journals Machine Learning Generalisation across Different 3D Architectural Heritage

2020 ◽  
Vol 9 (6) ◽  
pp. 379 ◽  
Author(s):  
Eleonora Grilli ◽  
Fabio Remondino
Keyword(s):  
Machine Learning ◽  
Point Cloud ◽  
Machine Learning Techniques ◽  
Training Dataset ◽  
High Complexity ◽  
Architectural Heritage ◽  
Learning Techniques ◽  
Machine Learning Model ◽  
Point Cloud Classification

The use of machine learning techniques for point cloud classification has been investigated extensively in the last decade in the geospatial community, while in the cultural heritage field it has only recently started to be explored. The high complexity and heterogeneity of 3D heritage data, the diversity of the possible scenarios, and the different classification purposes that each case study might present, makes it difficult to realise a large training dataset for learning purposes. An important practical issue that has not been explored yet, is the application of a single machine learning model across large and different architectural datasets. This paper tackles this issue presenting a methodology able to successfully generalise to unseen scenarios a random forest model trained on a specific dataset. This is achieved looking for the best features suitable to identify the classes of interest (e.g., wall, windows, roof and columns).

Urban Water Demand Modeling Using Machine Learning Techniques: Case Study of Fortaleza, Brazil

2021 ◽  
Vol 147 (1) ◽  
pp. 05020026
Author(s):  
Taís Maria Nunes Carvalho ◽  
Francisco de Assis de Souza Filho ◽  
Victor Costa Porto
Keyword(s):  
Machine Learning ◽  
Water Demand ◽  
Machine Learning Techniques ◽  
Urban Water ◽  
Demand Modeling ◽  
Urban Water Demand ◽  
Learning Techniques
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