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I have completed this specialization from Coursera by deeplearning.ai. I have uploaded the solutions of the assignments in this repo.
Carbon Capture and Sequestration (CCS) has been proposed as a promising and necessary technology for mitigating CO2 and the effects of anthropogenic climate change. Deep geological formations, like saline aquifers, are pointed out as promising areas for large-scale storage of CO2. If CCS is implemented on large scale to make noticeable reductions in atmospheric CO2, then it will require a solid scientific foundation defining the coupled hydrologic–geochemical–geomechanical processes that govern the long-term fate of CO2 in the subsurface, migration behavior of CO2, trapping mechanisms, proper utilization of methods to characterize and select sequestration sites, workflow and evaluation process, simulation methods, subsurface engineering to optimize performance, well placement, injection rate and cost, approaches to ensure safe operation, monitoring technology, remediation methods, regulatory overview, and an institutional approach for managing long-term liability. To address the above issues, we demonstrated, reviewed and developed the overall workflow of the process of CO2 sequestration in this study.
Solutions to all quiz and all the programming assignments!!!
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
Deep Learning Specialization by Andrew Ng on Coursera.
Time series forecasting (TSF) is the task of predicting future values of a given sequence using historical data. Recently, this task has attracted the attention of researchers in the area of machine learning to address the limitations of traditional forecasting methods, which are time-consuming and full of complexity. With the increasing availability of extensive amounts of historical data along with the need of performing accurate production forecasting, particularly a powerful forecasting technique infers the stochastic dependency between past and future values is highly needed. In this research, we applied machine learning approach capable to address the limitations of traditional forecasting approaches and show accurate predictions and showed comparison of different machine learning models. For evaluation purpose, a case study from the petroleum industry domain is carried out using the production data of an actual gas field of Bangladesh. Toward a fair evaluation, the performance of the models were evaluated by measuring the goodness of fit through the coefficient of determination (R2 ) and Root Mean Square Error (RMSE), Mean Squared Error (MSE) , Mean Absolute Error(MAE) and model Accuracy
:triangular_ruler: Jekyll theme for building a personal site, blog, project documentation, or portfolio.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.