Machine Learning Application to Atmospheric Chemistry Modeling

Atmospheric chemistry is a high-dimensionality, large-data problem and thus may be suited to machine-learning algorithms. We show here the potential of a random forest regression algorithm to replace the gas-phase chemistry solver in the GEOS-Chem chemistry model. In this proof-of-concept study, we used one month of model output to train random forest regression models to predict the concentrations of each long-lived chemical species after integration based upon the physical and chemical conditions before the chemical integration. The choice of prediction type has a strong impact on the skill of the regression model. We find best results from predicting the change in concentration for very long-lived species and the absolute concentration for shorter lived species. The skill of the machine learning algorithm is further improved by using a family approach for NO and NO2 rather than treating them independently.By replacing the numerical integrator with the random forest algorithm and running this model for one month, we find that the model is able to reproduce many of the features of the reference chemistry simulation. Replacing the integration methodology with a machine learning algorithm has the potential to be substantially faster. There are a wide range of applications for such an approach, e.g. to generate boundary conditions, for use in air quality forecasts or chemical data assimilation systems, etc

Machine Learning Application to Atmospheric Chemistry Modeling

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O (sup 1) D (Deuterium) to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it

The Best You: Gym Based Machine Learning Application

Use of artificial intelligence (AI) and machine learning is rapidly becoming more widespread in the 21st century. Both are quickly emerging increasingly vital aspects of today's standard exercise routines. Artificial intelligence has become inextricably linked to health and fitness. Experts in the field of technology believe that AI will solve all problems. When it comes to fitness, it has the ability to empower the app by drastically increasing engagement, which may lead to long-term income. In other words, it has the potential to make money. Apps that are equipped with AI have the potential to provide consumers a wide range of benefits. It is feasible for a person who is interested in fitness to save money because an artificial intelligence fitness trainer is more cost-effective than a human trainer. On the other hand, joining a gym may be cost prohibitive or just not doable given our hectic schedules. Aside from that, using fitness software that is powered by AI might make working out more fascinating and fun. In this section, we will discuss some of the best fitness applications that are powered by AI and machine learning models. This app creates unique training plans for each user using artificial intelligence This app was originally designed exclusively for use in gyms, but it recently changed its focus to meet the rising demand for at-home exercise. Simply put, FitnessAI pushes users to effectively build muscle every time they exercise by optimizing their weight lifting sets, repetitions, and weights for each activity. The main purpose of proposing this application system is to provide gym-goers with the right information at the right time, preventing them from taking the wrong supplements to maintain their body well