Emulation of Dynamic Process-Based Agroecosystem Models Using Long Short-Term Memory Networks

Modeling carbon balance in agroecosystems help monitoring changes in carbon emissions and influence in ecosystem functioning and productivity. Process-based models are widely used in modeling diverse agroecosystems, and also enable quantification of carbon balance in agroecosystems. However, process-based models tend to be very computationally demanding, due to their complex computations based on hypotheses and assumption of the dynamics of the system. The computational demands complicate performing large scale simulations, needed when simulating several different parameter scenarios, such as model calibration and sensitivity analysis. In order to mitigate the computational burden of large scale simulations, a surrogate model utilizing neural networks is developed to emulate the behavior of a process-based land model BASGRA\_N, obtaining a fast execution time. The emulator recognizes sequentially dependent data by networks specifically designed for sequential learning. Additionally, it is applicable to other similar agroecosystem models. The model is evaluated by 5-fold cross validation, achieving RMSEs of 0.0290 (g C m^(-2) h^(-1)) and 0.322 (m^2 m^(-2)) for weekly mean values of hourly NPP and LAI, respectively. Each of the 5 folds give R^2 of >0.91 for NPP and >0.93 for LAI. The thesis begins with basic concepts on neural networks, concerning to regression tasks, covering a fundamental neural network model, its architecture, features, and general training methods. Subsequently, the study continues to sequential modeling and introduces neural networks designed for processing sequentially structured data. Subsequently, an overall review on existing research on machine learning applications, especially in emulation of process-based models, is provided. Lastly a novel emulator model applying neural networks is introduced for emulation of an agroecosystem model. This project was done in collaboration with Carbon Cycle group of Finnish Meteorological Institute, for their requirement for an emulator for a process-based agroecosystem model BASGRA_N to enable large scale simulations for simulator calibration purposes

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table