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Climate change affects patterns and uncertainties associated with river water regimes, which significantly impact hydropower generation and reservoir storage operation. Hence, reliable and accurate short-term inflow forecasting is vital to face climate effects better and improve hydropower scheduling performance. This paper proposes a Causal Variational Mode Decomposition (CVD) preprocessing framework for the inflow forecasting problem. CVD is a preprocessing feature selection framework that is built upon multiresolution analysis and causal inference. CVD can reduce computation time while increasing forecasting accuracy by down-selecting the most relevant features to the target value (inflow in a specific location). Moreover, the proposed CVD framework is a complementary step to any machine learning-based forecasting method as it is tested with four different forecasting algorithms in this paper. CVD is validated using actual data from a river system downstream of a hydropower reservoir in the southwest of Norway. The experimental results show that CVD-LSTM reduces forecasting error metric by almost 70% compared with a baseline (scenario 1) and reduces by 25% compared to an LSTM for the same composition of input data (scenario 4). [ABSTRACT FROM AUTHOR]

Mass-screening programs for cervical cancer prevention in the Nordic countries have been effective in reducing cancer incidence and mortality at the population level. Women who have been regularly diagnosed with normal screening exams represent a sub-population with a low risk of disease and distinctive screening strategies which avoid over-screening while identifying those with high-grade lesions are needed to improve the existing one-size-fits-all approach. Machine learning methods for more personalized cervical cancer risk estimation may be of great utility to screening programs shifting to more targeted screening. However, deriving personalized risk prediction models is challenging as effective screening has made cervical cancer rare and the exam results are strongly skewed towards normal. Moreover, changes in female lifestyle and screening habits over time can cause a non-stationary data distribution. In this paper, we treat cervical cancer risk prediction as a longitudinal forecasting problem. We define risk estimators by extending existing frameworks developed on cervical cancer screening data to incremental learning for longitudinal risk predictions and compare these estimators to machine learning methods popular in biomedical applications. As input to the prediction models, we utilize all the available data from the individual screening histories.Using data from the Cancer Registry of Norway, we find in numerical experiments that the models are strongly biased towards normal results due to imbalanced data. To identify females at risk of cancer development, we adapt an imbalanced classification strategy to non-stationary data. Using this strategy, we estimate the absolute risk from longitudinal model predictions and a hold-out set of screening data. Comparing absolute risk curves demonstrate that prediction models can closely reflect the absolute risk observed in the hold-out set. Such models have great potential for improving cervical cancer risk stratification for more personalized screening recommendations. [ABSTRACT FROM AUTHOR]