Wave overtopping is an important design criterion for coastal structures such as dikes, breakwaters and promenades. Hence, the prediction of the expected wave overtopping discharge is an important research topic. Existing prediction tools consist of empirical overtopping formulae, machine learning techniques like neural networks, and numerical models. In this paper, an innovative machine learning method—gradient boosting decision trees—is applied to the prediction of mean wave overtopping discharges. This new machine learning model is trained using the CLASH wave overtopping database. Optimizations to its performance are realized by using feature engineering and hyperparameter tuning. The model is shown to outperform an existing neural network model by reducing the error on the prediction of the CLASH database by a factor of 2.8. The model predictions follow physically realistic trends for variations of important features, and behave regularly in regions of the input parameter space with little or no data coverage.
In this study, basic interpolation and machine learning data augmentation were applied to scarce data used in Water Quality Analysis Simulation Programme (WASP) and Continuous Stirred Tank Reactor (CSTR) that were applied to nitrogenous compound degradation modelling in a river reach. Model outputs were assessed for statistically significant differences. Furthermore, artificial data gaps were introduced into the input data to study the limitations of each augmentation method. The Python Data Analysis Library (Pandas) was used to perform the deterministic interpolation. In addition, the effect of missing data at local maxima was investigated. The results showed little statistical difference between deterministic interpolation methods for data augmentation but larger differences when the input data were infilled specifically at locations where extrema occurred.
<p>NASA&#180;s Earth Science Decadal Survey Report highlights mass transport monitoring as one of top priorities in Earth Observation for the next decade. To realize such a Mass Change Mission (MCM), NASA is seeking international partnership. Based on the large success of the GRACE and GRACE-FO missions and their contributions to climate change research, there is a large interest in Germany to continue mass change measurements.</p>
<p>GFZ and the German Space Agency at DLR have suggested a &#8220;GRACE-I&#8221; mission which is based on a GRACE-like concept combined with an optional ICARUS (International Cooperation for Animal Research Using Space) payload. In continuation of a 9 months Phase 0 study in 2021 this concept is currently investigated in Phase A (April &#8211; September 2022) with significant support of JPL/NASA as a future continuation of the very successful US-German GRACE/GRACE-FO technological and scientific partnership.</p>
<p>GRACE-I will be a single satellite pair based on a fully redundant Laser Ranging Interferometer on a polar orbit at 500 km altitude. Launch shall be not later than 2027 to guarantee data continuity w.r.t. GRACE-FO. GRACE-I could be a first component (P1) of a hybrid Bender constellation if combined with an inclined MAGIC pair (P2). The realization of this Mass-change And Geoscience International Constellation is currently discussed between ESA and NASA. P2 will fly on a lower orbit than P1 and will be based on advanced instrumentation. Therefore, Phase A also investigated the option to add one or two adapted MicroStar accelerometers to the baseline GRACE-FO like accelerometer on each P1 satellite as a technology demonstrator for P2.</p>
<p>At the time of writing this abstract the main focus was on the final steps to refine the technical design and to select the final payload configuration for a US/German MCM/GRACE-I mission. We will present the proposed mission architecture and will discuss further steps towards realization of MCM/GRACE-I.</p>
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