The current paper presents a novel method for reducing a multiclass confusion matrix into a $2\times 2$ version enabling the exploitation of the relevant performance metrics and methods such as the receiver operating characteristic and area under the curve for the assessment of different classification algorithms. The reduction method is based on class grouping and leads to a special type of matrix called the reduced confusion matrix. The developed method is then exploited for the assessment of state of the art machine learning algorithms applied on the net promoter score classification problem in the field of customer experience analytics indicating the value of the proposed method in real world classification problems.

In this work we investigate the performance of various lower-fidelity models of seawater intrusion in coastal aquifer management problems. The variable density model is considered as the high-fidelity model and a pumping optimization framework is applied on a hypothetical coastal aquifer system in order to calculate the optimal pumping rates which are used as a benchmark for the lower-fidelity approaches. The examined lower-fidelity models could be classified in two categories: (1) physics-based models, which include several widely used variations of the sharp-interface approximation and (2) machine learning assisted models, which aim to improve the efficiency of the SI approach. The Random Forest method was utilized to create a spatially adaptive correction factor for the original sharp-interface model, which improves its accuracy without compromising its efficiency as a lower-fidelity model. Both the original sharp-interface and Machine Learning assisted model are then tested in a single-fidelity optimization method. The optimal pumping rated which were calculated using the Machine Learning based SI model sufficiently approximate the solution from the variable density model. The Machine Learning assisted approximation seems to be a promising surrogate for the high-fidelity, variable density model and could be utilized in multi-fidelity groundwater management frameworks.

Floods are among the most prevalent and devastating natural disasters, affecting over 2 billion people worldwide. The increasing frequency and intensity of extreme precipitation events, driven by climate change, increase the vulnerability of communities to flooding. This underscores the urgent need for more accurate and reliable flood prediction and risk assessment models to enhance preparedness and mitigation efforts. In this research, we investigate the critical role of Digital Elevation Model (DEM) resolution in fluvial flood modeling. DEMs are essential for representing the Earth’s surface and are necessary in hydrodynamic simulations used to predict flood behavior. We employed a two-dimensional hydrodynamic model to simulate flood scenarios using DEMs of three different resolutions: 1 m, 5 m, and 25 m. The objective was to determine how variations in DEM resolution influence the accuracy of flood extent predictions. Our findings reveal that higher resolution DEMs, such as those with 1-meter resolution, provide a more precise and detailed representation of the terrain. This level of detail significantly reduces the predicted extent of flooding compared to lower resolution DEMs (5m and 25 m).