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In coastal river systems, floods, often during major storms or king tides, severely threaten lives and property. However, hydraulic structures such as dams, gates, pumps, and reservoirs exist in these river systems, and these floods can be mitigated or even prevented by strategically releasing water before extreme weather events. A standard approach used by local water management agencies is the “rule-based” method, which specifies predetermined water prereleases based on historical human experience, but which tends to result in excessive or inadequate water release. Iterative optimization methods that rely on detailed physics-based models for prediction are an alternative approach. Whereas, such methods tend to be computationally intensive, requiring hours or even days to solve the problem optimally. In this paper, we propose a Forecast Informed Deep Learning Architecture, FIDLAR, to achieve rapid and near-optimal flood management with precise water prereleases. FIDLAR seamlessly integrates two neural network modules: one called the Flood Manager, which is responsible for generating water pre-release schedules, and another called the Flood Evaluator, which evaluates those generated schedules. The Evaluator module is pre-trained separately, and its gradient-based feedback is utilized to train the Manager model, ensuring near-optimal water pre-releases. We have conducted experiments with a flood-prone coastal area in South Florida. Results show that FIDLAR is several orders of magnitude faster than currently used physics-based approaches while outperforming baseline methods with improved water pre-release schedules. 

Abstract Miami‐Dade County (MDC) has over 112,000 septic systems, some of which are at risk of compromise due to water table rise associated with sea level rise. MDC is surrounded by protected water bodies, including Biscayne Bay, with environmentally sensitive ecosystems and is underlain by highly transmissive karstic limestone. The main objective of the study is to provide first estimates of the locations and magnitudes of septic return flows to discharge endpoints. This is accomplished by leveraging MDC's county‐scale surface‐groundwater model using pathline analysis to estimate the transport and discharge fate of septic system flows under the complex time history of groundwater flow response to pumping, canal management, storms, and other environmental factors. The model covers an area of 4772 km²in Southeast Florida. Outputs from the model were used to create a 30‐year (2010 to 2040) simulation of the spatial–temporal pathlines from septic input locations to their termination points, allowing us to map flow paths and the spatial distribution of the septic flow discharge endpoints under the simulated conditions. Most septic return flows were discharged to surface water, primarily canals 52,830 m³/d and Biscayne Bay (5696 m³/d), and well fields (14,066 m³/d). Results allow us to identify “hotspots” to guide water quality sampling efforts and to provide recommendations for septic‐to‐sewer conversion areas that should provide most benefit by reducing nutrient loading to water bodies. 

Explaining deep learning models operating on time series data is crucial in various applications of interest which require interpretable and transparent insights from time series signals. In this work, we investigate this problem from an information theoretic perspective and show that most existing measures of explainability may suffer from trivial solutions and distributional shift issues. To address these issues, we introduce a simple yet practical objective function for time series explainable learning. The design of the objective function builds upon the principle of information bottleneck (IB), and modifies the IB objective function to avoid trivial solutions and distributional shift issues. We further present TimeX++, a novel explanation framework that leverages a parametric network to produce explanation-embedded instances that are both in-distributed and label-preserving. We evaluate TimeX++ on both synthetic and real-world datasets comparing its performance against leading baselines, and validate its practical efficacy through case studies in a real-world environmental application. Quantitative and qualitative evaluations show that TimeX++ outperforms baselines across all datasets, demonstrating a substantial improvement in explanation quality for time series data. 

Abstract. Miami-Dade County (south-east Florida) is among the most vulnerable regions to sea level rise in the United States, due to a variety of natural andhuman factors. The co-occurrence of multiple, often statistically dependent flooding drivers – termed compound events – typically exacerbatesimpacts compared with their isolated occurrence. Ignoring dependencies between the drivers will potentially lead to underestimation of flood riskand under-design of flood defence structures. In Miami-Dade County water control structures were designed assuming full dependence between rainfalland Ocean-side Water Level (O-sWL), a conservative assumption inducing large safety factors. Here, an analysis of the dependence between theprincipal flooding drivers over a range of lags at three locations across the county is carried out. A two-dimensional analysis of rainfall andO-sWL showed that the magnitude of the conservative assumption in the original design is highly sensitive to the regional sea level rise projectionconsidered. Finally, the vine copula and Heffernan and Tawn (2004) models are shown to outperform five standard higher-dimensional copulas incapturing the dependence between the principal drivers of compound flooding: rainfall, O-sWL, and groundwater level. The work represents a firststep towards the development of a new framework capable of capturing dependencies between different flood drivers that could potentially beincorporated into future Flood Protection Level of Service (FPLOS) assessments for coastal water control structures.