Effective water resources management depends on monitoring the volume of water flowing through streams and rivers, but collecting continuous discharge measurements using traditional streamflow gages is prohibitively expensive. Time-lapse cameras offer a low-cost option for streamflow monitoring, but training models for predicting streamflow directly from images requires streamflow data to use as labels, which are often unavailable. We address this data gap by proposing the alternative task of Streamflow Rank Estimation (SRE), in which the goal is to predict relative measures of streamflow such as percentile rank rather than absolute flow. In particular, we use a learning-to-rank framework to train SRE models using pairs of stream images ranked in order of discharge by an annotator, obviating the need for discharge training data and thus facilitating monitoring streamflow conditions at streams without gages. We also demonstrate a technique for converting SRE model predictions to stream discharge estimates given an estimated streamflow distribution. Using data and images from six small US streams, we compare the performance of SRE with conventional regression models trained to predict absolute discharge. Our results show that SRE performs nearly as well as regression models on relative flow prediction. Further, we observe that the accuracy of absolute discharge estimates obtained by mapping SRE model predictions through a discharge distribution largely depends on how well the assumed discharge distribution matches the field observed data.

Our work is motivated by an important network design problem in climate adaptation. As floods become more frequent and severe due to climate change, it is increasingly crucial that road infrastructure be strategically upgraded to support post-disaster recovery efforts and normal functionality. We focus on the problem of allocating a fixed budget towards restoring edges to maximize the satisfied travel demand between locations in a network, which we formalize as the budget-constrained prize-collecting Steiner forest problem. We prove that the satisfiable travel demand objective exhibits restricted supermodularity over forests, and utilize this property to design an iterative algorithm based on maximizing successive modular lower bounds for the objective that finds better solutions than a baseline greedy approach. We also propose an extremely fast heuristic for maximizing modular functions subject to knapsack and graph matroid constraints that can be used as a subroutine in the iterative algorithm, or as a standalone method that matches the greedy baseline in terms of quality but is orders of magnitude faster. We evaluate the algorithms on synthetic data, and apply them to a real-world instance of retrofitting the Senegal national road network against flooding.

Developing and maintaining resilient transportation infrastructure is a key strategy for meeting several UN sustainable development goals in the face of climate change-driven extreme flooding events. We present a framework for performing data-driven vulnerability analysis for flooding on existing transportation networks, and use this analysis to inform decision-making about investments for climate adaptation. We apply this approach to study the potential impacts of severe flooding on regional mobility in Senegal, using a combination of flood hazard maps and a travel demand model based on call detail record data. We use the estimated number of infeasible trips as a direct measure of flooding-induced mobility impacts, as well as an objective for minimizing these impacts. We then compare three alternative road network upgrade strategies to assess the extent to which each strategy would preserve network functionality under a given flooding scenario. We illustrate that strategies driven solely by travel demand can lead to underinvestment in roads that are at risk of flooding, while solely focusing on repairing flooded road segments neglects the criticality of those repairs to mobility. For example, in a 100 year flooding scenario with a fixed budget, our strategy that considers both flooding and mobility data can achieve a 53% reduction in the number of infeasible trips, while a strategy that just considers flooding data achieves only a 38% reduction for the same cost. Our framework can be applied more broadly to integrate information from a variety of sources about climate hazards and potential human impacts to make better informed decisions about investments in critical infrastructure systems.

The spread of invasive species to new areas threatens the stability of ecosystems and causes major economic losses. We propose a novel approach to minimize the spread of an invasive species given a limited intervention budget. We first model invasive species spread using Hawkes processes, and then derive closed-form expressions for characterizing the effect of an intervention action on the invasion process. We use this to obtain an optimal intervention plan based on an integer programming formulation, and compare the optimal plan against several ecologically-motivated heuristic strategies used in practice. We present an empirical study of two variants of the invasive control problem: minimizing the final rate of invasions, and minimizing the number of invasions at the end of a given time horizon. The optimized intervention achieves nearly the same level of control that would be attained by completely eradicating the species, but at only 60-80\% of the cost.