Scenario Planning for Sea Level Rise via Reinforcement Learning Climate change and sea level rise impacts will affect coastal communities with multiple threats, including increased frequency of compound events, such as storm surge combined with heavy precipitation. Accurately modeling how the stakeholders, such as governments and residents, may respond to sea level rise scenarios (i.e., scenario planning) can assist in the creation of policies tailored to local impacts and resilience strategies. In this paper, our contributions are twofold. Firstly, considering a singleagent model for government, we numerically show that the governmentxe2x80x99s policy on infrastructure improvement should be based on the observed sea levels rather than the observed cost from nature. The latter refers to the straightforward policy that any responsive (but not proactive) government would follow. Through a reinforcement learning algorithm based on a Markov decision process model we show that the precautionary measures, (i.e., infrastructure improvements triggered by the sea levels) are more effective in decreasing the expected cost than the aftermath measures triggered by the cost from nature. Secondly, to generate different scenarios we consider several sea level rise projections by NOAA, and model different government and resident prototypes using cooperation indices in terms of being responsive to the sea level rise problem. We present a reinforcement learning algorithm to generate simulations for a set of scenarios defined by the NOAA projections and cooperation indices.

Hierarchical LSTM Framework for LongTerm Sea Surface Temperature Forecasting Multistep prediction of sea surface temperature (SST) is a challenging problem because small errors in its shortrange forecasts can be compounded to create large errors at longer ranges. In this paper, we propose a hierarchical LSTM framework to improve the accuracy for longterm SST prediction. Our framework alleviates the error accumulation problem in multistep prediction by leveraging outputs from an ensemble of physicallybased dynamical models. Unlike previous methods, which simply take a linear combination of the outputs to produce a single deterministic forecast, our framework learns a nonlinear relationship among the ensemble member forecasts. In addition, its multilevel structure is designed to capture the temporal autocorrelation between forecasts generated for the same lead time as well as those generated for different lead times. Experiments performed using SST data from the tropical Pacific ocean region show that the proposed framework outperforms various baseline methods in more than 70% of the grid cells located in the study region.