An ensemble learning framework for long-term flood forecasting

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Given flood risks, forecasting river flood is important for water resource management and risk preven-tion.Even though long-term flood forecasting is a difficult task, log-term flood forecasting is very useful forinstance to let municipalities to have enough time for preparation and action. The efforts in ultimately creatinga long-term forecasting framework are usually faced with the challenges stemming from weather dynamics.Machine learning techniques have recently been recognized and widely adopted for modeling complexproblems in sustainable infrastructures, especially in forecasting extreme events. In particular, machinelearning techniques have been successfully applied for flood forecasting and provided improved forecastingtechniques and relatively more accurate results. More recently, Ensemble learning has re-ceived a significantamount of interest. Ensemble learning provides a more stable prediction performance compared to singlemodel, driving the diminishing uncertainty behaviour of ensemble learning. The main goal of this researchproject is to develop ensemble based machine learning (EML) models for the long-term forecasting of riverflow under different information criterion and limited history of extreme events. The new, highly accurate andreliable long-term forecasting models will provide US, and in turn their clients across Canada, with very usefulmodels that will allow significantly improved long-term forecast-ing and will consequently help in effectivelyand sustainably plan and manage extreme events response strategies.