Data-driven Flood Emulation: Speeding up Urban Flood Predictions by Deep Convolutional Neural Networks

Computational complexity has been the bottleneck of applying physically-based simulations on large urban areas with high spatial resolution for efficient and systematic flooding analyses and risk assessments. To address this issue of long computational time, this paper proposes that the prediction of maximum water depth rasters can be considered as an image-to-image translation problem where the results are generated from input elevation rasters using the information learned from data rather than by conducting simulations, which can significantly accelerate the prediction process. The proposed approach was implemented by a deep convolutional neural network trained on flood simulation data of 18 designed hyetographs on three selected catchments. Multiple tests with both designed and real rainfall events were performed and the results show that the flood predictions by neural network uses only 0.5 % of time comparing with physically-based approaches, with promising accuracy and ability of generalizations. The proposed neural network can also potentially be applied to different but relevant problems including flood predictions for urban layout planning

Sampling motif-constrained ensembles of networks

The statistical significance of network properties is conditioned on null models which satisfy spec- ified properties but that are otherwise random. Exponential random graph models are a principled theoretical framework to generate such constrained ensembles, but which often fail in practice, either due to model inconsistency, or due to the impossibility to sample networks from them. These problems affect the important case of networks with prescribed clustering coefficient or number of small connected subgraphs (motifs). In this paper we use the Wang-Landau method to obtain a multicanonical sampling that overcomes both these problems. We sample, in polynomial time, net- works with arbitrary degree sequences from ensembles with imposed motifs counts. Applying this method to social networks, we investigate the relation between transitivity and homophily, and we quantify the correlation between different types of motifs, finding that single motifs can explain up to 60% of the variation of motif profiles.Comment: Updated version, as published in the journal. 7 pages, 5 figures, one Supplemental Materia

Nova metodologija za procenu šteta usled plavljenja urbanih površina

Urban flooding caused by extreme rainfall events is becoming considerably more frequent and more destructive. Thus, enhanced models to predict accurately flood magnitude and location are of paramount importance. These models can then be used for urban planning, flood forecasting, flood management (real-time control, raise of flood alerts (emergency services management, etc.) and, ultimately, to estimate flood damage assessment. This paper demonstrates the capability of the Automatic Overland Flow Delineation (AOFD) methodology developed by the authors for flood damage estimation in urban areas. Properties in risk of flood are identified based on a spatial analysis, using the locations of flood - prone areas (ponds) and the location of buildings. The results obtained in this study open new research directions to estimate flood damage with even more detail, and extend flood damage estimation beyond property level, i.e. considering also traffic disruption, health issues and alike.Plavljenja urbanih površina usled jakih pljuskova postaje sve češće i opasnije. Zbog toga je neophodno raspolagati sa kvalitetnim modelom koji može predvideti intenzitet i lokaciju plavljenja. Takav model se može koristiti za urbanistička planiranja, predviđanje poplava i šteta usled poplava, kao i za upozorenja usled očekivanih poplava. U ovom radu se istražuje mogućnost primene metodologije za automatsku delineaciju površinskih tokova za procenu šteta u urbanim površinama. Objekti koji se plave se određuju na osnovu prostorne analize, koristeći rezultate analiza depresija na urbanim površinama. Dobijeni rezultati u ovom radu otvaraju nove oblasti za istraživanje: uticaj bolje prostorne rezolucije na proračuna šteta, i uticaj poplava na saobraćaj, zdravlje ljudi i slično