首页|Reports from University of Western Ontario Describe Recent Advances in Machine Learning (Impacts of Dem Type and Resolution On Deep Learning-based Flood Inundation Mapping)
Reports from University of Western Ontario Describe Recent Advances in Machine Learning (Impacts of Dem Type and Resolution On Deep Learning-based Flood Inundation Mapping)
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Springer Nature
Research findings on Machine Learning are discussed in a new report. According to news reporting from London, Canada, by NewsRx journalists, research stated, "The increasing availability of hydrological and physiographic spatiotemporal data has boosted machine learning's role in rapid flood mapping. Yet, data scarcity, especially high-resolution DEMs, challenges regions with limited access." Financial support for this research came from National Oceanic Atmospheric Admin (NOAA) - USA. The news correspondents obtained a quote from the research from the University of Western Ontario, "This paper examines how DEM type and resolution affect flood prediction accuracy, utilizing a cutting-edge deep learning (DL) method called 1D convolutional neural network (CNN). It utilizes synthetic hydrographs as training input and water depth data obtained from LISFLOOD-FP, a 2D hydrodynamic model, as target data. This study investigates digital surface models (DSMs) and digital terrain models (DTMs) derived from a 1 m LIDAR-based DTM, with resolutions from 15 to 30 m. The methodology is applied and assessed in a established benchmark, city of Carlisle, UK. The models' performance is then evaluated and compared against an observed flood event using RMSE, Bias, and Fit indices. Leveraging the insights gained from this region, the paper discusses the applicability of the methodology to address the challenges encountered in a data-scarce flood-prone region, exemplified by Pakistan. Results indicated that utilizing a 30 m DTM outperformed a 30 m DSM in terms of flood depth prediction accuracy by about 21% during the flood peak stage, highlighting the superior performance of DTM at lower resolutions. Increasing the resolution of DTM to 15 m resulted in a minimum 50% increase in RMSE and a 20% increase in fit index across all flood stages. The findings emphasize that while a coarser resolution DEM may impact the accuracy of machine learning models, it remains a viable option for rapid flood prediction."
LondonCanadaNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningUniversity of Western Ontario
NETL
NSTL
Springer Nature
