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• W3022275280 abstract "Environmental sustainability is a major concern for urban and rural development. Actors and stakeholders need economic, effective and efficient simulations in order to predict and evaluate the impact of development on the environment and the constraints that the environment imposes on development. Numerical simulation models are usually computation expensive and require expert knowledge. We consider the problem of hydrological modelling and simulation. With a training set consisting of pairs of inputs and outputs from an off-the-shelves simulator, We show that a neural network can learn a surrogate model effectively and efficiently and thus can be used as a surrogate simulation model. Moreover, we argue that the neural network model, although trained on some example terrains, is generally capable of simulating terrains of different sizes and spatial characteristics." @default.
• W3022275280 created "2020-05-13" @default.
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• W3022275280 date "2020-01-01" @default.
• W3022275280 modified "2023-09-25" @default.
• W3022275280 title "Hydrological Process Surrogate Modelling and Simulation with Neural Networks" @default.
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• W3022275280 doi "https://doi.org/10.1007/978-3-030-47436-2_34" @default.
• W3022275280 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7206312" @default.
• W3022275280 hasPublicationYear "2020" @default.
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