Hydrological Process Surrogate Modelling and Simulation with Neural Networks

Autor:	Abhishek Saha, Stéphane Bressan, Jifang Xing, Dewa Made Sri Arsa, Remmy A. M. Zen, Ruixi Zhang
Rok vydání:	2020
Předmět:	Computer simulation Artificial neural network business.industry Process (engineering) Computer science Computation Hydrological modelling Terrain Machine learning computer.software_genre Surrogate model Sustainability Artificial intelligence business computer
Zdroj:	Advances in Knowledge Discovery and Data Mining ISBN: 9783030474355 PAKDD (2)
DOI:	10.1007/978-3-030-47436-2_34
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::dd1e8e74ba4975527c93ff4f73dfe0b9 https://doi.org/10.1007/978-3-030-47436-2_34 Zobrazit plný text záznamu