@inproceedings{c530d0293a2e42ee9073d3fc0e6f95c0,
title = "Hydrological Process Surrogate Modelling and Simulation with Neural Networks",
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.",
keywords = "Neural networks hydrological, Simulation, Surrogate model",
author = "Ruixi Zhang and Remmy Zen and Jifang Xing and Arsa, {Dewa Made Sri} and Abhishek Saha and St{\'e}phane Bressan",
year = "2020",
doi = "10.1007/978-3-030-47436-2_34",
language = "English",
isbn = "9783030474355",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "SpringerOpen",
pages = "449--461",
editor = "Lauw, {Hady W.} and Ee-Peng Lim and Wong, {Raymond Chi-Wing} and Alexandros Ntoulas and See-Kiong Ng and Pan, {Sinno Jialin}",
booktitle = "Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings",
note = "24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020 ; Conference date: 11-05-2020 Through 14-05-2020",
}