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The complex and computationally expensive nature of landscape evolutionmodels pose significant challenges in the inference and optimisation of unknownparameters. Bayesian inference provides a methodology for estimation anduncertainty quantification of unknown model parameters. In our previous work,we developed parallel tempering Bayeslands as a framework for parameterestimation and uncertainty quantification for the Badlands landscape evolutionmodel. Parallel tempering Bayeslands features high-performance computing withdozens of processing cores running in parallel to enhance computationalefficiency. Although we use parallel computing, the procedure remainscomputationally challenging since thousands of samples need to be drawn andevaluated. \textcolor{black}{In large-scale landscape and basin evolutionproblems, a single model evaluation can take from several minutes to hours, andin some instances, even days. Surrogate-assisted optimisation has been used forseveral computationally expensive engineering problems which motivate its usein optimisation and inference of complex geoscientific models.} The use ofsurrogate models can speed up parallel tempering Bayeslands by developingcomputationally inexpensive models to mimic expensive ones. In this paper, weapply surrogate-assisted parallel tempering where that surrogate mimics alandscape evolution model by estimating the likelihood function from the model.\textcolor{black}{We employ a neural network-based surrogate model that learnsfrom the history of samples generated. } The entire framework is developed in aparallel computing infrastructure to take advantage of parallelism. The resultsshow that the proposed methodology is effective in lowering the overallcomputational cost significantly while retaining the quality of solutions.

Surrogate-assisted Bayesian inversion for landscape and basin evolution models