High-dimensional posterior exploration of hydrologic models using multiple-try DREAM(ZS) and high-performance computing
High-dimensional posterior exploration of hydrologic models using multiple-try DREAM(ZS) and high-performance computing
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DOI：
10.1029/2011wr010608
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发表时间：
2012-01-20
影响因子：
5.4
通讯作者：
Vrugt, Jasper A.
中科院分区：
文献类型：
--
作者：
Laloy, Eric;Vrugt, Jasper A.
空间分布的水文模型越来越多地被用于研究和预测土壤水分流动，地下水补给，地表径流和河流流量。这种复杂模型的有用性和适用性越来越受到潜在的数百（数千）个参数的阻碍，这些参数需要根据一些历史数据记录进行校准。当前一代的搜索和优化算法通常不足以处理非常大量的变量并总结参数和预测不确定性。我们以前提出了一个通用的马尔可夫链蒙特卡罗（MCMC）算法的贝叶斯推理的后验概率密度函数的水文模型参数。这种方法，称为差分进化自适应大都会（梦想），并行运行多个不同的马尔可夫链，并使用离散的建议分布，以发展采样后验分布。DREAM方法保持了详细的平衡，并在复杂的多模态搜索问题上表现出出色的性能。在这里，我们介绍了我们最新的算法发展，并介绍了MT-DREAM（（ZS）），它结合了多次尝试采样，斯诺克更新和从过去的状态档案中采样的优势。这种新的代码是专门设计来解决高维搜索问题，并收到特别壮观的性能改善时，使用分布式计算的其他自适应MCMC方法。四个不同的案例研究，增加到241个参数的维数来说明MT-DREAM（（ZS））的优势。
Spatially distributed hydrologic models are increasingly being used to study and predict soil moisture flow, groundwater recharge, surface runoff, and river discharge. The usefulness and applicability of such complex models is increasingly held back by the potentially many hundreds (thousands) of parameters that require calibration against some historical record of data. The current generation of search and optimization algorithms is typically not powerful enough to deal with a very large number of variables and summarize parameter and predictive uncertainty. We have previously presented a general-purpose Markov chain Monte Carlo (MCMC) algorithm for Bayesian inference of the posterior probability density function of hydrologic model parameters. This method, entitled differential evolution adaptive Metropolis (DREAM), runs multiple different Markov chains in parallel and uses a discrete proposal distribution to evolve the sampler to the posterior distribution. The DREAM approach maintains detailed balance and shows excellent performance on complex, multimodal search problems. Here we present our latest algorithmic developments and introduce MT-DREAM((ZS)), which combines the strengths of multiple-try sampling, snooker updating, and sampling from an archive of past states. This new code is especially designed to solve high-dimensional search problems and receives particularly spectacular performance improvement over other adaptive MCMC approaches when using distributed computing. Four different case studies with increasing dimensionality up to 241 parameters are used to illustrate the advantages of MT-DREAM((ZS)).