•We present a spatiotemporal appraisal of poverty in the coastal zone in Bangladesh.•Flood risk is positively correlated with land use/land cover (LULC) change.•The expected annual damage of 2005 is ...estimated to be more than double by 2030.•Flood risk and patterns of LULC change have a negative effect on wealth index.•The rate of increase of wealth index is likely to be low in the future.
The construction of polders in the coastal region of Bangladesh has significantly modified the patterns of flooding, as well as leading to significant land use/land cover (hereinafter, LULC) changes. The impact of LULC change and flooding on poverty is complex and poorly understood. This study presents a spatiotemporal appraisal of poverty in relation to LULC change and pluvial flood risk in the south western embanked area of Bangladesh. A combination of logistic regression (LR), cellular automata (CA), and Markov Chain models were utilised to predict future LULC based on historical data. Flood risk assessment was performed at present and for future LULC scenarios. A spatial regression model was developed, incorporating multiple parameters to estimate the wealth index (WI) for present-day and future scenarios. In the study area, agricultural lands reduced from 34 % in 2005 to 8% in 2010, while aquaculture land cover increased from 17 % to 39 % during the same time. The rate of LULC change was relatively low between 2010 and 2019. Based on the recent trend, LULC was predicted for the year 2030. Flood risk was positively correlated with LULC and the expected annual damage (EAD) was estimated at $903 million in 2005, which is likely to increase to $2096 million by 2030, considering changes in LULC scenarios. The analysis further showed that the EAD and LULC change were negatively associated with the WI. Despite consistent national GDP growth in Bangladesh in recent years, the rate of increase of WI is likely to be low in the future because flood risk and patterns of LULC change have a negative effect on WI.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Probabilistic inversion within a multiple‐point statistics framework is often computationally prohibitive for high‐dimensional problems. To partly address this, we introduce and evaluate a new ...training‐image based inversion approach for complex geologic media. Our approach relies on a deep neural network of the generative adversarial network (GAN) type. After training using a training image (TI), our proposed spatial GAN (SGAN) can quickly generate 2‐D and 3‐D unconditional realizations. A key characteristic of our SGAN is that it defines a (very) low‐dimensional parameterization, thereby allowing for efficient probabilistic inversion using state‐of‐the‐art Markov chain Monte Carlo (MCMC) methods. In addition, available direct conditioning data can be incorporated within the inversion. Several 2‐D and 3‐D categorical TIs are first used to analyze the performance of our SGAN for unconditional geostatistical simulation. Training our deep network can take several hours. After training, realizations containing a few millions of pixels/voxels can be produced in a matter of seconds. This makes it especially useful for simulating many thousands of realizations (e.g., for MCMC inversion) as the relative cost of the training per realization diminishes with the considered number of realizations. Synthetic inversion case studies involving 2‐D steady state flow and 3‐D transient hydraulic tomography with and without direct conditioning data are used to illustrate the effectiveness of our proposed SGAN‐based inversion. For the 2‐D case, the inversion rapidly explores the posterior model distribution. For the 3‐D case, the inversion recovers model realizations that fit the data close to the target level and visually resemble the true model well.
Key Points
A new deep‐learning scheme for 2‐D and 3‐D unconditional geostatistical simulation using one training image
The deep neural network admits a very low‐dimensional base which allows for parameter‐based inversion
The approach is illustrated for Bayesian inversion involving 2‐D and 3‐D categorical training images
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
In the scope of assessing aquifer systems in areas where freshwater is scarce, estimation of transit times is a vital step to quantify the effect of groundwater ion. Transit time distributions of ...different shapes, mean residence times, and contributions are used to represent the hydrogeological conditions in aquifer systems and are typically inferred from measured tracer concentrations by inverse modeling. In this study, a multi‐tracer sampling campaign was conducted in the Salalah Plain in Southern Oman including CFCs, SF6, 39Ar, 14C, and 4He. Based on the data of three tracers, a two‐component Dispersion Model (DMmix) and a nonparametric model with six age bins were assumed and evaluated using Bayesian statistics. In a Markov Chain Monte Carlo approach, the maximum likelihood parameter estimates and their uncertainties were determined. Model performance was assessed using Bayes factor and leave‐one‐out cross‐validation. Both models suggest that the groundwater in the Salalah Plain is composed of a very young component below 30 yr and a very old component beyond 1,000 yr, with the nonparametric model performing slightly better than the DMmix model. All wells except one exhibit reasonable goodness of fit. Our results support the relevance of Bayesian modeling in hydrology and the potential of nonparametric models for an adequate representation of aquifer dynamics.
Key Points
Groundwater in a semi‐arid area was dated with multiple tracers including the first full‐scale application of 39Ar with Argon Trap Trace Analysis
Bayesian inference was applied for modeling the transit time distributions using a Markov‐Chain Monte Carlo simulation
A Dispersion Model with two components and a nonparametric model with six age bins were applied, both suggesting a mixed groundwater of very old and very young origin
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
•A general multi-state balanced system is proposed.•We construct two specific balance functions to measure the system balance degree.•Reliability indexes are derived for the new model by a two-step ...FMCIA.
Reliability analysis of balanced systems has become an important research topic. In previous studies, a balanced system only has two states, i.e., perfect functioning and complete failure. However, most practical systems have more than two states because of the complex system structure and complicated working environment. To fill in this research gap, a general multi-state balanced system is proposed by considering that the components in the system and the whole system both have multiple states. In this paper, component state transitions are assumed to be caused by external shocks. Based on the operating states of all components, the multiple states of the system are determined according to different system balance degrees, formulated by a balance function. Multi-state balanced systems based on state distance and symmetric state distance are constructed in detail based on two specific balance functions. The corresponding state probability functions and some other reliability indexes are derived by using a two-step finite Markov chain imbedding approach. Finally, numerical examples are presented and some future research topics are discussed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
A 3-D shear wave velocity model of Southeastern Margin of Tibetan Plateau crust was constructed by Markov Chain Monte Carlo inversion, based on Rayleigh wave phase velocity tomography results which ...are obtained from ambient noise interferometry and tele-seismic two plane wave analysis, using seismic data of 137 permanent stations from China digital seismic network and 332 portable stations from ChinArray. The velocity model indicates the presence of an interconnected lower crustal channel flow in southeastern margin of Tibetan plateau, which is represented by an interconnected low shear wave velocity zone with Vs < 3.55 km/s. It consists of three parts which respectively locates beneath the Panzhihua area and to its west and east. The lower crustal channel flow from Tibetan Plateau is blocked by rigid lower crust of the Sichuan block and turns to flow through Emeishan area to the south direction, which alters the lower crust of Emeishan large igneous province (ELIP) together with the lower crustal channel flo
Abstract In a world made of atoms, computer simulations of molecular systems such as proteins in water play an enormous role in science. Software packages for molecular simulation have been developed ...for decades. They all discretize Hamilton’s equations of motion and treat long-range potentials through cutoffs or discretization of reciprocal space. This introduces severe approximations and artifacts that must be controlled algorithmically. Here, we bring to fruition a paradigm for molecular simulation that relies on modern concepts in statistics to explore the thermodynamic equilibrium with an exact and efficient non-reversible Markov process. It is free of all discretizations, approximations, and cutoffs. We explicitly demonstrate that this approach reaches a break-even point with traditional molecular simulation performed at high precision, but without any of its approximations. We stress the potential of our paradigm for crucial applications in biophysics and other fields, and as a practical approach to molecular simulation. We set out a strategy to reach our goal of rigorous molecular simulation.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Stochastic Gradient Markov Chain Monte Carlo Nemeth, Christopher; Fearnhead, Paul
Journal of the American Statistical Association,
03/2021, Volume:
116, Issue:
533
Journal Article
Peer reviewed
Open access
Markov chain Monte Carlo (MCMC) algorithms are generally regarded as the gold standard technique for Bayesian inference. They are theoretically well-understood and conceptually simple to apply in ...practice. The drawback of MCMC is that performing exact inference generally requires all of the data to be processed at each iteration of the algorithm. For large datasets, the computational cost of MCMC can be prohibitive, which has led to recent developments in scalable Monte Carlo algorithms that have a significantly lower computational cost than standard MCMC. In this article, we focus on a particular class of scalable Monte Carlo algorithms, stochastic gradient Markov chain Monte Carlo (SGMCMC) which utilizes data subsampling techniques to reduce the per-iteration cost of MCMC. We provide an introduction to some popular SGMCMC algorithms and review the supporting theoretical results, as well as comparing the efficiency of SGMCMC algorithms against MCMC on benchmark examples. The supporting R code is available online at
https://github.com/chris-nemeth/sgmcmc-review-paper
.
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BFBNIB, GIS, IJS, KISLJ, NUK, PNG, UL, UM, UPUK
Probabilistic programming allows for automatic Bayesian inference on user-defined probabilistic models. Recent advances in Markov chain Monte Carlo (MCMC) sampling allow inference on increasingly ...complex models. This class of MCMC, known as Hamiltonian Monte Carlo, requires gradient information which is often not readily available. PyMC3 is a new open source probabilistic programming framework written in Python that uses Theano to compute gradients via automatic differentiation as well as compile probabilistic programs on-the-fly to C for increased speed. Contrary to other probabilistic programming languages, PyMC3 allows model specification directly in Python code. The lack of a domain specific language allows for great flexibility and direct interaction with the model. This paper is a tutorial-style introduction to this software package.
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