An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping

GIS-based multicriteria decision analysis (MCDA) methods are increasingly being used in landslide susceptibility mapping. However, the uncertainties that are associated with MCDA techniques may significantly impact the results. This may sometimes lead to inaccurate outcomes and undesirable consequences. This article introduces a new GIS-based MCDA approach. We illustrate the consequences of applying different MCDA methods within a decision-making process through uncertainty analysis. Three GIS-MCDA methods in conjunction with Monte Carlo simulation (MCS) and Dempster–Shafer theory are analyzed for landslide susceptibility mapping (LSM) in the Urmia lake basin in Iran, which is highly susceptible to landslide hazards. The methodology comprises three stages. First, the LSM criteria are ranked and a sensitivity analysis is implemented to simulate error propagation based on the MCS. The resulting weights are expressed through probability density functions. Accordingly, within the second stage, three MCDA methods, namely analytical hierarchy process (AHP), weighted linear combination (WLC) and ordered weighted average (OWA), are used to produce the landslide susceptibility maps. In the third stage, accuracy assessments are carried out and the uncertainties of the different results are measured. We compare the accuracies of the three MCDA methods based on (1) the Dempster–Shafer theory and (2) a validation of the results using an inventory of known landslides and their respective coverage based on object-based image analysis of IRS-ID satellite images. The results of this study reveal that through the integration of GIS and MCDA models, it is possible to identify strategies for choosing an appropriate method for LSM. Furthermore, our findings indicate that the integration of MCDA and MCS can significantly improve the accuracy of the results. In LSM, the AHP method performed best, while the OWA reveals better performance in the reliability assessment. The WLC operation yielded poor results.     

新安江模型参数的不确定性分析

水文模型的不确定性研究是水文科学研究的重要课题。模型参数的不确定性分析是水文模型不确定性研究的重要内容之一。本文采用GLUE方法分析新安江模型参数的不确定性,结论基于对不同水文特征流域的长时间径流模拟,研究发现大量"等效性"参数组存在。据此将参数总结为三类:第一类为非敏感参数,如上层张力水容量UM等。它们对似然判据,及确定性系数(R²)影响小。第二类为敏感性参数,如河网蓄水消退系数CS等,其特点是对R²的影响大。第三类为区域性敏感参数,如张力水蓄水容量曲线的方次B等,它们对R²的影响力跟流域特征密切相关。这些结论有助于理解新安江模型参数,为今后流域水文模拟提供参考。文中还展望了未来水文模型不确定性研究的发展方向。     

Parameter sensitivity analysis for a biochemically-based photosynthesis model

A challenge for the development of Land Surface Models(LSMs) is improving transpiration of water exchange and photosynthesis of carbon exchange between terrestrial plants and the atmosphere, both of which are governed by stoma in leaves. In the photosynthesis module of these LSMs, variations of parameters arising from diversity in plant functional types(PFTs) and climate remain unclear. Identifying sensitive parameters among all photosynthetic parameters before parameter estimation can not only ...     

SWAT模型参数敏感性分析及应用

地理信息系统(GIS)支持下的SWAT(Soil and Water Assessment Tool)分布式水文模型以流域离散化空间参数来描述流域水文变化特性,从物理意义上表达流域内的水文过程,但众多不确定的参数影响了模型的应用效果,因此有必要对参数进行敏感性分析。将SWAT模型应用到祁连山黑河上游山区流域,进行了11年(1990-2000年)逐日径流模拟,通过一个简便的敏感性分析方法,将模型影响水文过程的参数分成4类敏感级别,最后确定模型的参数。在11年的逐日模拟中,1990-1995年为参数敏感性分析期和模型率定期,1996-2000年为模型的检验期,模拟结果显示,在黑河山区流域,丰水年逐日出山径流的模型效率系数R2达到0.8以上,平水年和枯水年R2在0.51~0.79之间。     

分布式水文模型全局敏感性高效分析方法研究(英文)

Sensitivity analysis of hydrological model is the key for model uncertainty quantification. However, how to effectively validate model and identify the dominant parameters for distributed hydrological models is a bottle-neck to achieve parameters optimization. For this reason, a new approach was proposed in this paper, in which the support vector machine was used to construct the response surface at first. Then it integrates the SVM-based response surface with the Sobol’ method, i.e. the RSMSobol’ method, to quantify the parameter sensitivities. In this work, the distributed time-variant gain model (DTVGM) was applied to the Huaihe River Basin, which was used as a case to verify its validity and feasibility. We selected three objective functions (i.e. water balance coefficient WB, Nash-Sutcliffe efficiency coefficient NS, and correlation coefficient RC) to assess the model performance as the output responses for sensitivity analysis. The results show that the parameters g1 and g2 are most important for all the objective functions, and they are almost the same to that of the classical approach. Furthermore, the RSMSobol method can not only achieve the quantification of the sensitivity, and also reduce the computational cost, with good accuracy compared to the classical approach. And this approach will be effective and reliable in the global sensitivity analysis for a complex modelling system.     

Soil and bedrock distribution estimated from gully form and frequency: A GIS-based decision-tree model for Lebanon

Torrential rainfall and relatively sparse vegetation in the Mediterranean region result in the development of gully systems and land degradation, notably on lands with specific types of soil and bedrock. This paper proposes a decision-tree model to predict the distribution of soil and bedrock susceptible to gully erosion (white Rendzinas and marly rocks) from the form and frequency of gullies. The study area is located in Lebanon and the model is linked to GIS. V-fold cross-validation of the pruned model indicates that gully features including cross-section size and shape, network frequency, types of meandering, and catchment area can explain 80% of variance in soil/rock properties. The overall accuracy of the soil/rock map was estimated to be ca. 87%. The proposed model is relatively simple, and may also be applied to other areas. It is particularly useful when information about soil and rock obtained from conventional field surveys is limited.     

Spatially-explicit sensitivity and uncertainty analysis in a MCDA-based flood vulnerability model

This study presents a methodology for conducting sensitivity and uncertainty analysis of a GIS-based multi-criteria model used to assess flood vulnerability in a case study in Brazil. The paper explores the robustness of model outcomes against slight changes in criteria weights. One criterion was varied at-a-time, while others were fixed to their baseline values. An algorithm was developed using Python and a geospatial data abstraction library to automate the variation of weights, implement the ANP (analytic network process) tool, reclassify the raster results, compute the class switches, and generate an uncertainty surface. Results helped to identify highly vulnerable areas that are burdened by high uncertainty and to investigate which criteria contribute to this uncertainty. Overall, the criteria ‘houses with improper building material’ and ‘evacuation drills and training’ are the most sensitive ones, thus, requiring more accurate measurements. The sensitivity of these criteria is explained by their weights in the base run, their spatial distribution, and the spatial resolution. These findings can support decision makers to characterize, report, and mitigate uncertainty in vulnerability assessment. The case study results demonstrate that the developed approach is simple, flexible, transparent, and may be applied to other complex spatial problems.     

A meta-modeling approach for spatio-temporal uncertainty and sensitivity analysis: an application for a cellular automata-based Urban growth and land-use change model

The paper presents a computationally efficient meta-modeling approach to spatially explicit uncertainty and sensitivity analysis in a cellular automata (CA) urban growth and land-use simulation model. The uncertainty and sensitivity of the model parameters are approximated using a meta-modeling method called polynomial chaos expansion (PCE). The parameter uncertainty and sensitivity measures obtained with PCE are compared with traditional Monte Carlo simulation results. The meta-modeling approach was found to reduce the number of model simulations necessary to arrive at stable sensitivity estimates. The quality of the results is comparable to the full-order modeling approach, which is computationally costly. The study shows that the meta-modeling approach can significantly reduce the computational effort of carrying out spatially explicit uncertainty and sensitivity analysis in the application of spatio-temporal models.     

Spatially-explicit sensitivity analysis of an agent-based model of land use change

The complexity of land use and land cover (LULC) change models is often attributed to spatial heterogeneity of the phenomena they try to emulate. The associated outcome uncertainty stems from a combination of model unknowns. Contrarily to the widely shared consensus on the importance of evaluating outcome uncertainty, little attention has been given to the role a well-structured spatially explicit sensitivity analysis (SSA) of LULC models can play in corroborating model results. In this article, I propose a methodology for SSA that employs sensitivity indices (SIs), which decompose outcome uncertainty and allocate it to various combinations of inputs. Using an agent-based model of residential development, I explore the utility of the methodology in explaining the uncertainty of simulated land use change. Model sensitivity is analyzed using two approaches. The first is spatially inexplicit in that it applies SI to scalar outputs, where outcome land use maps are lumped into spatial statistics. The second approach, which is spatially explicit, employs the maps directly in SI calculations. It generates sensitivity maps that allow for identifying regions of factor influence, that is, areas where a particular input contributes most to the clusters of residential development uncertainty. I demonstrate that these two approaches are complementary, but at the same time can lead to different decisions regarding input factor prioritization.     

GIS-based dynamic modelling and analysis of flash floods considering land-use planning

Flood inundation is a common natural disaster and a growing development challenge for many cities and thousands of small towns around the world. Soil features have frequently altered with the rapid development of urbanised regions, which has led to more frequent and longer duration of flooding in urban flood-prone regions. Thus, this paper presents a geographic information system (GIS)-based methodology for measuring and visualising the effects on urban flash floods generated by land-use changes over time. The measurement is formulated with a time series in order to perform a dynamic analysis. A catchment mesh is introduced into a hydrological model for reflecting the spatial layouts of infrastructure and structures over different construction periods. The Geelong Waurn Ponds campus of Deakin University is then selected as a case study. Based on GIS simulation and mapping technologies, this research illustrates the evolutionary process of flash floods. The paper then describes flood inundation for different built environments and presents a comparison by quantifying the flooding extents for infrastructure and structures. The results reveal that the GIS-based estimation model can examine urban flash floods in different development phases and identify the change of flooding extents in terms of land-use planning. This study will bring benefits to urban planners in raising awareness of flood impact and the approach proposed here could be used for flood mitigation through future urban planning.     

A GIS-based fire spread simulator integrating a simplified physical wildland fire model and a wind field model

??This article discusses the integration of two models, namely, the Physical Forest Fire Spread (PhFFS) and the High Definition Wind Model (HDWM), into a Geographical Information System-based interface. The resulting tool automates data acquisition, preprocesses spatial data, launches the aforementioned models and displays the corresponding results in a unique environment. Our implementation uses the Python language and Esri’s ArcPy library to extend the functionality of ArcMap 10.4. The PhFFS is a simplified 2D physical wildland fire spread model based on conservation equations, with convection and radiation as heat transfer mechanisms. It also includes some 3D effects. The HDWM arises from an asymptotic approximation of the Navier–Stokes equations, and provides a 3D wind velocity field in an air layer above the terrain surface. Both models can be run in standalone or coupled mode. Finally, the simulation of a real fire in Galicia (Spain) confirms that the tool developed is efficient and fully operational.     

Automatic selection of weights for GIS-based multicriteria decision analysis: site selection of transmission towers as a case study

Transmission line (TL) siting consists of finding suitable land to build transmission towers. This is just one of the numerous complex geographical problems often solved using GIS-based multicriteria decision analysis (MCDA), which is a set of techniques that weight several geographical features to identify suitable locations. This technique is mostly employed using expert knowledge to identify the correct set of weights; thus adding a certain amount of subjectivity to the analysis, meaning that for the same problem if we change the experts involved, we may reach different results.This research is a first attempt to try and solve this issue. We employed a statistical analysis to quantitatively calculate these weights and we tested our method on a case study about transmission line siting in Switzerland. We compared the distances between each sample in our dataset, in this case study these are location of transmission towers, with each geographical feature, e.g. distance from water features. Then we calculate the same distances but for random points, sampled throughout the study area. The reasoning behind this method is that if samples present a distance from a geographic feature statistically different from the random, it means that the feature played an important role in dictating the location of the sample. In this case for instance, high-voltage transmission towers are purposely built as far away as possible from urban areas. Random points are on the contrary by definition sampled without any constraint. Therefore, when comparing the two datasets, we should find that transmission towers have a larger average distance from urban areas than random points. This allows us to determine that this criterion (i.e. distance from urban centers) is important for planning new TL.The results indicate that this method can successfully weight and rank the most important criteria to be considered for an MCDA analysis, in line with weights proposed in the literature. The advantage of the proposed technique is that it completely excludes human factors, thus potentially increasing the social acceptance of the MCDA results.     

基于GIS的天山北坡聚落地名文化景观分析

通过利用地理信息手段对新疆天山北坡的地名文化景观进行可视化分析,将不同类型的地名进行分类,共分划分出了兵团类、自然类、工(宫)运类、民族类、数字类、姓氏类、直属类、移民类与其他类地名共9种类型。在此基础上,利用GIS技术将点状化的地名进行分类分层和核密度分析,将不同类型的地名集聚和分布状况进行可视化分析,着眼于新疆天山北坡的历史、经济、自然、民族文化等情况,对不同类型的地名集聚和分布状况进行分析,研究发现该区域地名整体较集中地分布在南部天山与北部沙漠盆地之间的几个绿洲城镇集群附近,其余9种类型地名除直属类分布较离散外,其他类型基本都分布在中部绿洲条带线上,呈现出各自历史情况、经济条件、自然特征和包括屯垦、民族、移民以及新疆地域文化特色规律的分布特征。     

模型多参数灵敏度与不确定性分析

以潮白河为研究区域,探讨了与模型参数及模型模拟性能有关的多参数灵敏度及不确定性分析方法(Multi-Parameter Sensitivity and Uncertainty Analysis, MPSUA)。基于Monte Carlo模拟的多参数灵敏度分析,可以评价模型中多个参数的相对重要性。GLUE不确定性分析则能对模型性能进行量化评估。实例研究表明,通过MPSUA方法,可以减少优化参数的个数。而且,在没有对模型进行参数优化之前,基于MPSUA就可以确定模型的模拟精度。例如同样的模型在潮河可以获得比在白河更高的模拟精度。这种同一模型在不同流域所体现的差异性,一方面是源于模型结构本身的不完善,另一方面则与用于建模的数据误差有关。SCE-UA参数优化结果与MPSUA结果几乎一致,说明本文的参数灵敏度与模型总体性能评估方法比较合理。     

区域生态敏感性评估的理论框架与模型设计——以陕西省为例

生态敏感性评估可以为管控生态风险与保障区域生态安全提供决策依据。在生态敏感性定义的基础之上运用经济学理论构建生态敏感性评估的理论模型,以土地利用转移几率比替代矩阵作为敏感性评价因子,并且利用生态系统服务价值的实际值与预测值之比的绝对值作为区域生态敏感性指数。结果表明：① 设计的生态敏感性评估方法可以弥补现有生态敏感性评估方法的不足,不仅可以观测到区域敏感性因子还能有效地进行敏感性分区,并且在不同尺度下得到了可靠的结果。② 在各县（区）最主要敏感性因子的列示中,陕西省1990—2000年以生态破坏因子为主导;而2000年以后,退耕还林工程导致耕地→林地、耕地→草地成为陕北地区最重要的敏感因子,陕南秦巴山区与关中地区由于众多水利工程建设导致生态系统服务价值对草地→水域因子的响应程度比较高。③ 陕西省总体敏感性由南向北呈现“低敏感-高敏感”间隔有序的基本格局,并且呈现空间上集聚、时间上稳定的状态;高敏感区主要分布沿秦岭、“子午岭-黄陵山”分布,低敏感区主要分布在关中平原以及榆林中部地区。     

Accounting for Parameter Uncertainty in Reservoir Uncertainty Assessment: The Conditional Finite-Domain Approach

An important aim of modern geostatistical modeling is to quantify uncertainty in geological systems. Geostatistical modeling requires many input parameters. The input univariate distribution or histogram is perhaps the most important. A new method for assessing uncertainty in the histogram, particularly uncertainty in the mean, is presented. This method, referred to as the conditional finite-domain (CFD) approach, accounts for the size of the domain and the local conditioning data. It is a stochastic approach based on a multivariate Gaussian distribution. The CFD approach is shown to be convergent, design independent, and parameterization invariant. The performance of the CFD approach is illustrated in a case study focusing on the impact of the number of data and the range of correlation on the limiting uncertainty in the parameters. The spatial bootstrap method and CFD approach are compared. As the number of data increases, uncertainty in the sample mean decreases in both the spatial bootstrap and the CFD. Contrary to spatial bootstrap, uncertainty in the sample mean in the CFD approach decreases as the range of correlation increases. This is a direct result of the conditioning data being more correlated to unsampled locations in the finite domain. The sensitivity of the limiting uncertainty relative to the variogram and the variable limits are also discussed.     

Regional-typological study and mapping of geosystems: analysis of the implementation

We give an outline of the scientific-methodological principles of using the regional-typological approach in geographical research. Based on analyzing cartographic products, we demonstrate the differences in understanding and implementing the approach. The structurallogical schematic diagram for the regional-typological approach has been developed, which opens up possibilities for its implementation in the study and mapping of geosystems and can provide an integral, systemic idea of this approach.     

A systematic sensitivity analysis of constrained cellular automata model for urban growth simulation based on different transition rules

Cellular automata (CA) have emerged as a primary tool for urban growth modeling due to its simplicity, transparency, and ease of implementation. Sensitivity analysis is an important component in CA modeling for a better understanding of errors or uncertainties and their propagation. Most studies on sensitivity analyses in urban CA modeling focus on specific component such as neighborhood configuration or stochastic perturbation. However, sensitivity analysis of transition rules, which is one of the core components in CA models, has not been systematically done. This article proposes a systematic sensitivity analysis of major operational components in urban CA modeling using a stepwise comparison approach. After obtaining transition rules, three stages (i.e. static calibration of transition rules, dynamic evolution with varied time steps, and incorporation with stochastic perturbation) are designed to facilitate a comprehensive analysis. This scheme implemented with a case study in Guangzhou City (China) reveals that gaps in performance from static calibration with different transition rules can be reduced when dynamic evolution is considered. Moreover, the degree of stochastic perturbation is closely related to obtain urban morphology. However, a more realistic (i.e. fragmented) urban landscape is achieved at the cost of decreasing pixel-based accuracy in this study. Thus, a trade-off between pixel-based and pattern-based comparisons should be balanced in practical urban modeling. Finally, experimental results illustrate that models for transition rules extraction with good quality can do an assistance for urban modeling through reducing errors and uncertainty range. Additionally, ensemble methods can feasibly improve the performance of CA models when coupled with nonparametric models (i.e. classification and regression tree).