Automated watershed subdivision for simulations using multi-objective optimization

A qualitative trial-and-error approach is commonly used to define watershed subdivisions through varying a single topographic threshold value. A methodology has been developed to quantitatively determine spatially variable threshold values using topography and a user-defined landscape reference layer. Optimization and topographic parameterization algorithms were integrated to create solutions that minimize the number of sub-watersheds and maximize the agreement between the discretized watershed and the reference layer. The system was evaluated using different reference datasets such as soil type, land management, and landscape form. Comparison of simulated results indicated that the scenario using land management as the reference layer yielded results closer to the scenario subdivided using a constant topographic threshold but with approximately 10 times more sub-catchments and therefore indicating customization of the watershed subdivision to the user-defined reference layer. The proposed optimization technology could be used in adequately applying watershed modeling technology in developing conservation practice implementation plans.     

SegOptim—A new R package for optimizing object-based image analyses of high-spatial resolution remotely-sensed data

Geographic Object-based Image Analysis (GEOBIA) is increasingly used to process high-spatial resolution imagery, with applications ranging from single species detection to habitat and land cover mapping. Image segmentation plays a key role in GEOBIA workflows, allowing to partition images into homogenous and mutually exclusive regions. Nonetheless, segmentation techniques require a robust parameterization to achieve the best results. Frequently, inappropriate parameterization leads to sub-optimal results and difficulties in comparing distinct methods.Here, we present an approach based on Genetic Algorithms (GA) to optimize image segmentation parameters by using the performance scores from object-based classification, thus allowing to assess the adequacy of a segmented image in relation to the classification problem. This approach was implemented in a new R package called SegOptim, in which several segmentation algorithms are interfaced, mostly from open-source software (GRASS GIS, Orfeo Toolbox, RSGISLib, SAGA GIS, TerraLib), but also from proprietary software (ESRI ArcGIS). SegOptim also provides access to several machine-learning classification algorithms currently available in R, including Gradient Boosted Modelling, Support Vector Machines, and Random Forest.We tested our approach using very-high to high spatial resolution images collected from an Unmanned Aerial Vehicle (0.03 – 0.10 m), WorldView-2 (2 m), RapidEye (5 m) and Sentinel-2 (10 – 20 m) in six different test sites located in northern Portugal with varying environmental conditions and for different purposes, including invasive species detection and land cover mapping. The results highlight the added value of our novel comparison of image segmentation and classification algorithms. Overall classification performances (assessed through cross-validation with the Kappa index) ranged from 0.85 to 1.00. Pilot-tests show that our GA-based approach is capable of providing sound results for optimizing the parameters of different segmentation algorithms, with benefits for classification accuracy and for comparison across techniques. We also verified that no particular combination of an image segmentation and a classification algorithm is suited for all the tasks/objectives. Consequently, it is crucial to compare and optimize available methods to understand which one is more suited for a certain objective.Our approach allows a closer integration between the segmentation and classification stages, which is of high importance for GEOBIA workflows. The results from our tests confirm that this integration has benefits for comparing and optimizing both processes. We discuss some limitations of the SegOptim approach (and potential solutions) as well as a future roadmap to expand its current functionalities.     

Improving explicit time integration by modal truncation techniques

This paper describes a modal weighting technique that improves the stability characteristics of explicit time-integration schemes used in structural dynamics. The central difference method was chosen as the trial algorithm because of its simplicity, both in terms of formulation and ease of numerical stability and convergence analysis. It is shown how explicit algorithms may be reformulated in order to make them stable for any integration time by attenuating high-frequency oscillation modes that are generated by mesh geometry rather than generic dynamical features. We discuss results from trial calculations obtained from mathematical models that represent hysteretic restoring force elements and an application on a physical, four-degree-of-freedom, base-isolated structure using the pseudodynamic technique. © 1998 John Wiley & Sons, Ltd.     

A memory-matrix-based identification methodology for structural and mechanical systems

This paper uses an associative memory approach to identify the properties of structural and mechanical systems. The methodology differs from standard identification methods in that it uses a set of parameter vectors simultaneously to generate the estimated parameter vector. The method develops a technique for sequentially generating genetically engineered relevant parameter vectors whose use results in accurate identification, while still using small data sets. This makes the approach promising for on-line, rapid, identification of structures and their health monitoring. © 1998 John Wiley & Sons, Ltd.     

Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management?

Natural hazards are often studied in isolation.However,there is a great need to examine hazards holistically to better manage the complex of threats found in any region.Many regions of the world have complex hazard landscapes wherein risk from individual and/or multiple extreme events is omnipresent.Extensive parts of Iran experience a complex array of natural hazards-floods,earthquakes,landslides,forest fires,subsidence,and drought.The effectiveness of risk mitigation is in part a function of whether the complex of hazards can be collectively considered,visualized,and evaluated.This study develops and tests individual and collective multihazard risk maps for floods,landslides,and forest fires to visualize the spatial distribution of risk in Fars Province,southern Iran.To do this,two well-known machine-learning algorithms-SVM and MARS-are used to predict the distribution of these events.Past floods,landslides,and forest fires were surveyed and mapped.The locations of occurrence of these events(individually and collectively) were randomly separated into training(70%) and testing(30%) data sets.The conditioning factors(for floods,landslides,and forest fires) employed to model the risk distributions are aspect,elevation,drainage density,distance from faults,geology,LULC,profile curvature,annual mean rainfall,plan curvature,distance from man-made residential structures,distance from nearest river,distance from nearest road,slope gradient,soil types,mean annual temperature,and TWI.The outputs of the two models were assessed using receiver-operating-characteristic(ROC) curves,true-skill statistics(TSS),and the correlation and deviance values from each models for each hazard.The areas-under-the-curves(AUC) for the MARS model prediction were 76.0%,91.2%,and 90.1% for floods,landslides,and forest fires,respectively.Similarly,the AUCs for the SVM model were 75.5%,89.0%,and 91.5%.The TSS reveals that the MARS model was better able to predict landslide risk,but was less able to predict flood-risk patterns and forest-fire risk.Finally,the combination of flood,forest fire,and landslide risk maps yielded a multi-hazard susceptibility map for the province.The better predictive model indicated that 52.3% of the province was at-risk for at least one of these hazards.This multi-hazard map may yield valuable insight for land-use planning,sustainable development of infrastructure,and also integrated watershed management in Fars Province.     

基于遗传算法的超声回弹综合法混凝土测强公式

利用30个混凝土试块的超声波声速、回弹、抗压强度实测数据,应用遗传算法建立了新的超声回弹的混凝土强度换算值计算公式,该公式明显改进了高强度区混凝土强度的拟合精度。结果表明,作为一种全局搜索技术,遗传算法在超声回弹综合法测强曲线研制过程中具有实际应用价值。     

平面影像到QTM像元的转换算法及精度分析

平面影像数据到四元三角网(Quaternary Triangular Mesh,QTM)像元的转换是实现全球QTM格网影像无缝建模的首要任务。提出基于面积权的数据转换方法,给出不同类型的权重及格网值的计算,并利用ArcGIS中256级灰度的Wsiearth.tif图像数据分析了转换精度。结果表明:将4 km分辨率的影像数据转换到12层的QTM像元时,转换误差在0~2灰度内占96.27%,对一般应用的影响可以忽略不计。     

基于粒子群优化算法的PSO-BP海底声学底质分类方法

利用粒子群优化算法（PSO）较强的鲁棒性和全局搜索能力等优点,将PSO算法与BP神经网络相结合,优化了BP神经网络分类时的初始权值和阈值。基于珠江河口三角洲的侧扫声呐图像数据,提取了海底声呐图像中砂、礁石、泥3类典型底质的6种主要特征向量,利用PSO-BP方法对海底底质进行分类识别。实验表明,3类底质分类精度均大于90%,高于BP神经网络70%左右的分类精度,表明PSO-BP方法可有效应用于海底底质的分类识别。     

Comparison of the performance of flow‐routing algorithms used in GIS‐based hydrologic analysis

Flow direction and specific catchment area were calculated for different flow‐routing algorithms using TAPES‐G and TauDEM. A fuzzy classification was used along with eight topo–climatic attributes to delineate six landscape classes from a 10‐m USGS DEM. A series of maps and tabular outputs were produced to compare flow‐routing predictions in different parts of the study area in the Santa Monica Mountains of southern California. The matched pair t‐test was used to compare the performance of pairs of specific catchment area grids across six user‐defined fuzzy landscape classes. The results show that (1) the ‘source’ cells predicted with the D∞, DEMON, and FD8 algorithms were confined to hilltops; (2) two single flow‐routing algorithms (Rho8, D8) produced poor results; and (3) the choice of flow‐routing algorithm has potentially important consequences for the calculation of upslope contributing areas, sediment transport capacity, topographic wetness, and several other topographic indices. Copyright © 2007 John Wiley & Sons, Ltd.     

Rational function optimization using genetic algorithms

In the absence of either satellite ephemeris information or camera model, rational functions are introduced by many investigators as mathematical model for image to ground coordinate system transformation. The dependency of this method on many ground control points (GCPs), numerical complexity, particularly terms selection, can be regarded as the most known disadvantages of rational functions. This paper presents a mathematical solution to overcome these problems. Genetic algorithms are used as an intelligent method for optimum rational function terms selection. The results from an experimental test carried out over a test field in Iran are presented as utilizing an IKONOS Geo image. Different numbers of GCPs are fed through a variety of genetic algorithms (GAs) with different control parameter settings. Some initial constraints are introduced to make the process stable and fast. The residual errors at independent check points proved that sub-pixel accuracies can be achieved even when only seven and five GCPs are used. GAs could select rational function terms in such a way that numerical problems are avoided without the need to normalize image and ground coordinates.