Quantifying flows along hydrological pathways by applying a new filtering algorithm in conjunction with master recession curve analysis

Quantifying the proportion of the river hydrograph derived from the different hydrological pathways is essential for understanding the behaviour of a catchment. This paper describes a new approach using the output from master recession curve analysis to inform a new algorithm based on the Lyne and Hollick ‘one‐parameter’ signal analysis filtering algorithm. This approach was applied to six catchments (including two subcatchments of these) in Ireland. The conceptual model for each catchment consists of four main flow pathways: overland flow, interflow, shallow groundwater and deep groundwater. The results were compared with those of the master recession curve analysis, a recharge coefficient approach developed in Ireland and the semi‐distributed, lumped and deterministic hydrological model Nedbør‐Afstrømings‐Model. The new algorithm removes the ‘free variable’ aspect that is typically associated with filtering algorithms and provides a means of estimating the contribution of each pathway that is consistent with the results of hydrograph separation in catchments that are dominated by quick response pathways. These types of catchments are underlain by poorly productive aquifers that are not capable of providing large baseflows in the river. Such aquifers underlie over 73% of Ireland, ensuring that this new algorithm is applicable in the majority of catchments in Ireland and potentially in those catchments internationally that are strongly influenced by the quick‐responding hydrological pathways. Copyright © 2013 John Wiley & Sons, Ltd.     

Retaining wall optimization using interior search algorithm with different bound constraint handling

Along with the applicability of optimization algorithms, there are lots of features that can affect the functioning of the optimization techniques. The main purpose of this paper is investigating the significance of boundary constraint handling (BCH) schemes on the performance of optimization algorithms. To this end, numbers of deterministic and probabilistic BCH approaches are applied to one of the most recent proposed optimization techniques, named interior search algorithm (ISA). Apart from the implementing different BCH methods, a sensitivity analysis is conducted to find an appropriate setting for the only parameter of ISA. Concrete cantilever retaining wall design as one of the most important geotechnical problems is tackled to declare proficiency of the ISA algorithm, on the one hand, and benchmark the effect of BCH schemes on the final results, on the contrary. As results demonstrate, various BCH approaches have a perceptible impact on the algorithm performance. In like manner, the essential parameter of ISA can also play a pivotal role in this algorithm's efficiency. Copyright © 2017 John Wiley & Sons, Ltd.     

Landform‐oriented flow‐routing algorithm for the dual‐structure loess terrain based on digital elevation models

The loess landform in the Loess Plateau of China is with typical dual structure, namely, the upper smooth positive terrain and the lower cliffy negative terrain (P–N terrain for short). Obvious differences in their morphological feature, geomorphological mechanism, and hydrological process could be found in the both areas. Based on the differences, a flow‐routing algorithm that separately addresses the dual‐structure terrain would be necessary to encompass this spatial variation in their hydrological behaviour. This paper proposes a mixed flow‐routing algorithm to address aforementioned problems. First, the loess landform surface is divided into P–N terrains based on digital elevation models. Then, specific catchment area is calculated with the new algorithm to simulate the water flows in both positive and negative terrain areas. The mixed algorithm consists of the multiple flow‐routing algorithm (multiple‐flow direction) for positive areas and the D8 algorithm for negative areas, respectively. The approach is validated in two typical geomorphologic areas with low hills and dense gullies in the northern Shaanxi Loess Plateau. Four indices are used to examine the results, which show that the new algorithm is more suitable for loess terrain in simulating the spatial distribution of water accumulation, as well as in modeling the flow characteristics of the true surface by considering the morphological structures of the terrain. Copyright © 2013 John Wiley & Sons, Ltd.     

Slope stability analysis using evolutionary optimization techniques

Slope stability optimization, in the presence of a band of a weak layer between two strong layers, is accounted for in complicated geotechnical problems. Classical optimization algorithms are not suitable for solving such problems as they need a proper preliminary solution to converge to a valid result. Therefore, it is necessary to find a proper algorithm which is capable of finding the best global solution. Recently a lot of metaheuristic algorithms have been proposed which are able to evade local minima effectively. In this study four evolutionary algorithms, including well‐known and recent ones, such as genetic algorithm, differential evolution, evolutionary strategy and biogeography‐based optimization (BBO), are applied in slope stability analysis and their efficiencies are explored by three benchmark case studies. Result show BBO is the most efficient among these evolutionary algorithms and other proposed algorithms applied to this problem. Copyright © 2016 John Wiley & Sons, Ltd.     

Simulation of site-scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China

The study of water fluxes is important to better understand hydrological cycles in arid regions. Data-driven machine learning models have been recently applied to water flux simulation. Previous studies have built site-scale simulation models of water fluxes for individual sites separately, requiring a large amount of data from each site and significant computation time. For arid areas, there is no consensus as to the optimal model and variable selection method to simulate water fluxes. Using data from seven flux observation sites in the arid region of Northwest China, this study compared the performance of random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), and multiple linear regression (MLR) models in simulating water fluxes. Additionally, the study investigated inter-annual and seasonal variation in water fluxes and the dominant drivers of this variation at different sites. A universal simulation model for water flux was built using the RF approach and key variables as determined by MLR, incorporating data from all sites. Model performance of the SVM algorithm (R² = 0.25–0.90) was slightly worse than that of the RF algorithm (R² = 0.41–0.91); the BPNN algorithm performed poorly in most cases (R² = 0.15–0.88). Similarly, the MLR results were limited and unreliable (R² = 0.00–0.66). Using the universal RF model, annual water fluxes were found to be much higher than the precipitation received at each site, and natural oases showed higher fluxes than desert ecosystems. Water fluxes were highest during the growing season (May–September) and lowest during the non-growing season (October–April). Furthermore, the dominant drivers of water flux variation were various among different sites, but the normalized difference vegetation index (NDVI), soil moisture and soil temperature were important at most sites. This study provides useful insights for simulating water fluxes in desert and oasis ecosystems, understanding patterns of variation and the underlying mechanisms. Besides, these results can make a contribution as the decision-making basis to the water management in desert and oasis ecosystems.     

Site characterization model using least‐square support vector machine and relevance vector machine based on corrected SPT data (Nc)

Statistical learning algorithms provide a viable framework for geotechnical engineering modeling. This paper describes two statistical learning algorithms applied for site characterization modeling based on standard penetration test (SPT) data. More than 2700 field SPT values (N) have been collected from 766 boreholes spread over an area of 220 sqkm area in Bangalore. To get N corrected value (N_c), N values have been corrected (N_c) for different parameters such as overburden stress, size of borehole, type of sampler, length of connecting rod, etc. In three‐dimensional site characterization model, the function N_c=N_c (X, Y, Z), where X, Y and Z are the coordinates of a point corresponding to N_c value, is to be approximated in which N_c value at any half‐space point in Bangalore can be determined. The first algorithm uses least‐square support vector machine (LSSVM), which is related to a ridge regression type of support vector machine. The second algorithm uses relevance vector machine (RVM), which combines the strengths of kernel‐based methods and Bayesian theory to establish the relationships between a set of input vectors and a desired output. The paper also presents the comparative study between the developed LSSVM and RVM model for site characterization. Copyright © 2009 John Wiley & Sons, Ltd.     

迁移学习支持下的高分影像山地滑坡灾害解译模型

无人机低空遥感是近年来新兴的一种快速获取灾情信息的手段,如何利用无人机高分影像构建滑坡灾害解译模型是实现快速自动解译滑坡的关键。针对该问题,对比了多种影像特征提取方法,将迁移学习(TL)特征和支持向量机(SVM)引入到构建滑坡灾害自动解译模型中,提出了一种TL支持下的高分影像滑坡灾害解译模型。选取5·12汶川地震及4·20芦山地震系列无人机影像构建了滑坡灾害样本库并进行了实验,TL特征方法整体分类准确度ACC为95%,ROC达到0.98,识别准确率达到97%。结果表明,所提方法可用于高分影像滑坡自动解译,同时可用于大面积高分影像中快速山地滑坡灾害定位及检测。     

利用多光谱卫星遥感和深度学习方法进行青藏高原积雪判识

青藏高原积雪对全球气候变化十分重要,针对已有积雪遥感判识方法中普遍采用的可见光与红外光谱数据易受复杂地形与高海拔影响,导致青藏高原地区积雪判识精度较低的问题,提出了一种基于多光谱遥感与地理信息数据特征级融合的积雪遥感判识方法:以风云三号卫星可见光与红外多光谱遥感资料与多要素地理信息作为数据源,由地面实测雪深数据与现有积雪产品交叉筛选出样本标签,构建并训练基于层叠去噪自编码器(SDAE)的特征融合与分类网络,从而有效辨识青藏高原遥感图像中的云、积雪以及无雪地表。经地面实测雪深数据验证,该方法分类精度显著高于使用相同数据源的FY-3A/MULSS积雪产品,略高于国际主流积雪产品MOD10A1与MYD10A1,并且年均云覆盖率最低。试验结果表明该方法可有效地减少云层对积雪判识的干扰,提升分类精度。     

历史数据和强化学习相结合的低频轨迹数据匹配算法

针对低频(采样间隔大于1min)轨迹数据匹配算法精度不高的问题,提出了一种基于强化学习和历史轨迹的匹配算法HMDP-Q,首先通过增量匹配算法提取历史路径作为历史参考经验库;根据历史参考经验库、最短路径和可达性筛选候选路径集;再将地图匹配过程建模成马尔科夫决策过程,利用轨迹点偏离道路距离和历史轨迹构建回报函数;然后借助强化学习算法求解马尔科夫决策过程的最大回报值,即轨迹与道路的最优匹配结果;最后应用某市浮动车轨迹数据进行试验。结果表明:本文算法能有效提高轨迹数据与道路匹配精度;本算法在1min低频采样间隔下轨迹匹配准确率达到了89.2%;采样频率为16min时,该算法匹配精度也能达到61.4%;与IVVM算法相比,HMDP-Q算法匹配精度和求解效率均优于IVVM算法,16min采样频率时本文算法轨迹匹配精度提高了26%。     

思维导图方法的教学实践

思维导图在国内外已有多项研究证明思维导图在促进学生创新思维发展上有作用。在测绘类学科的高校教学中引入思维导图这种学习和工作方法,用在测绘本科教学改革中的讲座责任制授课模式上,通过在测绘本科教学方法改革的需求分析、应用实践和学生调查统计,得出结论:思维导图用于测绘类高校教学中,有利于提高学生创新思辨和自主学习能力,有助于达到以"能力"为核心的测绘专业人才培养目标。