ALBERT

Description: This article aims to develop a Web-GIS based landslide early warning system (EWS) for the Chittagong Metropolitan Area (CMA), Bangladesh, where, in recent years, rainfall-induced landslides have caused great losses of lives and property. A method for combining static landslide susceptibility maps and rainfall thresholds is proposed by introducing a purposely-build hazard matrix. To begin with, eleven factor maps: soil permeability; surface geology; landcover; altitude; slope; aspect; distance to stream; fault line; hill cut; road cut; and drainage network along with a detailed landslide inventory map were produced. These maps were used, and four methods were applied: artificial neural network (ANN); multiple regressions; principal component analysis; and support vector machine to produce landslide susceptibility maps. After model validation, the ANN map was found best fitting and was classified into never warning, low, medium, and high susceptibility zones. Rainfall threshold analysis (1960–2017) revealed consecutive 5-day periods of rainfall of 71–282 mm could initiate landslides in CMA. Later, the threshold was classified into three rainfall rates: low rainfall (70–160 mm), medium rainfall (161–250 mm), and high rainfall (〉250 mm). Each landslide was associated with a hazard class (no warning vs. warning state) based on the assumption that the higher the susceptibility, the lower the rainfall. Finally, the EWS was developed using various libraries and frameworks that is connected with a reliable online-based weather application programming interface. The system is publicly available, dynamic, and replicable to similar contexts and is able to disseminate alerts five days in advance via email notifications. The proposed EWS is novel and the first of its kind in Bangladesh, and can be applied to mitigate landslide disaster risks.

Description: Many studies have empirically confirmed the relationship between urbanization and changes to the hydrologic cycle and degraded aquatic habitats. While much of the literature focuses on extent and configuration of impervious area as a causal determinant of degradation, in this article I do not attribute causes of decreased watershed storage on impervious area a priori . Rather, adapting the concept of variable source area (VSA) and its relationship to incremental storage to the particular conditions of urbanized catchments, I develop a statistically-robust linear regression-based methodology to detect evidence of VSA-dominant response. Using the physical and meteorological characteristics of the catchments as explanatory variables, I then use logistic regression to statistically analyze significant predictors of the VSA classification. I find that the strongest predictor of VSA-type response is the percent of undeveloped area in the catchment. Characteristics of developed areas, including total impervious area, percent developed open space, and the type of drainage infrastructure do not add to the explanatory power of undeveloped land in predicting VSA-type response. Within only developed areas, I find that total impervious area (TIA) and percent developed open space both decrease the odds of a catchment exhibiting evidence of VSA-type response and the effect of developed open space is more similar to that of TIA than undeveloped land in predicting VSA response. Different types of stormwater management infrastructure, including combined sewer systems (CSS) and infiltration, retention, and detention infrastructure are not found to have strong statistically significant effects on probability of VSA-type response. VSA-type response is also found to be stronger during the growing season than the dormant season. These findings are consistent across a national cross-section of urbanized watersheds, a higher resolution dataset of Baltimore Metropolitan Area watersheds, and a subsample of watersheds confirmed not to be served by (CSS). This article is protected by copyright. All rights reserved.

Description: With the rapid development of global positioning technologies and the pervasiveness of intelligent mobile terminals, trajectory data have shown a sharp growth trend both in terms of data volume and coverage. In recent years, increasing numbers of LBS (location based service) applications have provided us with trajectory data services such as traffic flow statistics and user behavior pattern analyses. However, the storage and query efficiency of massive trajectory data are increasingly creating a bottleneck for these applications, especially for large-scale spatiotemporal query scenarios. To solve this problem, we propose a new spatiotemporal indexing method to improve the query efficiency of massive trajectory data. First, the method extends the GeoSOT spatial partitioning scheme to the time dimension and forms a global space–time subdivision scheme. Second, a novel multilevel spatiotemporal grid index, called the GeoSOT ST-index, was constructed to organize trajectory data hierarchically. Finally, a spatiotemporal range query processing method is proposed based on the index. We implement and evaluate the index in MongoDB. By comparing the range query efficiency and scalability of our index with those of the other two space–time composite indexes, we found that our approach improves query efficiency levels by approximately 40% and has better scalability under different data volumes.

Description: Abstract Actual pumping tests may involve continuously decreasing rates over a certain period of time, and the hydraulic conductivity (K) and specific storage (Ss) of the tested confined aquifer cannot be interpreted from the classical constant‐rate test model. In this study, we revisit the aquifer drawdown characteristics of a pumping test with an exponentially decreasing rate using the dimensionless analytical solution for such a variable‐rate model. The drawdown may decrease with time for a short period of time at intermediate pumping times for such pumping tests. A larger ratio of initial to final pumping rate and a smaller radial distance of the observation well will enhance the decreasing feature. A larger decay constant results in an earlier decrease, but it weakens the extent of such a decrease. Based on the proposed dimensionless transformation, we have proposed two graphical methods for estimating K and Ss of the tested aquifer. The first is a new type curve method that does not employ the well function as commonly done in standard type curve analysis. Another is a new analytic method that takes advantage of the decreasing features of aquifer drawdown during the intermediate pumping stage. We have demonstrated the applicability and robustness of the two new graphical methods for aquifer characterization through a synthetic pumping test.

Description: In our previous study, Faulkner et al. (2009) developed a Stokes-Darcy (SD) model for flow in a karst aquifer with a conduit bedded in matrix. Beavers-Joseph (BJ) interface condition was used to describe the matrix/conduit interface. Cao et al. (2010) studied the mathematical well-posedness of a coupled continuum pipe-flow (CCPF) model as well as convergence rates of its finite element approximation. In this study, to compare the SD model with the CCPF model, we use numerical analyses to validate finite element discretization methods for the two models. Using computational experiments, simulation codes implementing the finite element discretizations are then verified. Further model validation studies are based on the results of laboratory experiments. Comparing the results of computer simulations and experiments, we conclude that the SD model with the BJ interface condition is a valid model for conduit/matrix systems. On the other hand, the CCPF model with the value of the exchange parameter chosen within the range suggested in the literature perhaps does not result in good agreement with experimental observations. We then examine the sensitivity of the CCPF model with respect to the exchange parameter, concluding that, as has previously been noted, the model is highly sensitive for small values of the exchange parameter. However, for larger values, the model becomes less sensitive and, more important, also produces results that are in better agreement with experimental observations. This suggests that the CCPF model may also produce accurate simulation results, if one chooses larger values of the exchange parameter than those suggested in the literature. Copyright © 2011 John Wiley & Sons, Ltd.

Description: This study presents an analytical solution of dam-break floods in a trapezoidal channel with detailed solution procedure. An approach predicting the peak discharge of floods caused by embankment dam failures was derived from the aforementioned analytical solution with a database of 27 historical dam failures. The prediction performance of this approach has been proved by comparing with other 14 straightforward equations for estimating the peak discharge. The proposed model with a small uncertainty of predicted peak flow rates, has a high coefficient of determination and a small standard error, being ranked in the top four of the 15 methods considered in this paper. The robustness and predictive capability of the proposed model are further demonstrated in two case studies, both were considered in the previous analyses performed by other investigators. This method provides a simple and transparent tool for engineers to predict the peak discharge and is easy to implement for trial and error calculation. This article is protected by copyright. All rights reserved.

Description: IJGI, Vol. 7, Pages 158: Using the TensorFlow Deep Neural Network to Classify Mainland China Visitor Behaviours in Hong Kong from Check-in Data ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7040158 Authors: Shanshan Han Fu Ren Chao Wu Ying Chen Qingyun Du Xinyue Ye Over the past decade, big data, including Global Positioning System (GPS) data, mobile phone tracking data and social media check-in data, have been widely used to analyse human movements and behaviours. Tourism management researchers have noted the potential of applying these data to study tourist behaviours, and many studies have shown that social media check-in data can provide new opportunities for extracting tourism activities and tourist behaviours. However, traditional methods may not be suitable for extracting comprehensive tourist behaviours due to the complexity and diversity of human behaviours. Studies have shown that deep neural networks have outpaced the abilities of human beings in many fields and that deep neural networks can be explained in a psychological manner. Thus, deep neural network methods can potentially be used to understand human behaviours. In this paper, a deep learning neural network constructed in TensorFlow is applied to classify Mainland China visitor behaviours in Hong Kong, and the characteristics of these visitors are analysed to verify the classification results. For the social science classification problem investigated in this study, the deep neural network classifier in TensorFlow provides better accuracy and more lucid visualisation than do traditional neural network methods, even for erratic classification rules. Furthermore, the results of this study reveal that TensorFlow has considerable potential for application in the human geography field.

Description: IJGI, Vol. 7, Pages 214: Trend Analysis of Relationship between Primary Productivity, Precipitation and Temperature in Inner Mongolia ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7060214 Authors: Tianyang Chen Yichun Xie Chao Liu Yongfei Bai Anbing Zhang Lishen Mao Siyu Fan This study mainly examined the relationships among primary productivity, precipitation and temperature by identifying trends of change embedded in time-series data. The paper also explores spatial variations of the relationship over four types of vegetation and across two precipitation zones in Inner Mongolia, China. Traditional analysis of vegetation response to climate change uses minimum, maximum, average or cumulative measurements; focuses on a whole region instead of fine-scale regional or ecological variations; or adopts generic analysis techniques. We innovatively integrate Empirical Mode Decomposition (EMD) and Redundancy Analysis (RDA) to overcome the weakness of traditional approaches. The EMD filtered trend surfaces reveal clear patterns of Enhanced Vegetation Index (EVI), precipitation, and temperature changes in both time and space. The filtered data decrease noises and cyclic fluctuations in the original data and are more suitable for examining linear relationship than the original data. RDA is further applied to reveal partial effect of precipitation and temperature, and their joint effect on primary productivity. The main findings are as follows: (1) We need to examine relationships between the trends of change of the variables of interest when investigating long-term relationships among them. (2) Long-term trend of change of precipitation or temperature can become a critical factor influencing primary productivity depending on local environments. (3) Synchronization (joint effect) of precipitation and temperature in growing season is critically important to primary productivity in the study area. (4) Partial and joint effects of precipitation and temperature on primary productivity vary over different precipitation zones and different types of vegetation. The method developed in this paper is applicable to ecosystem research in other regions.

Description: Soil macropore networks are subsurface connected void spaces caused by processes such as fracture of soils, micro-erosion, and fauna burrows. Axial X-ray computed tomography (CT) scanning provides a convenient means of recording the spatial structure of soil macropore networks. The objective of this study were to (1) based on CT technique and GIS digitized image method, construction a new technique for tracing, visualizing and measuring the soil macropore networks; (2) investigate the effects of farming activities on soil macropore networks characteristics. Our technique using left-turning and nine-direction judgment methods, a combination of the layer-by-layer analysis method and the up-down tracking algorithm. The characteristics for the overall structure patterns of macropores, the spatial distribution of the macropore networks and each single macropore network can conveniently identify by our technique. Eight undisturbed soil columns from fields with two distinct land uses (under cultivation and not been cultivated) and four different depths (0-20 cm, 20-40 cm, 40-60 cm and 60-80 cm) were investigated. The soil columns were scanned using X-ray CT at a voxel resolution of 0.075 mm × 0.075 mm × 3.000 mm. Results indicate that farming activities can destroy the initial structure of macropores and those remaining are mainly small and medium-sized networks with lower extension and hydraulic conductivity. The network properties show a significant difference between upper and lower layer. The results can provide beneficial reference to further research centered on non-equilibrium flow prediction and chemical transport modeling in field soils. This article is protected by copyright. All rights reserved.

Description: IJGI, Vol. 6, Pages 269: Consistent Roof Geometry Encoding for 3D Building Model Retrieval Using Airborne LiDAR Point Clouds ISPRS International Journal of Geo-Information doi: 10.3390/ijgi6090269 Authors: Yi-Chen Chen Bo-Yi Lin Chao-Hung Lin A 3D building model retrieval method using airborne LiDAR point clouds as input queries is introduced. Based on the concept of data reuse, available building models in the Internet that have geometric shapes similar to a user-specified point cloud query are retrieved and reused for the purpose of data extraction and building modeling. To retrieve models efficiently, point cloud queries and building models are consistently and compactly encoded by the proposed method. The encoding focuses on the geometries of building roofs, which are the most informative part of a building in airborne LiDAR acquisitions. Spatial histograms of geometric features that describe shapes of building roofs are utilized as shape descriptor, which introduces the properties of shape distinguishability, encoding compactness, rotation invariance, and noise insensitivity. These properties facilitate the feasibility of the proposed approaches for efficient and accurate model retrieval. Analyses on LiDAR data and building model databases and the implementation of web-based retrieval system, which is available at http://pcretrieval.dgl.xyz, demonstrate the feasibility of the proposed method to retrieve polygon models using point clouds.