Assessment of sediment transport approaches for sand-bed rivers by means of machine learning

Abstract

The quantification of the sediment carrying capacity of a river is a difficult task that has received much attention. For sand-bed rivers especially, several sediment transport functions have appeared in the literature based on various concepts and approaches; however, since they present a significant discrepancy in their results, none of them has become universally accepted. This paper employs three machine learning techniques, namely artificial neural networks, symbolic regression based on genetic programming and an adaptive-network-based fuzzy inference system, for the derivation of sediment transport formulae for sand-bed rivers from field and laboratory flume data. For the determination of the input parameters, some of the most prominent fundamental approaches that govern the phenomenon, such as shear stress, stream power and unit stream power, are utilized and a comparison of their efficacy is provided. The results obtained from the machine learning techniques are superior to those of the commonly-used sediment transport formulae and it is shown that each of the input combinations tested has its own merit, as they produce similarly good results with respect to the data-driven technique employed.

Editor Z.W. Kundzewicz     

Automatic sub-pixel co-registration of Landsat-8 Operational Land Imager and Sentinel-2A Multi-Spectral Instrument images using phase correlation and machine learning based mapping

ABSTRACT

This study investigates misregistration issues between Landsat-8/ Operational Land Imager and Sentinel-2A/ Multi-Spectral Instrument at 30?m resolution, and between multi-temporal Sentinel-2A images at 10?m resolution using a phase-correlation approach and multiple transformation functions. Co-registration of 45 Landsat-8 to Sentinel-2A pairs and 37 Sentinel-2A to Sentinel-2A pairs were analyzed. Phase correlation proved to be a robust approach that allowed us to identify hundreds and thousands of control points on images acquired more than 100 days apart. Overall, misregistration of up to 1.6 pixels at 30?m resolution between Landsat-8 and Sentinel-2A images, and 1.2 pixels and 2.8 pixels at 10?m resolution between multi-temporal Sentinel-2A images from the same and different orbits, respectively, were observed. The non-linear random forest regression used for constructing the mapping function showed best results in terms of root mean square error (RMSE), yielding an average RMSE error of 0.07?±?0.02 pixels at 30?m resolution, and 0.09?±?0.05 and 0.15?±?0.06 pixels at 10?m resolution for the same and adjacent Sentinel-2A orbits, respectively, for multiple tiles and multiple conditions. A simpler 1st order polynomial function (affine transformation) yielded RMSE of 0.08?±?0.02 pixels at 30?m resolution and 0.12?±?0.06 (same Sentinel-2A orbits) and 0.20?±?0.09 (adjacent orbits) pixels at 10?m resolution.     

Locating day-labor employment: toward a geographic understanding of day-labor hiring site locations in the San Diego metropolitan area

Day-labor hiring sites are common features of the contemporary North American landscape. These are public and semi-public spaces where mostly male residents congregate daily in hopes of being hired for short-term work. Research on contemporary day-labor markets in the United States to date tends to be policy-oriented, intended to reduce the injustices that are a common part of life as a day laborer. Unfortunately, very little is understood about the spatial organization of day-labor markets. Drawing on more than five years of mixed-methods research in the San Diego Metropolitan Area (SDMA), this paper takes two important steps toward a spatial understanding of day-labor hiring sites. First, it demonstrates that informal hiring sites are established in locations that maximize laborers’ chances of finding employment. Second, it establishes a geo-spatial typology of hiring sites for the SDMA that can be used to better tailor day-labor support efforts and policy to site-level context.     

Neighborhood Factors Associated with Same-Sex Households in U.S. Cities

Quality of life cannot be fully described by the availability of regional amenities. Instead, it also depends on the quality of neighborhoods and housing. In connection with discussions on knowledge workers, not much systematic research has been done on the characteristics of neighborhoods characterized by high proportions of same-sex households. By analyzing census tracts within select counties across the United States using Poisson regressions, this article investigates what factors are related to the number of same-sex households.     

The Discursive Regime of the "American Dream" and the New Suburban Frontier: The Case of Kendall County,Illinois

This study examines the current role of ideology in maintaining and reproducing consumer demand for residential suburban spaces in the outer ring of American metropolitan regions. It highlights the contradiction between the promises that are made through the ideology and discourse of the American Dream and the inherent limits of those promises within the realities of a highly dynamic capitalist society. It is argued that this powerful discursive formation has evolved through recent neoliberal times, and is manifest in the suburban landscape in new ways. It must continue to produce simulated images that signify mythologized narratives of the past that cater to sentiments of hope and a better life in the future. At the same time, it works through existing fears and anxieties in order to secure the progressive accumulation of capital in the suburban built environment. Evidence will be provided from the ongoing suburbanization of Kendall County, Illinois at the southwestern edge of metropolitan Chicago.     

An Algorithm for Localised Contour Removal over Steep Terrain

Abstract

Isolines have proved to be a highly effective way of conveying the shape of a surface (most commonly in the form of height contours to convey geographical landscape). Selecting the right contour interval is a compromise between showing sufficient detail in flat regions, whilst avoiding excessive crowding of lines in steep and morphologically complex areas. The traditional way of avoiding coalescence and confusion across steep regions has been to manually remove short sections of intermediate contours, while retaining index contours. Incorporating humans in automated environments is not viable. This research reports on the design, implementation and evaluation of an automated solution to this problem involving the automatic identification of coalescing lines, and removal of line segments to ensure clarity in the interpretation of contour information. Evaluation was made by subjective comparison with Ordnance Survey products. The results were found to be very close to the quality associated with manual techniques.     

A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment

Abstract

A novel artificial intelligence approach of Bayesian Logistic Regression (BLR) and its ensembles [Random Subspace (RS), Adaboost (AB), Multiboost (MB) and Bagging] was introduced for landslide susceptibility mapping in a part of Kamyaran city in Kurdistan Province, Iran. A spatial database was generated which includes a total of 60 landslide locations and a set of conditioning factors tested by the Information Gain Ratio technique. Performance of these models was evaluated using the area under the ROC curve (AUROC) and statistical index-based methods. Results showed that the hybrid ensemble models could significantly improve the performance of the base classifier of BLR (AUROC?=?0.930). However, RS model (AUROC?=?0.975) had the highest performance in comparison to other landslide ensemble models, followed by Bagging (AUROC?=?0.972), MB (AUROC?=?0.970) and AB (AUROC?=?0.957) models, respectively.     

Elements of viral cartography

ABSTRACT

Making and sharing maps is easier than ever, and social media platforms make it possible for maps to rapidly attain widespread visibility and engagement. Such maps can be considered examples of viral cartography – maps that reach rapid popularity via social media dissemination. In this research we propose a framework for evaluating the design and social dissemination characteristics of viral maps. We apply this framework in two case studies using maps that reached wide audiences on Twitter. We then analyze collections of maps derived from and inspired by viral maps using image analysis and machine learning to characterize their design elements. Based on our initial work to conceptualize and analyze virality in cartography, we propose a set of new research challenges to better understand viral mapmaking and leverage its social affordances.     

A Machine Learning-based Cloud Detection Algorithm for the Himawari-8 Spectral Image

Cloud Masking is one of the most essential products for satellite remote sensing and downstream applications. This study develops machine learning-based (ML-based) cloud detection algorithms using spectral observations for the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite. Collocated active observations from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used to provide reference labels for model development and validation. We introduce both daytime and nighttime algorithms that differ according to whether solar band observations are included, and the artificial neural network (ANN) and random forest (RF) techniques are adopted for comparison. To eliminate the influences of surface conditions on cloud detection, we introduce three models with different treatments of the surface. Instead of developing independent ML-based algorithms, we add surface variables in a binary way that enhances the ML-based algorithm accuracy by ~5%. Validated against CALIOP observations, we find that our daytime RF-based algorithm outperforms the AHI operational algorithm by improving the accuracy of cloudy pixel detection by ~5%, while at the same time, reducing misjudgment by ~3%. The nighttime model with only infrared observations is also slightly better than the AHI operational product but may tend to overestimate cloudy pixels. Overall, our ML-based algorithms can serve as a reliable method to provide cloud mask results for both daytime and nighttime AHI observations. We furthermore suggest treating the surface with a set of independent variables for future ML-based algorithm development.     

风机节能技术应用与经济性对比

主要针对300MW级循环流化床锅炉一、二次离心风机的几种调速节能方式进行比较,针对电厂普遍采用的变频器和液力耦合器调速的经济性进行了探讨、对比。