scholarly journals A Clustering Approach for Predicting Dune Morphodynamic Response to Storms Using Typological Coastal Profiles: A Case Study at the Dutch Coast

2021 ◽  
Vol 8 ◽  
Author(s):  
Panagiotis Athanasiou ◽  
Ap van Dongeren ◽  
Alessio Giardino ◽  
Michalis Vousdoukas ◽  
Jose A. A. Antolinez ◽  
...  
Keyword(s):  
Early Warning Systems ◽  
Efficient Estimation ◽  
Good Prediction ◽  
Clustering Methods ◽  
Large Spatial Scale ◽  
Dune Erosion ◽  
Dutch Coast ◽  
Elevation Data ◽  
Offshore Wave

Dune erosion driven by extreme marine storms can damage local infrastructure or ecosystems and affect the long-term flood safety of the hinterland. These storms typically affect long stretches (∼100 km) of sandy coastlines with variable topo-bathymetries. The large spatial scale makes it computationally challenging for process-based morphological models to be used for predicting dune erosion in early warning systems or probabilistic assessments. To alleviate this, we take a first step to enable efficient estimation of dune erosion using the Dutch coast as a case study, due to the availability of a large topo-bathymetric dataset. Using clustering techniques, we reduce 1,430 elevation profiles in this dataset to a set of typological coastal profiles (TCPs), that can be employed to represent dune erosion dynamics along the whole coast. To do so, we use the topo-bathymetric profiles and historic offshore wave and water level conditions, along with simulations of dune erosion for a number of representative storms to characterize each profile. First, we identify the most important drivers of dune erosion variability at the Dutch coast, which are identified as the pre-storm beach geometry, nearshore slope, tidal level and profile orientation. Then using clustering methods, we produce various sets of TCPs, and we test how well they represent dune morphodynamics by cross-validation on the basis of a benchmark set of dune erosion simulations. We find good prediction skill (0.83) with 100 TCPs, representing a 93% input and associated computational costs reduction. These TCPs can be used in a probabilistic model forced with a range of offshore storm conditions, enabling national scale coastal risk assessments. Additionally, the presented techniques could be used in a global context, utilizing elevation data from diverse sandy coastlines to obtain a first order prediction of dune erosion around the world.

scholarly journals Intrabasin Floral Differences in the Sonoran Desert; a case study from the Casa Grande Valley

2014 ◽  
Author(s):  
Gabriela Garcia ◽  
Sarah Gabriele ◽  
Benjamin Cowgill ◽  
Xavier Rodriguez ◽  
Robert J Gay
Keyword(s):  
Community Composition ◽  
Soil Ph ◽  
Sonoran Desert ◽  
Mineral Content ◽  
Controlling Factors ◽  
Floral Variation ◽  
Significant Difference ◽  
Elevation Data ◽  
Expected Values

Background: The purpose of this study was to determine what floral differences exist in North Mountain Park and Casa Grande Mountain Park which are both located on opposite sides of the Casa Grande Valley, Pinal County, Arizona and to attempt to explain any measured differences. Previous authors have proposed several explanations for floral variation within the Sonoran Desert including elevation, soil pH, and mineral content. This study explicitly tests several of these proposed mechanisms for determining community composition. Methods: The floral composition was measured in both North Mountain Park and Casa Grande Mountain Park through a series of transects which were sampled by multiple times in 2012 and 2013. Elevation data soil pH were also sampled. Results: The data recovered from North Mountain Park differed from the expected values in Casa Grande Mountain Park by 22%. This indicates a significant difference in the flora between these two localities that was not predicted by earlier studies. Elevation and soil pH differences between sampled localities were not significant. This suggests that mineral composition of the soil may play an important role within this basin in determining community composition. Discussion: Many factors that have been proposed in prior studies do not appear to play a significant role within the Casa Grande Valley in determining community composition. This indicates that the composition of a community is influenced by different factors in different locations within the Sonoran Desert. This makes determining overall controlling factors across an ecosystem difficult.

scholarly journals Wave-Induced Sediment Motion Beyond the Surf Zone: Case Study of Lubiatowo (Poland)

2015 ◽  
Vol 62 (1-2) ◽  
pp. 27-39 ◽  
Author(s):  
Grzegorz R. Cerkowniak ◽  
Rafał Ostrowski ◽  
Magdalena Stella
Keyword(s):  
Surf Zone ◽  
Shear Stresses ◽  
Research Station ◽  
Wave Parameters ◽  
Sea Bottom ◽  
Theoretical Investigations ◽  
Deep Water Wave ◽  
Offshore Wave ◽  
Wave Induced

AbstractThe paper presents results of field and theoretical investigations of a natural sandy shore located near the IBW PAN Coastal Research Station in Lubiatowo (Poland, the south Baltic Sea). The study site displays multi-bar cross-shore profiles that intensively dissipate wave energy, mostly by breaking. The main field data comprise offshore wave parameters and three cross-shore bathymetric profiles. Waveinduced nearbed velocities and bed shear stresses are theoretically modelled for weak, moderate, strong and extreme storm conditions to determine sediment motion regimes at various locations on the seaward boundary of the surf zone. The paper contains a discussion on the depth of closure concept, according to which the offshore range of sea bottom changes can be determined by the extreme seasonal deep-water wave parameters.

scholarly journals A novel computational model for predicting potential LncRNA-disease associations based on both direct and indirect features of LncRNA-disease pairs

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Yubin Xiao ◽  
Zheng Xiao ◽  
Xiang Feng ◽  
Zhiping Chen ◽  
Linai Kuang ◽  
...  
Keyword(s):  
Computational Model ◽  
Cross Validation ◽  
State Of The Art ◽  
Prediction Methods ◽  
Good Prediction ◽  
Average Case ◽  
Comparison Results ◽  
Disease Associations ◽  
Fold Cross Validation

Abstract Background Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) are closely associated with human diseases, and it is useful for the diagnosis and treatment of diseases to get the relationships between lncRNAs and diseases. Due to the high costs and time complexity of traditional bio-experiments, in recent years, more and more computational methods have been proposed by researchers to infer potential lncRNA-disease associations. However, there exist all kinds of limitations in these state-of-the-art prediction methods as well. Results In this manuscript, a novel computational model named FVTLDA is proposed to infer potential lncRNA-disease associations. In FVTLDA, its major novelty lies in the integration of direct and indirect features related to lncRNA-disease associations such as the feature vectors of lncRNA-disease pairs and their corresponding association probability fractions, which guarantees that FVTLDA can be utilized to predict diseases without known related-lncRNAs and lncRNAs without known related-diseases. Moreover, FVTLDA neither relies solely on known lncRNA-disease nor requires any negative samples, which guarantee that it can infer potential lncRNA-disease associations more equitably and effectively than traditional state-of-the-art prediction methods. Additionally, to avoid the limitations of single model prediction techniques, we combine FVTLDA with the Multiple Linear Regression (MLR) and the Artificial Neural Network (ANN) for data analysis respectively. Simulation experiment results show that FVTLDA with MLR can achieve reliable AUCs of 0.8909, 0.8936 and 0.8970 in 5-Fold Cross Validation (fivefold CV), 10-Fold Cross Validation (tenfold CV) and Leave-One-Out Cross Validation (LOOCV), separately, while FVTLDA with ANN can achieve reliable AUCs of 0.8766, 0.8830 and 0.8807 in fivefold CV, tenfold CV, and LOOCV respectively. Furthermore, in case studies of gastric cancer, leukemia and lung cancer, experiment results show that there are 8, 8 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with MLR, and 8, 7 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with ANN, having been verified by recent literature. Comparing with the representative prediction model of KATZLDA, comparison results illustrate that FVTLDA with MLR and FVTLDA with ANN can achieve the average case study contrast scores of 0.8429 and 0.8515 respectively, which are both notably higher than the average case study contrast score of 0.6375 achieved by KATZLDA. Conclusion The simulation results show that FVTLDA has good prediction performance, which is a good supplement to future bioinformatics research.

scholarly journals Innovative Monitoring Tools and Early Warning Systems for Risk Management: A Case Study

Geosciences ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 62 ◽  
Author(s):  
Andrea Segalini ◽  
Andrea Carri ◽  
Alessandro Valletta ◽  
Maurizio Martino
Keyword(s):  
Monitoring System ◽  
Early Warning ◽  
Slope Failure ◽  
Central Italy ◽  
Early Warning Systems ◽  
Critical Event ◽  
Warning Systems ◽  
Automatic Data ◽  
Area Of Interest

During recent years, the availability of innovative monitoring instrumentation has been a fundamental component in the development of efficient and reliable early warning systems (EWS). In fact, the potential to achieve high sampling frequencies, together with automatic data transmission and elaboration are key features for a near-real time approach. This paper presents a case study located in Central Italy, where the realization of an important state route required a series of preliminary surveys. The monitoring system installed on site included manual inclinometers, automatic modular underground monitoring system (MUMS) inclinometers, piezometers, and geognostic surveys. In particular, data recorded by innovative instrumentation allowed for the detection of major slope displacements that ultimately led to the landslide collapse. The implementation of advanced tools, featuring remote and automatic procedures for data sampling and elaboration, played a key role in the critical event identification and prediction. In fact, thanks to displacement data recorded by the MUMS inclinometer, it was possible to forecast the slope failure that was later confirmed during the following site inspection. Additionally, a numerical analysis was performed to better understand the mechanical behavior of the slope, back-analyze the monitored event, and to assess the stability conditions of the area of interest.

scholarly journals Study on the combined threshold for gully-type debris flow early warning

2019 ◽  
Vol 19 (1) ◽  
pp. 41-51 ◽  
Author(s):  
Jian Huang ◽  
Theodoor Wouterus Johannes van Asch ◽  
Changming Wang ◽  
Qiao Li
Keyword(s):  
Debris Flow ◽  
Pore Pressure ◽  
Early Warning ◽  
Early Stage ◽  
Warning System ◽  
Early Warning Systems ◽  
Great Loss ◽  
Mountainous Regions ◽  
Pressure Threshold

Abstract. Gully-type debris flow induced by high-intensity and short-duration rainfall frequently causes great loss of properties and causalities in mountainous regions of southwest China. In order to reduce the risk by geohazards, early warning systems have been provided. A triggering index can be detected in an early stage by the monitoring of rainfall and the changes in physical properties of the deposited materials along debris flow channels. Based on the method of critical pore pressure for slope stability analysis, this study presents critical pore pressure threshold in combination with rainfall factors for gully-type debris flow early warning. The Wenjia gully, which contains an enormous amount of loose material, was selected as a case study to reveal the relationship between the rainfall and pore pressure by field monitoring data. A three-level early warning system (zero, attention, and warning) is adopted and the corresponding judgement conditions are defined in real time. Based on this threshold, there are several rainfall events in recent years have been validated in Wenjia gully, which prove that such a combined threshold may be a reliable approach for the early warning of gully-type debris flow to safeguard the population in the mountainous areas.

scholarly journals A Remote Sensing Based Method to Detect Soil Erosion in Forests

2019 ◽  
Vol 11 (5) ◽  
pp. 513 ◽  
Author(s):  
Hanqiu Xu ◽  
Xiujuan Hu ◽  
Huade Guan ◽  
Bobo Zhang ◽  
Meiya Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Soil Loss ◽  
Southern China ◽  
Red Soil ◽  
Forest Area ◽  
Landsat 8 ◽  
Large Spatial Scale ◽  
Five Factors

Rainwater-induced soil erosion occurring in the forest is a special phenomenon of soil erosion in many red soil areas. Detection of such soil erosion is essential for developing land management to reduce soil loss in areas including southern China and other red soil regions of the world. Remotely sensed canopy cover is often used to determine the potential of soil erosion over a large spatial scale, which, however, becomes less useful in forest areas. This study proposes a new remote sensing method to detect soil erosion under forest canopy and presents a case study in a forest area in southern China. Five factors that are closely related to soil erosion in forest were used as discriminators to develop the model. These factors include fractional vegetation coverage, nitrogen reflectance index, yellow leaf index, bare soil index and slope. They quantitatively represent vegetation density, vegetation health status, soil exposure intensity and terrain steepness that are considered relevant to forest soil erosion. These five factors can all be derived from remote sensing imagery based on related thematic indices or algorithms. The five factors were integrated to create the soil erosion under forest model (SEUFM) through Principal Components Analysis (PCA) or a multiplication method. The case study in the forest area in Changting County of southern China with a Landsat 8 image shows that the first principal component-based SEUFM achieves an overall accuracy close to 90%, while the multiplication-based model reaches 81%. The detected locations of soil erosion in forest provide the target areas to be managed from further soil loss. The proposed method provides a tool to understand more about soil erosion in forested areas where soil erosion is usually not considered an issue. Therefore, the method is useful for soil conservation in forest.

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