Co-design and co-deployment of nature based solutions for river flooding mitigation in northern Italy river embankments

Author(s):  
Beatrice Pulvirenti ◽  
Paolo Ruggieri ◽  
Alessio Domeneghetti ◽  
Elena Toth ◽  
Silvia Maria Alfieri ◽  
...  

<p>Po valley in the Emilia Romagna region, Northern Italy, is threatened by hydro-meteorological hazards, such as river flooding. In the last 50 years this area was interested by an intensive urbanization (with cities that span from the size of a village to metropolitan urban areas such as Bologna) with the realization of infrastructures, e.g. roads and residential settlements near rivers. In addition, the strengthening and expansion of the embankment system led to the development of the areas prone to floods located nearby the rivers. These modifications, in combination with the occurrence of high flood peaks recently experienced in this area have increased the impacts and thus, the attention, on riverine floods. The last event occurred in December 2020, where Panaro river, a tributary of the Po river, broke its banks near Modena causing large flooded area.</p><p>Co-design and co-deployment of nature based solutions (NBS) to reduce flooding risk in the Panaro river is one of the objective of the H2020 project OPEn-air laboRAtories for Nature baseD solUtions to Manage environmental risks (OPERANDUM). A portion of the Panaro river embankment is one of the Open Air Laboratories (OAL) where special deep rooted plants were implemented to evidence the mitigation of hydro-meteorological risks by NBS.</p><p>In this work, a combined approach between Earth Observation (EO) data and multi-scale modelling is shown, to support the co-design process of the NBS. Synthetic Aperture Radar (SAR) and optical EO data were used to identify areas at risk, i.e. the area most likely to be affected by severe flooding events.  A thresholding method was applied to the SAR and optical images available during past extreme events to identify size and location of the floods. The remote sensing analysis allowed the definition of specific portions of the Panaro river where NBS can be more effective for flood risk reduction. In a second step, a multi-scale modelling approach, based on the characterisation of deep-rooted plants by laboratory experiments and in-field measurements, is used to determine the response of the identified portions of Panaro river to flooding events and to evaluate the effectiveness of possible NBS.</p><p>Remote sensing analysis indicates that the area between Secchia and Panaro rivers, delimited to the north by the town of Bomporto and to the South by the town of Albareto has been most frequently inundated in the recent extreme events. The integrated analysis leads to the identification of potential sites, along the Panaro river, where NBS could be effective for river flooding risk reduction, contributing to the definition of the priority sites among the ones defined by the stakeholders and engineers.</p>

Author(s):  
Libasse Sow ◽  
Fabrice Bernard ◽  
Siham Kamali-Bernard

This paper presents a hierarchical multi-scale modelling approach devoted to investigating the mechanical behaviour of cement-bound gravels. Material studied is based on Non-Hazardous Waste Incineration (NHWI) bottom ashes. The elastic moduli of NHWI particles have been previously determined by an original indentation campaign never conducted so far on these types of aggregates. The results of the experimental campaign serve as input data to the developed numerical strategy. The modelling is based on the definition of Representative Elementary Volumes (REV) considering all the heterogeneities of the material. The "virtual laboratory" set up made it possible to determine the mechanical parameters characterizing the gravel treated with 3% of cement. The high value obtained of the internal friction angle (76 °) gives the material a good bearing capacity. The classification in mechanical classes 3 and 4 when the Young's modulus of the NHWI particles varies from 20 to 80 GPa proves the feasibility of the reuse of this type of industrial by-products in this sector of activity. The present modelling approach is validated by means of comparisons with experimental results of the literature.


Author(s):  
B. Chopard ◽  
Joris Borgdorff ◽  
A. G. Hoekstra

We review a methodology to design, implement and execute multi-scale and multi-science numerical simulations. We identify important ingredients of multi-scale modelling and give a precise definition of them. Our framework assumes that a multi-scale model can be formulated in terms of a collection of coupled single-scale submodels. With concepts such as the scale separation map, the generic submodel execution loop (SEL) and the coupling templates, one can define a multi-scale modelling language which is a bridge between the application design and the computer implementation. Our approach has been successfully applied to an increasing number of applications from different fields of science and technology.


2005 ◽  
Vol 38 ◽  
pp. 147
Author(s):  
Γ. ΜΠΑΘΡΕΛΛΟΣ ◽  
Χ. ΣΚΥΛΟΔΗΜΟΥ ◽  
Π. ΚΑΚΑΛΙΚΑ

Floods are part of the géomorphologie cycle during the landscape formation. The flooding events may become dangerous when affect the human activities. Hence, a certain methodology to assess the flooding risk for the basin of Trikala - Kalambaka is presented above, and a flood hazard map has been produced. The method used for the definition of locations prone to flooding combines the study of the relief with the mechanical composition of the soil. In this paper, the towns and settlements that are vulnerable to future flooding events. In this way, the contribution of geology to the urban and natural planning is of outmost importance.


2021 ◽  
Vol 10 (7) ◽  
pp. 488
Author(s):  
Peng Li ◽  
Dezheng Zhang ◽  
Aziguli Wulamu ◽  
Xin Liu ◽  
Peng Chen

A deep understanding of our visual world is more than an isolated perception on a series of objects, and the relationships between them also contain rich semantic information. Especially for those satellite remote sensing images, the span is so large that the various objects are always of different sizes and complex spatial compositions. Therefore, the recognition of semantic relations is conducive to strengthen the understanding of remote sensing scenes. In this paper, we propose a novel multi-scale semantic fusion network (MSFN). In this framework, dilated convolution is introduced into a graph convolutional network (GCN) based on an attentional mechanism to fuse and refine multi-scale semantic context, which is crucial to strengthen the cognitive ability of our model Besides, based on the mapping between visual features and semantic embeddings, we design a sparse relationship extraction module to remove meaningless connections among entities and improve the efficiency of scene graph generation. Meanwhile, to further promote the research of scene understanding in remote sensing field, this paper also proposes a remote sensing scene graph dataset (RSSGD). We carry out extensive experiments and the results show that our model significantly outperforms previous methods on scene graph generation. In addition, RSSGD effectively bridges the huge semantic gap between low-level perception and high-level cognition of remote sensing images.


2021 ◽  
Vol 13 (7) ◽  
pp. 1243
Author(s):  
Wenxin Yin ◽  
Wenhui Diao ◽  
Peijin Wang ◽  
Xin Gao ◽  
Ya Li ◽  
...  

The detection of Thermal Power Plants (TPPs) is a meaningful task for remote sensing image interpretation. It is a challenging task, because as facility objects TPPs are composed of various distinctive and irregular components. In this paper, we propose a novel end-to-end detection framework for TPPs based on deep convolutional neural networks. Specifically, based on the RetinaNet one-stage detector, a context attention multi-scale feature extraction network is proposed to fuse global spatial attention to strengthen the ability in representing irregular objects. In addition, we design a part-based attention module to adapt to TPPs containing distinctive components. Experiments show that the proposed method outperforms the state-of-the-art methods and can achieve 68.15% mean average precision.


2021 ◽  
Vol 13 (12) ◽  
pp. 2333
Author(s):  
Lilu Zhu ◽  
Xiaolu Su ◽  
Yanfeng Hu ◽  
Xianqing Tai ◽  
Kun Fu

It is extremely important to extract valuable information and achieve efficient integration of remote sensing data. The multi-source and heterogeneous nature of remote sensing data leads to the increasing complexity of these relationships, and means that the processing mode based on data ontology cannot meet requirements any more. On the other hand, the multi-dimensional features of remote sensing data bring more difficulties in data query and analysis, especially for datasets with a lot of noise. Therefore, data quality has become the bottleneck of data value discovery, and a single batch query is not enough to support the optimal combination of global data resources. In this paper, we propose a spatio-temporal local association query algorithm for remote sensing data (STLAQ). Firstly, we design a spatio-temporal data model and a bottom-up spatio-temporal correlation network. Then, we use the method of partition-based clustering and the method of spectral clustering to measure the correlation between spatio-temporal correlation networks. Finally, we construct a spatio-temporal index to provide joint query capabilities. We carry out local association query efficiency experiments to verify the feasibility of STLAQ on multi-scale datasets. The results show that the STLAQ weakens the barriers between remote sensing data, and improves their application value effectively.


Author(s):  
Alexandru Szabo ◽  
Radu Negru ◽  
Alexandru-Viorel Coşa ◽  
Liviu Marşavina ◽  
Dan-Andrei Şerban

Author(s):  
Weiguo Cao ◽  
Marc J. Pomeroy ◽  
Yongfeng Gao ◽  
Matthew A. Barish ◽  
Almas F. Abbasi ◽  
...  

AbstractTexture features have played an essential role in the field of medical imaging for computer-aided diagnosis. The gray-level co-occurrence matrix (GLCM)-based texture descriptor has emerged to become one of the most successful feature sets for these applications. This study aims to increase the potential of these features by introducing multi-scale analysis into the construction of GLCM texture descriptor. In this study, we first introduce a new parameter - stride, to explore the definition of GLCM. Then we propose three multi-scaling GLCM models according to its three parameters, (1) learning model by multiple displacements, (2) learning model by multiple strides (LMS), and (3) learning model by multiple angles. These models increase the texture information by introducing more texture patterns and mitigate direction sparsity and dense sampling problems presented in the traditional Haralick model. To further analyze the three parameters, we test the three models by performing classification on a dataset of 63 large polyp masses obtained from computed tomography colonoscopy consisting of 32 adenocarcinomas and 31 benign adenomas. Finally, the proposed methods are compared to several typical GLCM-texture descriptors and one deep learning model. LMS obtains the highest performance and enhances the prediction power to 0.9450 with standard deviation 0.0285 by area under the curve of receiver operating characteristics score which is a significant improvement.


2021 ◽  
Vol 13 (3) ◽  
pp. 366
Author(s):  
Renato Macciotta ◽  
Michael T. Hendry

Transportation infrastructure in mountainous terrain and through river valleys is exposed to a variety of landslide phenomena. This is particularly the case for highway and railway corridors in Western Canada that connect towns and industries through prairie valleys and the Canadian cordillera. The fluidity of these corridors is important for the economy of the country and the safety of workers, and users of this infrastructure is paramount. Stabilization of all active slopes is financially challenging given the extensive area where landslides are a possibility, and monitoring and minimization of slope failure consequences becomes an attractive risk management strategy. In this regard, remote sensing techniques provide a means for enhancing the monitoring toolbox of the geotechnical engineer. This includes an improved identification of active landslides in large areas, robust complement to in-place instrumentation for enhanced landslide investigation, and an improved definition of landslide extents and deformation mechanisms. This paper builds upon the extensive literature on the application of remote sensing techniques and discusses practical insights gained from a suite of case studies from the authors’ experience in Western Canada. The review of the case studies presents a variety of landslide mechanisms and remote sensing technologies. The aim of the paper is to transfer some of the insights gained through these case studies to the reader.


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