Flood-prone areas delineation in coastal regions using the Geomorphic Flood Index

2021 ◽  
Author(s):  
Cinzia Albertini ◽  
Domenico Miglino ◽  
Vito Iacobellis ◽  
Francesco De Paola ◽  
Salvatore Manfreda
Keyword(s):  
Return Period ◽  
Performance Metrics ◽  
Binary Classification ◽  
Economic Losses ◽  
Good Representation ◽  
Flood Inundation ◽  
Coastal Zones ◽  
Flood Prone Areas ◽  
Coastal Basin ◽  
Flood Index

<p>Detecting areas exposed to flood inundation in coastal zones is of paramount importance for reducing damages and preventing human and economic losses. In general, the Geomorphic Flood Index (GFI) method, based on a Digital Elevation Model (DEM) and mostly applied to riverine flood, provides a good representation of flood-prone areas with low requirements in terms of data, time and costs. However, the method does not account for inter-basin floodwater transfers and, therefore, performs poorly on coastal basins. The present work addresses this shortcoming by explicitly taking into account these potential inter-basin water transfers. We applied the GFI method with this new feature to a coastal basin located in southern Italy and the outcome was compared with a flood inundation map obtained by a two-dimensional hydraulic simulation for a return period of 300 years. Its transferability was tested in a second adjacent coastal basin using a threshold binary classification and the sensitivity of the methodology to the return period was investigated. Results show that coastal flood-prone areas are successfully delineated with performance metrics above 93%. This achievement represents a step further in the application of the GFI method, that can help stakeholders in flood risk management to rapidly and inexpensively characterize hazard-prone areas.</p>

scholarly journals Integrated economic and environmental building classification and optimal seismic vulnerability/energy efficiency retrofitting

2021 ◽  
Author(s):  
Martina Caruso ◽  
Rui Pinho ◽  
Federica Bianchi ◽  
Francesco Cavalieri ◽  
Maria Teresa Lemmo
Keyword(s):  
Energy Efficiency ◽  
Life Cycle ◽  
Seismic Hazard ◽  
Environmental Impacts ◽  
Classification System ◽  
Seismic Vulnerability ◽  
Performance Metrics ◽  
Climatic Conditions ◽  
Economic Losses ◽  
Operational Energy

AbstractA life cycle framework for a new integrated classification system for buildings and the identification of renovation strategies that lead to an optimal balance between reduction of seismic vulnerability and increase of energy efficiency, considering both economic losses and environmental impacts, is discussed through a parametric application to an exemplificative case-study building. Such framework accounts for the economic and environmental contributions of initial construction, operational energy consumption, earthquake-induced damage repair activities, retrofitting interventions, and demolition. One-off and annual monetary expenses and environmental impacts through the building life cycle are suggested as meaningful performance metrics to develop an integrated classification system for buildings and to identify the optimal renovation strategy leading to a combined reduction of economic and environmental impacts, depending on the climatic conditions and the seismic hazard at the site of interest. The illustrative application of the framework to an existing school building is then carried out, investigating alternative retrofitting solutions, including either sole structural retrofitting options or sole energy refurbishments, as well as integrated strategies that target both objectives, with a view to demonstrate its practicality and to explore its ensuing results. The influence of seismic hazard and climatic conditions is quantitatively investigated, by assuming the building to be located into different geographic locations.

scholarly journals A Spatial Improved-kNN-Based Flood Inundation Risk Framework for Urban Tourism under Two Rainfall Scenarios

2021 ◽  
Vol 13 (5) ◽  
pp. 2859
Author(s):  
Shuang Liu ◽  
Rui Liu ◽  
Nengzhi Tan
Keyword(s):  
Nearest Neighbor ◽  
Scientific Information ◽  
Climate Extremes ◽  
Extreme Rainfall ◽  
Tourism Industry ◽  
Urban Tourism ◽  
Evaluation Framework ◽  
Economic Losses ◽  
Flood Inundation ◽  
K Nearest Neighbor

Urban tourism has been suffering socio-economic challenges from flood inundation risk (FIR) triggered by extraordinary rainfall under climate extremes. The evaluation of FIR is essential for mitigating economic losses, and even casualties. This study proposes an innovative spatial framework integrating improved k-nearest neighbor (kNN), remote sensing (RS), and geographic information system (GIS) to analyze FIR for tourism sites. Shanghai, China, was selected as a case study. Tempo-spatial factors, including climate, topography, drainage, vegetation, and soil, were selected to generate several flood-related gridded indicators as inputs into the evaluation framework. A likelihood of FIR was mapped to represent possible inundation for tourist sites under a moderate-heavy rainfall scenario and extreme rainfall scenario. The resultant map was verified by the maximum inundation extent merged by RS images and water bodies. The evaluation outcomes deliver the baseline and scientific information for urban planners and policymakers to take cost-effective measures for decreasing and evading the pressure of FIR on the sustainable development of urban tourism. The spatial improved-kNN-based framework provides an innovative, effective, and easy-to-use approach to evaluate the risk for the tourism industry under climate change.

scholarly journals Matrix Product State–Based Quantum Classifier

2019 ◽  
Vol 31 (7) ◽  
pp. 1499-1517 ◽  
Author(s):  
Amandeep Singh Bhatia ◽  
Mandeep Kaur Saggi ◽  
Ajay Kumar ◽  
Sushma Jain
Keyword(s):  
Performance Metrics ◽  
Quantum Computer ◽  
Binary Classification ◽  
Learning Ability ◽  
Matrix Product ◽  
Product State ◽  
Data Set ◽  
Matrix Product State ◽  
Tensor Network ◽  
Network States

Interest in quantum computing has increased significantly. Tensor network theory has become increasingly popular and widely used to simulate strongly entangled correlated systems. Matrix product state (MPS) is a well-designed class of tensor network states that plays an important role in processing quantum information. In this letter, we show that MPS, as a one-dimensional array of tensors, can be used to classify classical and quantum data. We have performed binary classification of the classical machine learning data set Iris encoded in a quantum state. We have also investigated its performance by considering different parameters on the ibmqx4 quantum computer and proved that MPS circuits can be used to attain better accuracy. Furthermore the learning ability of an MPS quantum classifier is tested to classify evapotranspiration (ET[Formula: see text]) for the Patiala meteorological station located in northern Punjab (India), using three years of a historical data set (Agri). We have used different performance metrics of classification to measure its capability. Finally, the results are plotted and the degree of correspondence among values of each sample is shown.

scholarly journals Global flood hazard map and exposed GDP comparison: a China case study

2020 ◽  
Author(s):  
Jerom P. M. Aerts ◽  
Steffi Uhlemann-Elmer ◽  
Dirk Eilander ◽  
Philip J. Ward
Keyword(s):  
Return Period ◽  
Flood Hazard ◽  
Regional Scale ◽  
Flood Protection ◽  
Economic Losses ◽  
Return Periods ◽  
Hazard Maps ◽  
High Exposure ◽  
Pluvial Flooding

Abstract. Floods are among the most frequent and damaging natural hazard events in the world. In 2016, economic losses from flooding amounted to $56 bn globally, of which $20 bn occurred in China (Munich Re, 2017). National or regional scale mapping of flood hazard is at present providing an inconsistent and incomplete picture of floods. Over the past decade global flood hazard models have been developed and continuously improved. There is now a significant demand for testing of the global hazard maps generated by these models in order to understand their applicability for international risk reduction strategies and for reinsurance portfolio risk assessments using catastrophe models. We expand on existing methods for comparing global hazard maps and analyse 8 global flood models (GFMs) that represent the current state of the global flood modelling community. We apply our comparison to China as a case study and, for the first time, we include industry models, pluvial flooding, and flood protection standards in the analysis. We find substantial variability between the flood hazard maps in modelled inundated area and exposed GDP across multiple return periods (ranging from 5 to 1500 years) and in expected annual exposed GDP. For example, for the 100 year return period undefended (assuming no flood protection) hazard maps the percentage of total affected GDP of China ranges between 4.4 % and 10.5 % for fluvial floods. For the majority of the GFMs we see only a small increase in inundated area or exposed GDP for high return period undefended hazard maps compared to low return periods, highlighting major limitations in the models’ resolution and their output. The inclusion of industry models which currently model flooding at higher spatial resolution, and which additionally include pluvial flooding, strongly improves the comparison and provides important new benchmarks. Pluvial flooding can increase the expected annual exposed GDP by as much as 1.3 % points. Our study strongly highlights the importance of flood defenses for a realistic risk assessment in countries like China that are characterized by high concentrations of exposure. Even an incomplete (1.74 % of area of China) but locally detailed layer of structural defenses in high exposure areas reduces the expected annual exposed GDP to fluvial and pluvial flooding from 4.1 % to 2.8 %.

Local People’s Responses to Flood Disasters in Flood Prone Areas of Northeast Bangladesh

2015 ◽  
Vol 10 (2) ◽  
pp. 288-298 ◽  
Author(s):  
Naoki Yamashita ◽  
◽  
Terunori Ohmoto ◽  
Keyword(s):  
Flood Mitigation ◽  
Mitigation Measures ◽  
Agricultural Fields ◽  
Flood Inundation ◽  
Inundation Depth ◽  
Flood Disasters ◽  
The Status ◽  
Similarities And Differences ◽  
Flood Prone Areas ◽  
Selection Of

In the flood prone areas of Bangladesh, local people have adapted to flooding. Essentially, properties are protected against flooding by constructing villages in the highlands on natural levees, while using lowlands as agricultural fields during the dry season. It remains to spread flood inundation condition and exempts the necessity of strengthening measures against flooding. This study aims to clarify the status of self, community, and public assistance for flood disasters in flood prone areas of Northeast Bangladesh based on a questionnaire survey. We extracted similarities and differences between local people’s flood responses by comparing our findings to those of a similar study on a 2006 flood in the Sendai River Basin, Japan. The effects of preventive flood mitigation measures such as selection of house location are quantitatively confirmed. Maximum inundation depth and duration for houses is approximately 10% less than that for agricultural fields. The study reveals that both areas have evacuation activities, although factors motivating evacuation differ.

scholarly journals Impacts of Urban Floods on Road Connectivity - A Review and Systematic Bibliometric Analysis

2021 ◽  
Vol 16 (2) ◽  
pp. 575-593 ◽  
Author(s):  
Ashok Kadaverugu ◽  
Kasi Viswanadh Gorthi ◽  
Nageshwar Rao Chintala
Keyword(s):  
Geographical Information Systems ◽  
Early Warning Systems ◽  
Data Availability ◽  
Geographical Information ◽  
Economic Losses ◽  
Flood Inundation ◽  
Risk Modeling ◽  
Urban Centers ◽  
Urban Floods ◽  
Remote Sensing Methods

Urban floods are paralyzing surface transportation and inflicting heavy economic losses. Climate-induced increase in frequency and intensity of rainfalls and excessive urbanization makes urban centers even more vulnerable to floods. It is necessary to quantify all dimensions of losses caused to road connectivity to improve flood mitigation policy. There is a need to consolidate the existing body of peer-reviewed contemporary literature on flood inundation modeling and its impacts on road connectivity. This will improve the awareness of policymakers and researchers and help in science-based decision making. Articles archived in the Web of Science database having the keywords floods and road in their title published between 1977 and 2020 were analyzed using the blibliometrix library of R. Analysis shows that the flood inundation and flood extent modeling has evolved from the conventional hydrological models to the near real-time crowd-sourced modeling methods. Applications of geographical information systems and advanced remote sensing methods have been growing in identifying road network vulnerabilities. We observed a gap in harmonized data availability, due to the unstructured data formats at several scales, which hinders a generalized approach for flood risk modeling studies for urban planning. Concentrated efforts have to be made to fill the gaps in data availability and research methodologies, especially using crowd-sourced data. Further, efforts have to be made to increase awareness, early warning systems, and alternate transport networks, to make the cities less vulnerable to floods.

scholarly journals STUDI PEMETAAN DAERAH GENANGAN BANJIR DAS SEI KAMBING DENGAN SISTEM INFORMASI GEOGRAFIS

Teras Jurnal ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 165
Author(s):  
Asril Zevri
Keyword(s):  
River Basin ◽  
Return Period ◽  
Daily Rainfall ◽  
Water Levels ◽  
Disaster Mitigation ◽  
Flood Inundation ◽  
The Public ◽  
Inundation Area ◽  
Flood Level ◽  
The Impact

<p><em>Sei Sikambing River Basin is one of the Sub Das of Deli River which has an important role in water requirement in Medan City. Rainfall with high intensity is supported by changes in land use causing floods which reach 0.6 m to 1 m from river banks. The purpose of this study was to map the Sei Kambing River basin flood inundation area as information to the public in disaster mitigation efforts. The scope of this research is to analyze the maximum daily rainfall with a return period of 2 to 100 years, analyze flood discharge with a return period of 2 to 100, analyze flood water levels with HECRAS software, and spatially map flood inundation areas with GIS. The results showed that the return flood rate of the Sikambing watershed with a 25-year return period of 211.94 m<sup>3</sup>/s caused the flood level of the Sikambing watershed to be between 1.7 m to 3.7 m. The Sikambing watershed flood inundation area reached an area of 1.19 Km<sup>2</sup> which resulted in the impact of flooding on 5 sub-districts in Medan, namely Medan Selayang District, Medan Sunggal, Medan Petisah, Medan Helvetia, and West Medan.</em><em></em></p>

scholarly journals Exploring Symmetry of Binary Classification Performance Metrics

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 47 ◽  
Author(s):  
Amalia Luque ◽  
Alejandro Carrasco ◽  
Alejandro Martín ◽  
Juan Ramón Lama
Keyword(s):  
Performance Metrics ◽  
Binary Classification ◽  
Confusion Matrix ◽  
Full Range ◽  
Classification Performance ◽  
Classification Problems ◽  
Performance Metric ◽  
Selection For ◽  
Proper Performance ◽  
Insight Into

Selecting the proper performance metric constitutes a key issue for most classification problems in the field of machine learning. Although the specialized literature has addressed several topics regarding these metrics, their symmetries have yet to be systematically studied. This research focuses on ten metrics based on a binary confusion matrix and their symmetric behaviour is formally defined under all types of transformations. Through simulated experiments, which cover the full range of datasets and classification results, the symmetric behaviour of these metrics is explored by exposing them to hundreds of simple or combined symmetric transformations. Cross-symmetries among the metrics and statistical symmetries are also explored. The results obtained show that, in all cases, three and only three types of symmetries arise: labelling inversion (between positive and negative classes); scoring inversion (concerning good and bad classifiers); and the combination of these two inversions. Additionally, certain metrics have been shown to be independent of the imbalance in the dataset and two cross-symmetries have been identified. The results regarding their symmetries reveal a deeper insight into the behaviour of various performance metrics and offer an indicator to properly interpret their values and a guide for their selection for certain specific applications.

scholarly journals Four Global Catastrophic Risks – A Personal View

2021 ◽  
Vol 9 ◽  
Author(s):  
Russell Blong
Keyword(s):  
Sea Level ◽  
Natural Hazards ◽  
Return Period ◽  
Extreme Events ◽  
Social Economic ◽  
Economic Losses ◽  
Personal View ◽  
Average Return ◽  
Catastrophic Risks ◽  
Near Future

Global catastrophic risks (GCRs) affect a larger than hemispheric area and produce death tolls of many millions and/or economic losses greater than several trillion USD. Here I explore the biophysical, social-economic, demographic and cultural strands of four global catastrophic risks – sea level rise, a VEI 7 eruption, a pandemic, and a geomagnetic storm – one human-exacerbated at the least, one geological, one biological in large part, and one from space. Durations of these biophysical events range from a day or two to more than 100 years and the hazards associated range from none to numerous. Each of the risks has an average return period of no more than a few hundred years and lie within a range where many regulators ordinarily demand efforts in the case of less extreme events at enhancing resilience. Losses produced by GCRs and other natural hazards are usually assessed in terms of human mortality or dollars but many less tangible losses are at least as significant. Despite the varying durations, biophysical characteristics, and the wide array of potential consequences, the aftermath at global (and at more granular scales) can be summarised by one of four potential futures. While this assessment considers the present and the near future (the Anthropocene), much of this appraisal applies also to global catastrophic risks in the Early Holocene.

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