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A sustainable transportation system should represent a win-win situation: minimizing transport's impact on the environment and reducing natural disasters' effects on transportation. A well-distributed set of rain gauges is crucial for monitoring services in smart cities. However, those services should consider the uncertainties about the registers of rainfall impacts. In this paper, the authors present a case study of optimal rain gauge location based on an actual database of rainfall events with impacts on urban mobility in the city of Sao Paulo (Brazil).

This paper presents a maximal covering location formulation and proposes a robustness analysis considering spatial location perturbations.

In this case study, the robustness of the objective function is above 99.99%. The robustness for the number of covered demand points is 88.93%, and the frequency associated with every candidate is between 11.71% and 69.49%.

Incorporating spatial uncertainties on coverage problems is essential to provide stakeholders more realistic supporting tools and to draw different possible scenarios.

The purpose of this study is to address a highly heterogeneous rift margin environment and exhibit considerable spatiotemporal hydro-climatic variations. In spite of limited, random and inaccurate data retrieved from rainfall gauging stations, the recent advancement of satellite rainfall estimate (SRE) has provided promising alternatives over such remote areas. The aim of this research is to take advantage of the technologies through performance evaluation of the SREs against ground-based-gauge rainfall data sets by incorporating its applicability in calibrating hydrological models.

Selected multi satellite-based rainfall estimates were primarily compared statistically with rain gauge observations using a point-to-pixel approach at different time scales (daily and seasonal). The continuous and categorical indices are used to evaluate the performance of SRE. The simple scaling time-variant bias correction method was further applied to remove the systematic error in satellite rainfall estimates before being used as input for a semi-distributed hydrologic engineering center's hydraulic modeling system (HEC-HMS). Runoff calibration and validation were conducted for consecutive periods ranging from 1999–2010 to 2011–2015, respectively.

The spatial patterns retrieved from climate hazards group infrared precipitation with stations (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP) and tropical rainfall measuring mission (TRMM) rainfall estimates are more or less comparably underestimate the ground-based gauge observation at daily and seasonal scales. In comparison to the others, MSWEP has the best probability of detection followed by TRMM at all observation stations whereas CHIRPS performs the least in the study area. Accordingly, the relative calibration performance of the hydrological model (HEC-HMS) using ground-based gauge observation (Nash and Sutcliffe efficiency criteria [NSE] = 0.71; R² = 0.72) is better as compared to MSWEP (NSE = 0.69; R² = 0.7), TRMM (NSE = 0.67, R² = 0.68) and CHIRPS (NSE = 0.58 and R² = 0.62).

Calibration of hydrological model using the satellite rainfall estimate products have promising results. The results also suggest that products can be a potential alternative source of data sparse complex rift margin having heterogeneous characteristics for various water resource related applications in the study area.

This research is an original work that focuses on all three satellite rainfall estimates forced simulations displaying substantially improved performance after bias correction and recalibration.

Climate change has had serious consequences at the global and local levels, which has required more effective scientific studies and management measures for disaster risk reduction strategies. This paper aims to analyze and discuss the degree of institutional vulnerability in terms of disaster risk governance, with emphasis on non-structural measures taken in the municipality of Jaboatão dos Guararapes, Pernambuco, Brazil.

Five indicators were analyzed, composed of Planning and Management Instrument, Management Structure, Preventive Action, Multidisciplinary Work and Emergency Funds. It is worth highlighting the form application with government actors, involving technicians from strategic areas of the municipality and official reports analysis. It stands out the importance of non-structural measures to strengthen this governance.

From the results of this study was noted that municipal management adopted measures to develop integrated planning, acting within the principles recommended in the Sendai framework (2015–2030), characterized as a medium degree of institutional vulnerability. Recommendations are suggested for the improvement of the entire governance system, according to the indicators and documents analyzed.

This article integrates a set of data and analyses relevant to the Disaster Risk Governance, regarding the assessment of institutional vulnerability with a view to non-structural actions. The importance and significance of the composed indicators allow measuring and evaluating institutional vulnerabilities. The methodology created fomented the production of scientific knowledge that allows employment in other municipalities.

Chronicles and literary records show that Sri Lanka has been affected by various natural disasters from time to time in the past, as it is now. The sea surge/coastal flooding during King Kelani Tissa's reign, around 190 BC could have been a tsunami. A severe drought and famine known as Beminitiya Seya occurred during the intermittent reign of the Brahmin King Thiya (Tissa) and King Valagamba (89–77 BC). There are also references to droughts prior to this, namely, Akkakayika Seya (it is said that about 24,000 monks died and others left the country, and some of those who remained survived by eating kara leaves). Subsequently, this led King Valagamba to undertake to record the sayings of the Buddha (Tripitaka) for posterity. Duttagamini (161–137 BC), Ekanalika Seya during the reign of King Kunchanaga (187–189 AD) and others, none of which had been as severe as Beminitiya Seya. Apart from these extreme hazard events other incidents have been reported, including the flooding incidents during the British rule (National Disaster Management Plan, 2007).

Radar can provide valuable information on the spatial distribution of rainfall, but is not as yet able to provide accurate quantitative information on rainfall rate. Describes research on the use of polarization towards improving the radar monitoring of storms.

The pace of urbanization in the developing world is led by Asia. Over the next 25 years, Asia's urban population will grow by around 70% to more than 2.6 billion people. An additional billion people will have urban habitats (ADB, 2006).

The “Hyogo Framework for Action 2005–2015: Building the Resilience of Nations and communities to disasters” (HFA) was adopted at the UN World Conference on Disaster Reduction (January 2005, Kobe, Japan). The HFA specifies that disaster risk is compounded by increasing vulnerabilities related to various elements including unplanned urbanization. Across the HFA, important elements on urban risk reduction are mentioned as one of crucial areas of work to implement the HFA. In particular incorporating disaster risk reduction into urban planning is specified to reduce the underlying risk factors (Priority 4).

The purpose of this research is to provide a theoretical and practical theory and application that provides understanding and means to manage complex infrastructures.

In this research, complexity, nonlinear, noncontinuous effects and aleatoric and data unknowns are bypassed by directly addressing systems' responses. Graph theory, statistics and digital signal processing (DSP) tools are applied within a theoretical framework of the theory of faults (ToF). Motivational complex infrastructure systems (CISs) are difficult to model. Data are often missing or erroneous, changes are not well documented and processes are not well understood. On top of it, under complexity, stalwart analytical tools have limited predictive power. The aleatoric risk, such as rain and risk cascading from interconnected infrastructures, is unpredictable. Mitigation, response and recovery efforts are adversely affected.

The theory and application are presented and demonstrated by a step-by-step development of an application to a municipal drainage system. A database of faults is analyzed to produce system statistics, spatio-temporal morphology, behavior and traits. The gained understanding is compared to the physical system's design and to its modus operandi. Implications for design and maintenance are inferred; DSP tools to manage the system in real time are developed.

Sociological systems are interest driven. Some events are intentionally created and directed to the benefit and detriment of the opposing parties in a project. Those events may be explained and possibly predicted by understanding power plays, not power functions. For those events, sociological game theories provide better explanatory value than mathematical gain theories.

The theory provides a thematic network for modeling and resolving aleatoric uncertainty in engineering and sociological systems. The framework may be elaborated to fields such as energy, healthcare and critical infrastructure.

ToF provides a framework for the modeling and prediction of faults generated by inherent aleatoric uncertainties in social and technological systems. Therefore, the framework and theory lay the basis for automated monitoring and control of aleatoric uncertainties such as mechanical failures and human errors and the development of mitigation systems.

The contribution of this research is in the provision of an explicatory theory and a management paradigm for complex systems. This theory is applicable to a wide variety of fields from facilities and construction project management to maintenance and from academic studies to commercial use.

The purpose of this paper is to integrate the proactive role of communities and the use of flood modeling in the implementation of a flood early warning system.

Manual rain gauges were installed in 20 houses of volunteers living within the Bicol River basin to monitor rainfall. Rain information is sent twice daily via SMS message to a receiving computer. The received data are used to run a basin model that was developed in HEC‐HMS, which converts precipitation excess to overland flow and channel run‐off.

Different watershed models were developed for different rainfall events. Geomorphic analysis using 3 s SRTM Digital Elevation Model (DEM) processed in a GIS platform was also done to refine the overland flow. The derived hydrographs were used in the HEC‐RAS hydraulic model which has as main output threshold values for the rain‐flood relationship.

Although SRTM DEM that was used for the geomorphic analysis was sufficient for the purpose of the study, higher resolution DEMs can further improve the mapping of spatial extent of flood areas.

The results are used for the forecast of flood and the timely issuance of flood bulletins.

This study is the first to incorporate the involvement of the community in establishing a flood early warning system. The method can also be used as a prototype for other flood models in other parts of the country.

Highlights two works being carried out by the French Laboratoire Central des Ponts et Chaussées in the field of smart sensors. The first concerns the knowledge of loads applied to bridges in order to evaluate extreme load effects and fatigue load effects over their lifetime. To achieve these goals, a data acquisition system based on smart sensors extracting and classifying extrema in the traffic loads signal has been developed. The second concerns distributed systems software cost reduction by means of a generic model. The aim of the model is the design of a software generator for smart sensor‐based systems. The key of the system is in the description of an instrumentation plan under the form of a data dependence graph (DDG). The goal of the generator is to map and “execute” that DDG on the physical architecture according to the number of transducers, their affectation to the smart sensors and a PC‐based system controller.