Introduction

Urban flood modelling within the DCs

Calibration of flood models

Conclusion – the challenges now for flood modelling?

Acknowledgements

References

Abstract

The prevalence of flooding events and the associated risk in the urban areas is an increasingly important issue of global significance, although it is more critical for the developing countries (DCs), such as Nigeria, where the hazard is often poorly understood and understudied. With current predictions of worsening future scenarios, it is important to pursue integrated flood risk management approaches which incorporate flood modelling. This paper is part of a research programme which is assessing and modelling urban flood risks in the DCs and data poor areas. It focuses on the latest science and philosophy in relation to urban flood risk management in the DCs. It reviews the literature around current flood modelling techniques and provides a comprehensive table of the different approaches alongside the strengths and weaknesses of the different models. Indeed, research in the vicinity of flood modelling has been extensive, and over the years has resulted in the development of a wide variety of schema, datasets and methodologies for simulating flood hydrodynamics. However, the actual potential of these developments has not been demonstrated in the management of flood risk within the DCs. To date, a perfect model or generic technique which can capture every aspect of flood hydrodynamics in an optimal fashion within the diversity of study locations is still unrealistic. Thus, to bypass the present flood modelling challenges within the context of the DCs, extensive calibration of state-of-the-art flood models is of significance. Additionally, researchers within the DCs should be fascinated by the prospect of developing bespoke flood models based on simple mathematical formulations which are easy to parameterise using global, open source and freely- accessible datasets.

Introduction

he rate at which flooding occurs in recent times has been unprecedented, with the implication that only few coastal, rural, and urban environments still have their natural states unaltered [67,133,151]. This situation is of global significance, although it seems that the perception of flooding in the developing countries (DCs) for example Nigeria, is being nuanced by obvious limitations in research, economy and policy framework [111]. During flooding, water largely covers land areas not usually covered by water, destroying farmland and critical infrastructure, displacing human populations, disrupting economic activities, and in the worst cases, leading to epidemic and death ([135], pg.309). These incidences, especially those relating to fluvial and coastal flooding often were due to sea-level rise, ice melt, overtopping or destruction of water defences and coastal tsunamis [35,93]. However, pluvial flooding, the focus of this review, appears to be widespread in recent times especially within the urban areas where its impacts are increasingly a major source of concern for urban residents and policy makers [38,75,90,152].

Pluvial flooding is basically due to increased frequency and intensity of rainfall, although there are a number of other potential causative factors which have been identified. In Dawson et al. [39] and Adeloye and Rustum [5] pluvial flooding was problematised on the basis of land use change, geomorphology, failure of urban drainage facilities and poor urban planning. Mark et al. [95] perceived pluvial flooding with regards to the scale of its impacts which in fact seem to correlate positively with the large number of human population and development assets within the urban areas. The study demonstrates that urban areas are the hotspot of large-scale flooding impacts to the same degree that they are typically fundamental to any nations’ sustainable development. Along with the much-discussed global climate change, which triggers heavy storms in recent times, current knowledge of rapid urbanisation and demographic pressures which characterise the DCs underscores the inclusive nature of urban pluvial flooding, and the primacy of galvanizing discussions for the management of its threats within the DCs [58,156,164,168].

1. Introduction

2. Flood risk management

3. Flood resilience

4. Conclusions

Appendix A. Supplementary data

Research Data

References

Abstract

Flood risk management has proven successful at reducing the threat of some flooding hazards, preventing loss of life during flooding events and easing the economic burden to communities and regions following floods. It is a useful approach for assessing risks and guiding decisions on implementing protection measures. Recently, in addition to flood risk management, flood resilience is discussed as a new approach in academic literature. This contribution tries to unravel the relation between flood risk management and flood resilience. Therefore, three aspects are discussed: the definition of resilience, its measurement methods and also its possible implementation and embedding in flood risk management.

Introduction

Resilience is a relatively new concept as applied to environmental hazard management [2]. Currently, a risk-based approach is dominating the way society deals with natural hazards, such as floods. Whereas the risk-based approach provides a rational way of balancing the costs of mitigation and adaptation measures [38], resilience embraces the uncertainties associated with natural hazards by focusing on the ability of affected systems to absorb shocks [33].

The idea of resilience follows closely with a modern emphasis on integrated solutions to environmental issues which are becoming increasingly complex as they expand geographically, economically, socially and politically. At the same time, resilience is considered to be too abstract, apolitical and ahistorical in social science [49]. The traditional ways of applying measures which consider closed and simplified boundary conditions are no longer adequate. In a modern world, processes and people are highly dynamic and far-reaching. Therefore, our measures must reflect society by learning to become similarly flexible and inclusive.

1- Introduction

2- Exploration information systems (EIS)

3- Discussion

4- Concluding remarks

References

Abstract

The advent of modern data collection and storage technologies has brought about a huge increase in data volumes with both traditional and machine learning tools struggling to effectively handle, manage and analyse the very large data quantities that are now available. The mineral exploration industry is by no means immune to this big data issue. Exploration decision-making has become much more complex in the wake of big data, in particular with respect to questions about how to best manage and use the data to obtain information, generate knowledge and gain insight. One of the ways in which the mineral exploration industry works with big data is by using a geographic information system (GIS). For example, GIS platforms are often used for integration, interrogation and interpretation of diverse geoscience and mineral exploration data with the goal of refining and prioritising known and identifying new targets. Here we (i) briefly discuss the importance of carefully translating conceptual ore deposit models into effective exploration targeting maps, (ii) propose and describe what we term exploration information systems (EIS): a new idea for an information system designed to better integrate the conceptual mineral deposit model (i.e., the critical and constituent processes of the targeted mineral system) with data available to support exploration targeting, and (iii) discuss how best to categorise mineral systems in an EIS as scale-dependent subsystems to form mineral deposits. Our vision for the future use of EIS in exploration targeting is one whereby the mappable ingredients of a targeted mineral system are translated and combined into a set of weighted evidence (or proxy) maps automatically, resulting in an auto-generated mineral prospectivity map and a series of ranked exploration targets. We do not envisage the EIS replacing human input and ingenuity; rather we envisage the EIS as an additional tool in the exploration toolbox and as an intelligence amplifying system in which humans are making use of machines to achieve the best possible results.

Introduction

Mineral exploration targeting requires the compilation, integration and interrogation of diverse, multi-disciplinary data (e.g., Hronsky and Groves, 2008). In today’s world, this is commonly done using a geographic information system (GIS), the light table environment of our modern computer-age. A GIS is a powerful computer-based system designed to capture, store, manipulate, analyse, manage, and present spatial data (e.g., Groves et al., 2000) and their non-spatial attributes that can be stored in linked, interrogatable tables. In mineral exploration targeting, as in many other fields of applied geoscience, GIS has surpassed the human ability to integrate and quantitatively analyse the ever-growing amount of geospatial data available, thereby progressively replacing the traditional working methods. Over the past two decades and driven by significant improvements in soft- and hardware capabilities, powerful GIS-based, algorithm-driven methods have been developed in support of exploration targeting. These methods fall under the umbrella term of mineral prospectivity mapping (MPM), also known as mineral prospectivity analysis, spatial predictive modelling or mineral potential modelling (e.g., Bonham-Carter, 1994; Pan and Harris, 2000; Carranza, 2008; Porwal and Kreuzer, 2010; Yousefi and Nykänen, 2017; Hronsky and Kreuzer, 2019).

The main objective of this study is to analyze and compare greenhouse gas emission profiles, dynamics thereof, and the climate change mitigation efforts of the major players in the global climate change arena, namely the G7 and BRICS countries that are accounting for more than 60% of the world greenhouse gas emissions as in 2017. Given that the energy sector is the major source of the greenhouse emissions in these countries, the framework of sustainable energy development indicators was applied to the comparative analysis. The analysis revealed the differences among the groups of countries in terms of greenhouse gas emission profiles and development trends of these profiles. The convergence of greenhouse gas emission profiles can be noticed between G7 and BRICS countries during 1990e2017 period. Greenhouse gas emissions per capita has decreased in G7 countries and increased in BRICS countries. Energy intensity, economic growth and carbon factor are the main drivers of greenhouse gas emissions per capita growth in both groups of countries. Carbon factor represents carbon intensity of a country’s energy sector and is measured by greenhouse gas emissions per unit of total primary energy supply. The impact of carbon factor was least significant among the main drivers in both groups. Energy intensity was the major driver of decrease in greenhouse gas emissions per capita for both groups, however, the economic growth exceeded the contribution of energy intensity in BRICS countries and greenhouse gas emissions have increased in this group during investigated period. The comparative analysis of greenhouse gas emission profiles development trends and the main drivers of these trends in both groups of countries provides important insights in shaping future climate change mitigation policies and developing greenhouse gas emission reduction targets. Due to the high greenhouse gas profiles, low GHG reduction commitments and insufficient climate change mitigation the Perform, Achieve and Trade (PAT) scheme in India efforts in both groups there is danger of postponing implementation of Paris commitments and achieving the 1.5
target. The policies implemented in high ranked countries, like or Integrated Resources Planning in South Africa or measures under Energy efficiency directive in EU can be applied in low ranked countries by providing the substantial contribution to the country’s climate targets.

Introduction

The climate change has become an important global issue that is related to energy, economics, environment, and technology. Without taking further actions to reduce the global greenhouse gas (GHG) emissions, global warming is likely to exceed 2
C above the pre-industrialized levels. Such development would have a huge impact on the world’s landscape and sea levels and affect the economic and social development of countries all over the world. Accordingly, it is necessary to identify the paths for GHG emission management in the major emitting countries, which are subjected to the international commitments and national policies. The Group 7 (G7) and BRICS countries account for more than 60% of the world GHG emissions excluding Land Use, Land Use Change and Forestry (LULUCF) as in 2017. These countries present different GHG emission profiles and trends under the climate change mitigation agenda (Climate Transparency, 2018; Climate Action Tracker, 2018). The G7 is an international group of seven world leading developed countries (Canada, France, Germany, Italy, Japan, the United Kingdom, the United States). The G7 represents more than 60% of the global net wealth and almost 50% of the global GDP. The five leading emerging economies compose BRICS (Brazil, Russia, India, China, and South Africa). The G7 and BRICS countries (except Russia) have ratified the Paris Agreement, and Trump Administration’s intent to withdraw from the Paris Agreement creates many concerns in other G7 countries and all over the world. Therefore, the climate change policies and the associated decarbonization actions in those countries are particularly important within the transition towards the low-carbon economy.

1- Introduction

2- Sustainable control objectives in freeway traffic control

3- The modelling framework for sustainable freeway traffic control

4- Sustainable freeway control strategies

5- Remarks on sustainable freeway traffic control

6- Future steps towards a greener mobility

References

Abstract

Sustainability is a key point in the design and management of mobility and traffic systems, which affects also the development of traffic control strategies for freeway networks. According to sustainability-related concepts, freeway traffic controllers should be devised not only for maximally exploiting the road capacity and decreasing vehicles travel delays, but also for reducing pollutant emissions, fuel consumptions, accidents, noise, and so on. This paper analyses the state of the art of freeway traffic control strategies characterised by sustainability-related objectives, in particular referred to traffic emissions and traffic safety, by providing a classification of the research papers in this area. The final part of the paper highlights the main research challenges for modelling and control techniques brought by the introduction of emerging information and communication technologies, that are becoming more and more widespread and are transforming the concept of vehicles in intelligent and connected agents, able to measure the traffic state and to implement specific traffic control policies.

Introduction

Nowadays, the greater awareness about the dangerous effects produced by myopic policies aimed only at pursuing short-term benefits has brought the issue of sustainable development to the centre of the political agenda of many countries worldwide. In recent years, in fact, the theme of sustainability has been addressed considering various fields of the human activity and proposing several definitions and targets (as those proposed in Kates, Parris, & Leiserowitz, 2005 and Giovannoni & Fabietti, 2014). Although concerning different aspects, such definitions agree that only the actions aimed at achieving the optimal relation between the humans and the environment can ensure a fair development of contemporary and future society. Yet, the concept of sustainability is extremely complex, since it requires the fulfilment of a mix of often conflicting objectives. The creation of sustainable cities, the promotion of a more equitable economic growth and the environmental safeguard represent just some of the objectives identified in United Nations (2017) to accomplish the 2030 Agenda for Sustainable Development (United Nations General Assembly, 2015). The implementation of the 2030 Program requires a rethinking of transportation systems in a more open-minded and environmentfriendly perspective. In this sustainable vision, the transport system should enhance the social equity and ensure a safer mobility by increasing the access to services and to the most disadvantaged areas, without damaging the environment and people’s health. This ambitious journey towards sustainable mobility starts from the awareness that the transport system, in particular the road sector, is today one of the leading sources of greenhouse gases and pollution, involving serious consequences both for human health and for the environment (Hoek, Brunekreef, Goldbohm, Fischer, & Brandt, 2002). A survey developed by the World Health Organization, on the basis of 2012 data, revealed that 3.7 millions of premature deaths may be attributed to environmental pollution. Most of these deaths and diseases are caused by the prolonged exposure to substances resulting from the use of fossil fuels.

The intermittent nature of wind energy raised multiple challenges to the power systems and is the biggest challenge to declare wind energy a reliable source. One solution to overcome this problem is wind energy forecasting. A precise forecast can help to develop appropriate incentives and wellfunctioning electric markets. The paper presents a comprehensive review of existing research and current developments in deterministic wind speed and power forecasting. Firstly, we categorize wind forecasting methods into four broader classifications: input data, time-scales, power output, and forecasting method. Secondly, the performance of wind speed and power forecasting models is evaluated based on 634 accuracy tests reported in twenty-eight published articles covering fifty locations of ten countries. From the analysis, the most significant errors were witnessed for the physical models, whereas the hybrid models showed the best performance. Although, the physical models have a large normalized root mean square error values but have small volatility. The hybrid models perform best for every time horizon. However, the errors almost doubled at the medium-term forecast from its initial value. The statistical models showed better performance than artificial intelligence models only in the very short term forecast. Overall, we observed the increase in the performance of forecasting models during the last ten years such that the normalized mean absolute error and normalized root mean square error values reduced to about half the initial values.

Introduction

In recent years, wind power is the most competitively priced technology in many markets. According to Global Wind Energy Council (GWEC) Annual Report 2018 [1], the cumulative wind power installed during 2001 to 2018 is 591 GW that is expected to reach 908 GW by the end of 2023 as shown in Fig. 1. Despite providing more than half of renewables growth [2], the intermittent nature of wind raised multiple challenges to the power systems and is the biggest challenge to declare wind energy a reliable source. The challenges that raised to the power system due to the intermittent nature of wind includes planning and operational difficulties, quality of power, and standard of inter-connections. For example, the system operator needs to allocate additional energy reserves in case any power fluctuation occurs between programmed and actual power produced. This additional reserves would increase the operational costs, which subsequently increases the final energy prices [3]. Albadi and Saadany discussed a detailed review of wind power intermittency impacts on power systems [4]. One solution to overcome this problem is wind energy forecasting. A precise forecast would help to develop appropriate incentives and well-functioning hour-a-head or dayahead electric markets [5].