Advancing scenario planning through integrating urban growth prediction with future flood risk models

High uncertainty about future urbanization and flood risk conditions limits the ability to increase resiliency in traditional scenario-based urban planning. While scenario planning integrating urban growth prediction modeling is becoming more common, these models have not been effectively linked with future flood plain changes due to sea level rise. This study advances scenario planning by integrating urban growth prediction models with flood risk scenarios. The Land Transformation Model, a land change prediction model using a GIS based artificial neural network, is used to predict future urban growth scenarios for Tampa, Florida, USA, and future flood risks are then delineated based on the current 100-year floodplain using NOAA level rise scenarios. A multi-level evaluation using three urban prediction scenarios (business as usual, growth as planned, and resilient growth) and three sea level rise scenarios (low, high, and extreme) is conducted to determine how prepared Tampa's current land use plan is in handling increasing resilient development in lieu of sea level rise. Results show that the current land use plan (growth as planned) decreases flood risk at the city scale but not always at the neighborhood scale, when compared to no growth regulations (business as usual). However, flood risk when growing according to the current plan is significantly higher when compared to all future growth residing outside of the 100-year floodplain (resilient growth). Understanding the potential effects of sea level rise depends on understanding the probabilities of future development options and extreme climate conditions.     

Visualization of Object‐Centered Vulnerability to Possible Flood Hazards

As flood events tend to happen more frequently, there is a growing demand for understanding the vulnerability of infrastructure to flood‐related hazards. Such demand exists both for flood management personnel and the general public. Modern software tools are capable of generating uncertainty‐aware flood predictions. However, the information addressing individual objects is incomplete, scattered, and hard to extract. In this paper, we address vulnerability to flood‐related hazards focusing on a specific building. Our approach is based on the automatic extraction of relevant information from a large collection of pre‐simulated flooding events, called a scenario pool. From this pool, we generate uncertainty‐aware visualizations conveying the vulnerability of the building of interest to different kinds of flooding events. On the one hand, we display the adverse effects of the disaster on a detailed level, ranging from damage inflicted on the building facades or cellars to the accessibility of the important infrastructure in the vicinity. On the other hand, we provide visual indications of the events to which the building of interest is vulnerable in particular. Our visual encodings are displayed in the context of urban 3D renderings to establish an intuitive relation between geospatial and abstract information. We combine all the visualizations in a lightweight interface that enables the user to study the impacts and vulnerabilities of interest and explore the scenarios of choice. We evaluate our solution with experts involved in flood management and public communication.     

Structural plan similarity based on refinements in the space of partial plans

Plan similarity measures play a key role in many areas of artificial intelligence, such as case‐based planning, plan recognition, ambient intelligence, or digital storytelling. In this paper, we present 2 novel structural similarity measures to compare plans based on a search process in the space of partial plans. Partial plans are compact representations of sets of plans with some common structure and can be organized in a lattice so that the most general partial plans are above the most specific ones. To compute our similarity measures, we traverse this space of partial plans from the most general to the most specific using successive refinements. Our first similarity measure is designed for propositional plan formalisms, and the second is designed for classical planning formalisms (including variables and types). We also introduce 2 novel refinement operators used to traverse the space of plans: an ideal downward refinement operator for propositional partial plans and a finite and complete downward refinement operator for classical partial plans. Finally, we evaluate our similarity measures in the context of a nearest neighbor classifier using 2 datasets commonly used in the plan recognition literature (Linux and Monroe), showing good results in both synthetic and real data.     

Anytime search in dynamic graphs

Agents operating in the real world often have limited time available for planning their next actions. Producing optimal plans is infeasible in these scenarios. Instead, agents must be satisfied with the best plans they can generate within the time available. One class of planners well-suited to this task are anytime planners, which quickly find an initial, highly suboptimal plan, and then improve this plan until time runs out.A second challenge associated with planning in the real world is that models are usually imperfect and environments are often dynamic. Thus, agents need to update their models and consequently plans over time. Incremental planners, which make use of the results of previous planning efforts to generate a new plan, can substantially speed up each planning episode in such cases.In this paper, we present an A^∗-based anytime search algorithm that produces significantly better solutions than current approaches, while also providing suboptimality bounds on the quality of the solution at any point in time. We also present an extension of this algorithm that is both anytime and incremental. This extension improves its current solution while deliberation time allows and is able to incrementally repair its solution when changes to the world model occur. We provide a number of theoretical and experimental results and demonstrate the effectiveness of the approaches in a robot navigation domain involving two physical systems. We believe that the simplicity, theoretical properties, and generality of the presented methods make them well suited to a range of search problems involving dynamic graphs.     

Evaluating opportunistic networks in disaster scenarios

Forwarding data in scenarios where devices have sporadic connectivity is a challenge. An example scenario is a disaster area, where forwarding information generated in the incident location, like victims' medical data, to a coordination point is critical for quick, accurate and coordinated intervention. New applications are being developed based on mobile devices and wireless opportunistic networks as a solution to destroyed or overused communication networks. But the performance of opportunistic routing methods applied to emergency scenarios is unknown today. In this paper, we compare and contrast the efficiency of the most significant opportunistic routing protocols through simulations in realistic disaster scenarios in order to show how the different characteristics of an emergency scenario impact in the behaviour of each one of them.     

Contingency Planning for Large‐Scale Floods in the Netherlands

Contingency plans for hazards are based on scenarios at different scales. The most extreme scenarios reflect the idea of ‘think the unthinkable’. For large‐scale floods in the Netherlands, this idea has been given an upper limit called ‘worst credible floods’: an upper limit for floods that are still considered realistic or credible by experts. Considering the enormous impact of a worst credible flood in the Netherlands and the uncertainty of how a disaster might unfold, a realistic preparation for flood disasters should leave room for improvisation and should be based on relatively simple plans, and on public awareness. The huge consequences of worst credible floods show that the country's safety will continue to depend on pro‐active and preventive measures.     

Enterprisewide Information Management: State-of-the-Art Strategic Planning

MIS plans compete with many other potential business investments and business problems for the attention of senior management. Consequently, a strategic planning methodology should not only produce a plan linked to business planning but also should create a persuasive case for its support. This article examines the state of the art in strategic planning in terms of enterprisewide information management (EwIM), which is a set of concepts and tools that enable MIS managers to plan, organize, implement, and control information resources to meet current and future strategic goals.     

Tools to Improve the Production of Emergency Plans for Floods: Are They Being Used by the People that Need Them?

Recently emergency plans for floods have started to be developed in England and France. At present, tools, such as checklists, guidance and specialized software appear to be used rarely to improve the effectiveness of these plans. Research was undertaken with flood managers in the two countries. The objective was to establish why tools that can usefully contribute to improving emergency plans for floods are often not being used. Many flood managers are not aware of the tools that are available to assist them in formulating emergency plans for floods. It was concluded that there is a need for guidance on the tools and how they can be used to help to improve emergency planning for floods.     

Robust capacitated facility location model for acquisitions under uncertainty

We study a new robust formulation for strategic location and capacity planning considering potential company acquisitions under uncertainty. Long-term logistics network planning is among the most difficult decisions for supply-chain managers. While costs, demands, etc. may be known or estimated well for the short-term, their future development is uncertain and difficult to predict.A new model formulation for the robust capacitated facility location problem is presented to cope with uncertainty in planning. Minimizing the expectation of the relative regrets across scenarios over multiple periods is the objective. It is achieved by dynamically assigning multi-level production allocations, locations and capacity adjustments for uncertain parameter development over time. Considering acquisitions for profit maximization and its supply-chain impact is new as well as the simultaneous decision of capacity adjustment and facility location over time. The solution of the novel robust formulation provides a single setup where good results can be achieved for any realized scenario. Hence, the solution may not be optimal for one particular scenario but may be good, i.e. the highest expected profit to gain, for any highly probable future realization. We show that robust mixed-integer linear programming model achieves superior results to the deterministic configurations in exhaustive computational tests. This dynamic robust formulation allows the supply-chain to favorably adapt to acquisitions and uncertain developments of revenue, demand and costs and hence reduces the potential negative impacts of uncertainty on supply-chain operations.     

High-resolution urban change modeling and flood exposure estimation at a national scale using open geospatial data: A case study of the Philippines

Many developing countries in Asia are experiencing rapid urban expansion in climate hazard prone areas. To support climate resilient urban planning efforts, here we present an approach for simulating future urban land-use changes and evaluating potential flood exposure at a high spatial resolution (30 m) and national scale. As a case study, we applied this model to the Philippines – a country frequently affected by extreme rainfall events. Urban land-use changes were simulated to the year 2050 using a trend-based logistic regression cellular automata model, considering three different scenarios of urban expansion (assuming low/medium/high population growth). Flood exposure assessment was then conducted by overlaying the land-use simulation results onto a global floodplain map. We found that approximately 6040–13,850 ha of urban land conversion is likely to be located in flood prone regions between 2019 and 2050 (depending on the scenario), affecting approximately 2.5–5.8 million additional urban residents. In locations with high rates of future urban development in flood prone areas (Mindanao Island, in particular), climate resilient land-use plans should be developed/enforced, and flood mitigation infrastructure protected (in the case of “nature-based” infrastructure) or constructed. The data selected for our land-use change modeling and flood exposure assessment were all openly and (near-)globally available, with the intention that our methodology can potentially be applied in other countries where rapid urban expansion is occurring. The 2050 urban land-use maps generated in this study are available for download at https://www.iges.or.jp/en/pub/ph-urban2050/en to allow for their use in future works.     

Planning and execution through variable resolution planning

Generating sequences of actions–plans–for robots using Automated Planning in stochastic and dynamic environments has been shown to be a difficult task with high computational complexity. These plans are composed of actions whose execution might fail due to different reasons. In many cases, if the execution of an action fails, it prevents the execution of some (or all) of the remainder actions in the plan. Therefore, in most real-world scenarios computing a complete and sound (valid) plan at each (re-)planning step is not worth the computational resources and time required to generate the plan. This is specially true given the high probability of plan execution failure. Besides, in many real-world environments, plans must be generated fast, both at the start of the execution and after every execution failure. In this paper, we present Variable Resolution Planning which uses Automated Planning to quickly compute a reasonable (not necessarily sound) plan. Our approach computes an abstract representation–removing some information from the planning task–which is used once a search depth of $k$ steps has been reached. Thus, our approach generates a plan where the first $k$ actions are applicable if the domain is stationary and deterministic, while the rest of the plan might not be necessarily applicable. The advantages of this approach are that it: is faster than regular full-fledged planning (both in the probabilistic or deterministic settings); does not spend much time on the far future actions that probably will not be executed, since in most cases it will need to replan before executing the end of the plan; and takes into account some information of the far future, as an improvement over pure reactive systems. We present experimental results on different robotics domains that simulate tasks on stochastic environments.     

A heuristic technique for multi‐agent planning

The subject of multi‐agent planning has been of continuing concern in Distributed Artificial Intelligence (DAI). In this paper, we suggest an approach to multi‐agent planning that contains heuristic elements. Our method makes use of subgoals, and derived sub‐plans, to construct a global plan. Agents solve their individual sub‐plans, which are then merged into a global plan. The suggested approach reduces overall planning time and derives a plan that approximates the optimal global plan that would have been derived by a central planner, given those original subgoals. We explore three different scenarios. The first involves a group of agents with a common goal. The second considers how agents can interleave planning and execution when planning towards a common, though dynamic, goal. The third examines the case where agents, each with their own goal, can plan together to reach a state in consensus for the group. Finally, we consider how these approaches can be adapted to handle rational, manipulative agents. This revised version was published online in June 2006 with corrections to the Cover Date.     

High performance and safe flight of full‐scale helicopters from takeoff to landing with an ensemble of planners

Autonomous flight of unmanned full‐size rotor‐craft has the potential to enable many new applications. However, the dynamics of these aircraft, prevailing wind conditions, the need to operate over a variety of speeds and stringent safety requirements make it difficult to generate safe plans for these systems. Prior work has shown results for only parts of the problem. Here we present the first comprehensive approach to planning safe trajectories for autonomous helicopters from takeoff to landing. Our approach is based on two key insights. First, we compose an approximate solution by cascading various modules that can efficiently solve different relaxations of the planning problem. Our framework invokes a long‐term route optimizer, which feeds a receding‐horizon planner which in turn feeds a high‐fidelity safety executive. Secondly, to deal with the diverse planning scenarios that may arise, we hedge our bets with an ensemble of planners. We use a data‐driven approach that maps a planning context to a diverse list of planning algorithms that maximize the likelihood of success. Our approach was extensively evaluated in simulation and in real‐world flight tests on three different helicopter systems for duration of more than 109 autonomous hours and 590 pilot‐in‐the‐loop hours. We provide an in‐depth analysis and discuss the various tradeoffs of decoupling the problem, using approximations and leveraging statistical techniques. We summarize the insights with the hope that it generalizes to other platforms and applications.     

Reasoning about actions with loops via Hoare logic

Plans with loops are more general and compact than classical sequential plans, and gaining increasing attentions in artificial intelligence (AI). While many existing approaches mainly focus on algorithmic issues, few work has been devoted to the semantic foundations on planning with loops. In this paper, we first develop a tailored action language AL K, together with two semantics for handling domains with non-deterministic actions and loops. Then we propose a sound and (relative) complete Hoare-style proof system for efficient plan generation and verification under 0-approximation semantics, which uses the so-called idea offline planning and on-line querying strategy in knowledge compilation, i.e., the agent could generate and store short proofs as many as possible in the spare time, and then perform quick query by constructing a long proof from the stored shorter proofs using compositional rule. We argue that both our semantics and proof system could serve as logical foundations for reasoning about actions with loops.     

A case-based reasoning safety decision-support tool: Nextcase/safety

We developed, made suitable and tested a generic case-based reasoning platform, called Nextcase/safety, for helping safety managers taking decisions on prevention measures more efficiently. Recommendations provided by the software are based on quick and effective past similar incident retrieval and expertise-driven advice. Hence, the developed software makes it easier and more objective for safety managers to draw conclusions and to give recommendations based on the expertise embedded in past incidents when an incident occurs in their company. Nextcase/safety provides its expert user with suggestions and information and does not require an extensive amount of time. The most time-consuming phase is datasheet generation and inputting accident cases into the database. The program was tested using a real accident database from the marine industry. The case-study indicates the high potential of the Nextcase/safety software for all industrial safety managers. Furthermore, the pros and cons of the Nextcase/safety program for building a company accident database were indicated.     

An evolutionary approach towards contact plan design for disruption-tolerant satellite networks

Delay and disruption tolerant networks (DTNs) are becoming an appealing solution for satellite networks where nodes can temporarily store and carry in-transit data until a link with a suitable next-hop becomes available. Since satellite trajectories and orientation can be predicted, on-board routing schemes can base these forwarding decisions on a contact plan comprising all forthcoming communication opportunities. In general, contact plans are previously calculated on ground where their design can be optimized to consider not only available spacecraft resources but also the expected traffic which is largely foreseeable in space applications. Despite optimal contact plan design procedures exist, their computation complexity might result prohibitive even for medium-sized satellite networks. In this work, we propose an evolutionary algorithm to provide sub-optimal yet efficient and implementable contact plans in bounded time. In particular, we depict specific strategies such as encoding and repairing techniques to later evaluate the algorithm performance in a typical scenario demonstrating its usefulness for planning future DTN-based satellite networks.     

A web based DSS for the management of floods and wildfires (FLIRE) in urban and periurban areas

The FLIRE DSS is a web-based Decision Support System for the combined forest and flood risk management and planning. State of the art tools and models have been used in order to enable Civil Protection agencies and local stakeholders to take advantage of web based DSS with no need of local complex infrastructure and maintenance. Civil protection agencies can predict the behavior of a fire event using real time data and in that way to plan its efficient elimination. Also, they can implement “what-if” scenarios for areas prone to fire and thus develop plans for forest fire management. Flood services include flood maps and flood-related warnings; these become available to relevant authorities for visualization and further analysis on a daily basis. Real time weather data from ground stations provide the necessary inputs for the calculation of the fire model in real time and a high resolution weather forecast grid support flood modeling and “what-if” scenarios for the fire modeling. The innovations of the FLIRE DSS are the use of common Earth Observation (EO) data as the backbone of the system to produce data for the support of fire and flood models, the common use of weather related information, the distributed architecture of the system and the web-based access of it with no need for installation of dedicated software. All these can be accessed by all means of computer sources like PC, laptop, Smartphone and tablet either by normal network connection or by using 3G and 4G cellular network. The latter is important for the accessibility of the FLIRE DSS during firefighting or rescue operations during flood events. FLIRE DSS can be easily transferred to other areas with similar characteristics due to its robust architecture and its flexibility.     

Workforce planning and financing on a production/capital discrete‐time model

Traditionally, companies have considered production planning and financial commitments separately. Production planning involves planning when to produce how much of a product, while the financial commitment considers which loans to take and how to repay them. In the current difficult financial environment with new challenges and with different opportunities, such as short‐term flexible loans for paying salaries, these related problems must be considered together. In this paper, we model the two processes (production and cash flows) in a single framework, using a mixed‐integer programming discrete‐time formulation. When taken individually, each of the problems has been thoroughly discussed in the literature, while the combined version that also incorporates labor financial costs and workforce sizing is more scarce. The main contribution of the paper involves new strategies for financing labor costs in strong agreement with the company's production plan and financial commitments. The new strategy relates credit ceiling with employment funding, using a sequence of flexible short‐term loans. We consider applications and propose mathematical programming based tools that can be used by companies’ managers for conducting their own solutions analysis, following their own findings and discussion of alternative scenarios.     

KEEPING IS STRATEGIC PLANS OFF THE SHELF

Although many IS managers spend a lot of time and effort creating an effective IS strategic plan that reflects the IS as well as the business needs of the organization, they often do not spend nearly as much time keeping this vital plan current. This column offers several reasons why this happens and what IS managers can do to keep their IS plans as active and useful as possible.     

THE IMPACT OF SPATIAL VERSUS ECONOMIC MEASURES IN AN URBAN TRANSPORTATION PLAN

A multimodal model for traffic was developed by the Flemish administration. For the first time an integrated set of measures was taken into account to forecast statistics on car-traffic and the use of public transport systems during evening peak-hours. The measures can be classified in four groups: planning, infrastructure, regulation and financial. Each input is forecasted for 2010 by means of two opposite scenarios: a trend scenario and a policy (sustainable) scenario. The model was first tested for the Antwerp region on a very detailed geographical level. We sought to identify which measures had a significant influence on the results. As a result of a sensitivity analysis we conclude that even in the long run planning measures are least effective, followed by infrastructure measures that may stimulate local people to make use of public transport. Regulatory measures and especially financial measures have clearly the most significant impact on generation of passenger kilometres and modal choice during the evening rush hour.