How an adaptive and flexible short-term flood planning can be beneficial - a UK case study application

2020 ◽  
Author(s):  
Mengke Ni ◽  
Tohid Erfani
Keyword(s):  
Flood Protection ◽  
Scenario Tree ◽  
Mixed Integer ◽  
Model Parameters ◽  
Integer Optimization ◽  
Short Term ◽  
Location Selection ◽  
Multi Stage ◽  
Flood Planning

<p>Short term flood intervention planning includes identifying how the limited resources should be allocated to the most appropriate affected locations. The water level is an important factor for temporary flood protection planning for which adaptability of the plan to its changing future condition is regarded valuable. Moreover, flexibility in activation, delaying and replacement of the existing plans should be considered to mitigate the damages caused by future unknown condition. This research applies real options analysis which incorporates adaptability and flexibility in addressing “least-cost alternative”  location selection via multi-stage stochastic programming. We apply the proposed model to a case study in Eden catchment with nine different flood-affected cities with different degrees of uncertainty along Eden River in England. A multi-objective and mixed integer optimization model was formulated to solve on a scenario tree capable to choose most appropriate locations for deploying intervention measures of temporary flood protection. We examine the solution under various model parameters uncertainty and compare the results with the business as usual case presenting the benefits of proposed formulation in terms of expected damage and cost.</p>

scholarly journals On the combination of water emergency wells and mobile treatment systems: a case study of the city of Berlin

2020 ◽  
Author(s):  
Christoph Stallkamp ◽  
Florian Diehlmann ◽  
Markus Lüttenberg ◽  
Marcus Wiens ◽  
Rebekka Volk ◽  
...  
Keyword(s):  
Water Treatment ◽  
Water Supply ◽  
Mixed Integer ◽  
Covering Problem ◽  
Water Supply Systems ◽  
Integer Optimization ◽  
Mobile Water ◽  
Trade Offs ◽  
The City

Abstract A shortage of water leads to severe consequences for populations. Recent examples like the ongoing water shortage in Kapstadt or in Gloucestershire in 2007 highlight both the challenges authorities face to restore the water supply and the importance of installing efficient preparedness measures and plans. This study develops a proactive planning approach of emergency measures for possible impairments of water supply systems and validates this with a case study on water contamination in the city of Berlin. We formulate a capacitated maximal covering problem as a mixed-integer optimization model where we combine existing emergency infrastructure with the deployment of mobile water treatment systems. The model selects locations for mobile water treatment systems to maximize the public water supply within defined constraints. With the extension to a multi-objective decision making model, possible trade-offs between the water supply coverage and costs, and between the coverage of differently prioritized demand points are investigated. Therefore, decision makers benefit from a significantly increased transparency regarding potential outcomes of their decisions, leading to improved decisions before and during a crisis.

A state-based and optimal path dependant short-term flood planning 

2021 ◽  
Author(s):  
Mengke Ni ◽  
Tohid Erfani
Keyword(s):  
Large Scale ◽  
Optimal Path ◽  
Weather Condition ◽  
Optimal Solution ◽  
Scenario Tree ◽  
Short Term ◽  
Planning Decisions ◽  
Proposed Model ◽  
Flood Planning ◽  
Deployment Planning

<p>Temporary flood protective defences (TFPD) are supplementary to permanent engineering solutions. In a flood event, asset managers are faced with a challenging task of deploying large-scale temporary defences at multiple locations. As the performance of temporary defences is sensitive to various uncertain weather condition factors, it is difficult to fix a single specific deployment plan as the optimal solution. This, moreover, leads to insufficient and/or underused defences on flood-affected locations. This paper describes a state-based (SB) mathematical modelling approach to deal with above challenge by adapting TFPD strategies consistently to short-term future as they unfold. We employ multistage stochastic and scenario tree to identify a set of alternative SB optimal paths for deployment planning. The proposed model is applied to nine flood-affected locations in Carlisle, northwest England. The results indicate that the inclusion of SB path-dependant solution strategy are beneficial for the flood asset manager faced with making short-term deployment planning decisions.</p>

How good does Automatic Model Structure Identification work? A Benchmark Study with 6915 Model Structures.

2021 ◽  
Author(s):  
Diana Spieler ◽  
Niels Schütze
Keyword(s):  
Optimization Algorithms ◽  
Calibration Procedure ◽  
Mixed Integer ◽  
Structure Identification ◽  
Model Parameters ◽  
Model Structure ◽  
Integer Optimization ◽  
Mixed Integer Optimization ◽  
Standard Calibration ◽  
Model Structures

<p>Recent investigations have shown it is possible to simultaneously calibrate model structures and model parameters to identify appropriate models for a given task (Spieler et al., 2020). However, this is computationally challenging, as different model structures may use a different number of parameters. While some parameters may be shared between model structures, others might be relevant for only a few structures, which theoretically requires the calibration of conditionally active parameters. Additionally, shared model parameters might cause different effects in different model structures, causing their optimal values to differ across structures. In this study, we tested how two current “of the shelf” mixed-integer optimization algorithms perform when having to handle these peculiarities during the automatic model structure identification (AMSI) process recently introduced by Spieler et al. (2020).</p><p>To validate the current performance of the AMSI approach, we conduct a benchmark experiment with a model space consisting of 6912 different model structures.  First, all model structures are independently calibrated and validated for three hydro-climatically differing catchments using the CMA-ES algorithm and KGE as the objective function. This is referred to as standard calibration procedure. We identify the best performing model structure(s) based on validation performance and analyze the range of performance as well as the number of structures performing in a similar range. Secondly, we run AMSI on all three catchments to automatically identify the most feasible model structure based on the KGE performance. Two different mixed-integer optimization algorithms are used – namely DDS and CMA-ES. Afterwards, we compare the results to the best performing models of the standard calibration of all 6912 model structures.</p><p>Within this experimental setup, we analyze if the best performing model structure(s) AMSI identifies are identical to the best performing structures of the standard calibration and if there are differences in performance when using different optimization algorithms for AMSI. We also validate if AMSI can identify the best performing model structures for a catchment at a fraction of the computational cost than the standard calibration procedure requires by using “off the shelf” mixed-integer optimization algorithms.</p><p> </p><p> </p><p> </p><p>Spieler, D., Mai, J., Craig, J. R., Tolson, B. A., & Schütze, N. (2020). Automatic Model Structure Identification for Conceptual Hydrologic Models. Water Resources Research, 56(9). https://doi.org/10.1029/2019WR027009</p>

Emplacing Berlin: Digital Itineraries and Short-term Study Abroad in the City

2018 ◽  
Vol 12 (1) ◽  
pp. 26-36 ◽  
Author(s):  
Richard B. Apgar
Keyword(s):  
Study Abroad ◽  
Urban Landscape ◽  
Physical Space ◽  
Short Term ◽  
Study Abroad Programs ◽  
Term Study ◽  
Digital Maps ◽  
Short Term Study ◽  
The City

As destination of choice for many short-term study abroad programs, Berlin offers students of German language, culture and history a number of sites richly layered with significance. The complexities of these sites and the competing narratives that surround them are difficult for students to grasp in a condensed period of time. Using approaches from the spatial humanities, this article offers a case study for enhancing student learning through the creation of digital maps and itineraries in a campus-based course for subsequent use during a three-week program in Berlin. In particular, the concept of deep mapping is discussed as a means of augmenting understanding of the city and its history from a narrative across time to a narrative across the physical space of the city. As itineraries, these course-based projects were replicated on site. In moving from the digital environment to the urban landscape, this article concludes by noting meanings uncovered and narratives formed as we moved through the physical space of the city.

The impact of Airbnb on a non-touristic city. A Case study of short-term rentals in Santa Cruz de Tenerife (Spain)

Erdkunde ◽  
2020 ◽  
Vol 74 (3) ◽  
pp. 191-204
Author(s):  
Marcus Hübscher ◽  
Juana Schulze ◽  
Felix zur Lage ◽  
Johannes Ringel
Keyword(s):  
Santa Cruz ◽  
Short Term ◽  
Rental Market ◽  
Rural Space ◽  
Use Of Data ◽  
Web Scraping ◽  
Per Capita ◽  
Per Se ◽  
The Impact

Short-term rentals such as Airbnb have become a persistent element of today’s urbanism around the globe. The impacts are manifold and differ depending on the context. In cities with a traditionally smaller accommodation market, the impacts might be particularly strong, as Airbnb contributes to ongoing touristification processes. Despite that, small and medium-sized cities have not been in the centre of research so far. This paper focuses on Santa Cruz de Tenerife as a medium-sized Spanish city. Although embedded in the touristic region of the Canary Islands, Santa Cruz is not a tourist city per se but still relies on touristification strategies. This paper aims to expand the knowledge of Airbnb’s spatial patterns in this type of city. The use of data collected from web scraping and geographic information systems (GIS) demonstrates that Airbnb has opened up new tourism markets outside of the centrally established tourist accommodations. It also shows that the price gap between Airbnb and the housing rental market is broadest in neighbourhoods that had not experienced tourism before Airbnb entered the market. In the centre the highest prices and the smallest units are identified, but two peripheral quarters stand out. Anaga Mountains, a natural and rural space, has the highest numbers of Airbnb listings per capita. Suroeste, a suburban quarter, shows the highest growth rates on the rental market, which implies a linkage between Airbnb and suburbanization processes.

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