Predictive Statistical Cost Estimation Model for Existing Single Family Home Elevation Projects

One of the most preferred flood mitigation techniques for existing homes is raising the elevation of the lowest floor above the base flood elevation (BFE). Determination of project effectiveness through benefit-cost analysis (BCA) relies on the expected avoided flood loss and the project cost. Conventional construction cost estimates are highly detailed, considering specific details of the project; however, mitigation project decisions must often be made while considering only highly generalized building details. To provide a robust, generalized project cost estimation method, this paper implements data modeling and mining methods such as multiple regression, random forest, generalized additive model (GAM), and model evaluation and selection with cross-validation methods to hindcast elevation costs for existing single-family homes based on average floor area, increase in floor elevation, number of stories, and foundation type. Project cost data for homes elevated in Louisiana, United States, between 2005 and 2015 are used in cost prediction analysis. The statistical modeling results are compared with detailed estimations for several types of home foundations over a range of elevations. The results show substantial agreement between regression predictions and detailed estimates using RSMeans cost data.

Neglecting uncertainties biases house-elevation decisions to manage riverine flood risks

Homeowners around the world elevate houses to manage flood risks. Deciding how high to elevate a house poses a nontrivial decision problem. The U.S. Federal Emergency Management Agency (FEMA) recommends elevating existing houses to the Base Flood Elevation (the elevation of the 100-year flood) plus a freeboard. This recommendation neglects many uncertainties. Here we analyze a case-study of riverine flood risk management using a multi-objective robust decision-making framework in the face of deep uncertainties. While the quantitative results are location-specific, the approach and overall insights are generalizable. We find strong interactions between the economic, engineering, and Earth science uncertainties, illustrating the need for expanding on previous integrated analyses to further understand the nature and strength of these connections. Considering deep uncertainties surrounding flood hazards, the discount rate, the house lifetime, and the fragility can increase the economically optimal house elevation to values well above FEMA’s recommendation.

FLOOD EARLY WARNING INFORMATION SYSTEM FOR MULTILOCATION BASED ANDROID

Development of flood early warning technology has grown rapidly. The technology has led to improvements in terms of communication and information technology. The use of the Internet of Things model (IOTs) has provided significant development to the development of early warning information systems. In this article is the development of a prototype model of flood monitoring information systems using Android has been designed by combining ultrasonic sensors as a water level detector, rain sensor, temperature sensor and moisture sensor. Arduino Uno Microcontroller Module used as sensor data processor, U-Blox Neo 6m GPS module as location detection and Ethernet module as sender of sensor data to station of flood early warning information system. The design of the prototype produces information on flood elevation, rain conditions, ambient temperature and soil moisture along with its location based on Google Maps interface on mobile android.

Uncertainty in Flood Mitigation Practices: Assessing the Economic Benefits of Property Acquisition and Elevation in Flood-Prone Communities

Adopting effective flood mitigation practices for repetitive flood events in the United States continues to play a prominent role in preventing future damage and fostering resilience to residential flooding. Two common mitigation practices for reducing residential flood risk consist of raising an existing structure to or above base flood elevation (BFE) and acquiring chronically damaged properties in flood prone areas and restoring them back to serve their natural functions as green open spaces. However, due to data accuracy limitations, decision makers are faced with the challenge of identifying the financially optimal approach to implementing mitigation measures. We address this problem through the following research questions: What does the optimal allocation of flood mitigation resources look like under data uncertainty, and what are the optimal methods to combining mitigation measures with consideration for the best economic benefits? Using a robust decision making (RDM) approach, the effects of uncertainty in property values, construction and demolition costs, and policy implementation options such as structure selection and budget allocation were measured. Our results indicate that the amount budgeted for mitigation and how those funds are allocated directly influence the selection of the most economically viable mitigation practices. Our research also contributes to the growing need for evaluating specific flood mitigation strategies.

Purwarupa Alat Deteksi Ketinggian Banjir Perbantuan SMS Gateway Dan Mikrontroler

<em>Flood disasters still occur regularly and continuously in Indonesia. Flooding can occur due to the volume of water in the river beyond the river body. Many impacts caused by flooding, not only material losses, flooding can also cause loss of life. The impact of flooding can be reduced if people are better prepared to face the flood. One way is to quickly disseminate information on river water levels to the community. It is necessary to make a solution on how to design an automatic sluice using Arduino UNO R3 and how to monitor the water situation during floods. The working principle of this tool uses an ultrasonic sensor as a water level detector, Arduino as a data processor, servo motor as opening and closing the door bar automatically and the modem as an SMS notification. Because design based detection system is needed In this study there are two formulations of the problem (i) How to design flood altitude detection devices using Arduino uno r3 which can open and close automatically. (ii) How to test the flood altitude detection system using a wavecome modem. The research objective is divided into two parts (i) Creating a series of flood elevation devices using Arduino r3 so that it can open and close automatically (ii) Gets the results of flood elevation system testing with an sms gateway</em>

Vulnerability assessment of residential buildings to tidal flood hazards in Sriwulan Village, Sayung District, Demak Regency

Tidal floods are among the destructive hazards to coastal settlements. In December of 2017, extreme tidal floods impacted 3, 500 houses in Sriwulan Village, Sayung District, Demak Regency. This research was intended to (1) asses the vulnerability levels of the residential buildings and (2) analyze the most influencing factors. The assessment was based on the scenarios built with a 150cm-high tidal flood, as observed during the 2017 event and projected for the subsequent five years (2022). The Analytical Hierarchy Process (AHP) and Spatial Multi-Criteria Evaluation (SMCE) were used in four scenarios, namely, hazard, physical, environmental, and economic. The equal scenario was also developed as a comparison to the first four scenarios to achieve the second objective. Based on the physical, environmental, and equal scenarios, 22 houses distributed throughout most of the areas of Nyangkringan Sub-Village fell into the category of highly vulnerable. The most determinants of the vulnerability are related to the physical and environmental parameters. The former includes the design flood elevation, building maintenance, and building materials, while the latter consists of the source of tidal floods and their preventive measures, distance to water bodies, and accessibility.

An objective and efficient method for estimating probabilistic coastal inundation hazards

The joint probability method (JPM) is the traditional way to determine the base flood elevation due to storm surge, and it usually requires simulation of storm surge response from tens of thousands of synthetic storms. The simulated storm surge is combined with probabilistic storm rates to create flood maps with various return periods. However, the map production requires enormous computational cost if state-of-the-art hydrodynamic models with high-resolution numerical grids are used; hence, optimal sampling (JPM-OS) with a small number of (~ 100–200) optimal (representative) storms is preferred. This paper presents a significantly improved JPM-OS, where a small number of optimal storms are objectively selected, and simulated storm surge responses of tens of thousands of storms are accurately interpolated from those for the optimal storms using a highly efficient kriging surrogate model. This study focuses on Southwest Florida and considers ~ 150 optimal storms that are selected based on simulations using either the low fidelity (with low resolution and simple physics) SLOSH model or the high fidelity (with high resolution and comprehensive physics) CH3D model. Surge responses to the optimal storms are simulated using both SLOSH and CH3D, and the flood elevations are calculated using JPM-OS with highly efficient kriging interpolations. For verification, the probabilistic inundation maps are compared to those obtained by the traditional JPM and variations of JPM-OS that employ different interpolation schemes, and computed probabilistic water levels are compared to those calculated by historical storm methods. The inundation maps obtained with the JPM-OS differ less than 10% from those obtained with JPM for 20,625 storms, with only 4% of the computational time.

ADVANCES AND ISSUES IN UNCERTAINTY QUANTIFICATION FOR COASTAL FLOOD HAZARDS

Probabilistic flood hazard assessments have advanced substantially, with modern methods for dealing with the risk from tropical cyclones utilizing either a variation of the joint probability method with optimal sampling (JPM-OS)2,3 or the statistical deterministic track method (SDTM)1,4. In the JPM-OS, tropical cyclones are reduced to a set of 5 to 9 parameters, whose characteristics are analyzed statistically to develop a joint probability distribution for tropical cyclones of given characteristics. In the SDTM, cyclogenesis of a large number of storms is seeded via a statistical model from historical data, then storms are propagated using one of several different methods, incorporating varying degrees of the physics of cyclone transformation as the storms propagate. Due to the significant cost of storm surge simulations, some form of optimization or selection is then performed to reduce the number of synthetic storms that must be simulated to determine the flood elevation corresponding to a given recurrence interval (e.g. the so-called 100-year flood). In both methods, substantial uncertainties exist, which have a tendency to increase the estimated flooding risk. Efforts to account for these uncertainties have varied, and there remains significant work to be done. Here, we demonstrate how these uncertainties tend to increase the flood risk and show that additional sources of uncertainty remain to be accounted for.

Direct local building inundation depth determination in 3-D point clouds generated from user-generated flood images

In recent years, the number of people affected by flooding caused by extreme weather events has increased considerably. In order to provide support in disaster recovery or to develop mitigation plans, accurate flood information is necessary. Particularly pluvial urban floods, characterized by high temporal and spatial variations, are not well documented. This study proposes a new, low-cost approach to determining local flood elevation and inundation depth of buildings based on user-generated flood images. It first applies close-range digital photogrammetry to generate a geo-referenced 3-D point cloud. Second, based on estimated camera orientation parameters, the flood level captured in a single flood image is mapped to the previously derived point cloud. The local flood elevation and the building inundation depth can then be derived automatically from the point cloud. The proposed method is carried out once for each of 66 different flood images showing the same building façade. An overall accuracy of 0.05 m with an uncertainty of ±0.13 m for the derived flood elevation within the area of interest as well as an accuracy of 0.13 m ± 0.10 m for the determined building inundation depth is achieved. Our results demonstrate that the proposed method can provide reliable flood information on a local scale using user-generated flood images as input. The approach can thus allow inundation depth maps to be derived even in complex urban environments with relatively high accuracies.