Remote sensing-based proxies for urban disaster risk management and resilience: A review

© 2018 by the authors. Rapid increase in population and growing concentration of capital in urban areas has escalated both the severity and longer-term impact of natural disasters. As a result, Disaster Risk Management (DRM) and reduction have been gaining increasing importance for urban areas. Remote sensing plays a key role in providing information for urban DRM analysis due to its agile data acquisition, synoptic perspective, growing range of data types, and instrument sophistication, as well as low cost. As a consequence numerous methods have been developed to extract information for various phases of DRM analysis. However, given the diverse information needs, only few of the parameters of interest are extracted directly, while the majority have to be elicited indirectly using proxies. This paper provides a comprehensive review of the proxies developed for two risk elements typically associated with pre-disaster situations (vulnerability and resilience), and two post-disaster elements (damage and recovery), while focusing on urban DRM. The proxies were reviewed in the context of four main environments and their corresponding sub-categories: built-up (buildings, transport, and others), economic (macro, regional and urban economics, and logistics), social (services and infrastructures, and socio-economic status), and natural. All environments and the corresponding proxies are discussed and analyzed in terms of their reliability and sufficiency in comprehensively addressing the selected DRM assessments. We highlight strength and identify gaps and limitations in current proxies, including inconsistencies in terminology for indirect measurements. We present a systematic overview for each group of the reviewed proxies that could simplify cross-fertilization across different DRM domains and may assist the further development of methods. While systemizing examples from the wider remote sensing domain and insights from social and economic sciences, we suggest a direction for developing new proxies, also potentially suitable for capturing functional recovery

A Review of Remote Sensing-Based Proxies and Data Processing Methods for Urban Disaster Risk Management

Disaster risk management (DRM) and reduction has been gaining in importance as a result of increasing impacts of natural disasters. Reliable and informative data are the foundation of any comprehensive and effective DRM. Synoptic and multi-type remote sensing has become an essential tool for rapid acquiring of geospatial data, particularly for complex and dynamic urban areas. Accordingly, it has been used for the assessment of all components of the disaster risk cycle, ranging from disaster preparedness to rapid damage assessment. However, due to the complex and multifaceted characteristics of many urban elements, in particular social and economic activities and functions, accurate risk assessment that takes account of the varied and complex set of vulnerabilities and their associated dynamics continues to be very difficult, and direct remote sensing observations are frequently insufficient. Therefore, methods have been developed to indirectly estimate the risk, utilizing image-based proxies. In recent years, using proxies has become a predominant way for such measurements in the DRM field for both pre- and post-disaster phases, at times with similar proxies being used for both situations. For example, the presence of vegetation in urban areas is used as a proxy for both pre-event social vulnerability and for post-disaster recovery assessments. In addition, existing proxies do not sufficiently address all assessment requirements, e.g. there is no proxy for building-based functional damage assessment. Another persistent challenge is the extraction those proxies as a basis for automating the urban DRM process. Although several remote sensing data processing methods have been developed to derive information for DRM in recent years, extracting proxies from remote sensing data requires more accurate results in detecting objects and features. In this study we carried out a comprehensive review of remote sensing-based proxies for different urban DRM phases, identified duplications on efforts, inconsistencies in terminology, but also remaining gaps. With a specific focus on post-disaster recovery assessment, which particularly relies on measures to assess the progress in functions and processes, the review was then used as a basis for the development of new proxies and indicators. The focus is on developing robust proxies to go beyond the physical evaluation perspective, and to extract socio- economic information and functional assessment of urban areas using new strategies, such as multiple-proxies approach, and fusing object- and pattern-based proxies from various remote sensing data, including very-high resolution satellite and aerial images, drone data, LiDAR data. In addition, the reliability of current remote sensing data processing methods in extracting proxies will be discussed, and accordingly how remote sensing data processing methods can contribute to developing reliable proxies will be demonstrated (e.g. using new pattern recognition, texture, and object detection methods)

TEHRAN AIR POLLUTION MODELING USING LONG-SHORT TERM MEMORY ALGORITHM: AN UNCERTAINTY ANALYSIS

Air pollution is a major environmental issue in urban areas, and accurate forecasting of particles 10 μm or smaller (PM10) level is essential for smart public health policies and environmental management in Tehran, Iran. In this study, we evaluated the performance and uncertainty of long short-term memory (LSTM) model, along with two spatial interpolation methods including ordinary kriging (OK) and inverse distance weighting (IDW) for mapping the forecasted daily air pollution in Tehran. We used root mean square error (RMSE) and mean square error (MSE) to evaluate the prediction power of the LSTM model. In addition, prediction intervals (PIs), and Mean and standard deviation (STD) were employed to assess the uncertainty of the process. For this research, the air pollution data in 19 Tehran air pollution monitoring stations and temperature, humidity, wind speed and direction as influential factors were taken into account. The results showed that the OK had better RMSE and STD in the test (32.48 ± 9.8 μg/m3) and predicted data (56.6 ± 13.3 μg/m3) compared with those of the IDW in the test (47.7 ± 22.43 μg/m3) and predicted set (62.18 ± 26.1 μg/m3). However, in PIs, IDW ([0, 0.7] μg/m3) compared with the OK ([0, 0.5] μg/m3) had better performance. The LSTM model achieved in the predicted values an RMSE of 8.6 μg/m3 and a standard deviation of 9.8 μg/m3 and PIs between [2.7 ± 4.8, 14.9 ± 15] μg/m3

GROUNDWATER LEVEL PREDICTION USING DEEP RECURRENT NEURAL NETWORKS AND UNCERTAINTY ASSESSMENT

Groundwater is one of the most important sources of regional water supply for humans. In recent years, several factors have contributed to a significant decline in groundwater levels (GWL) in certain regions. As a result of climate change, such as temperature increase, rainfall decrease, and changes in relative humidity, it is necessary to investigate and model the effects of these factors on GWL. Although a number of researches have been conducted on GWL modeling with machine learning (ML) and deep learning (DL) algorithms, only a limited number of studies have reported model uncertainty. In this paper, GWL modeling of some piezometric wells has been conducted by considering the effects of the meteorological parameters with Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. The models were trained on one piezometric well data and predictions were executed on six other wells. To perform an uncertainty assessment, the models were run 10 times and their means were calculated. Subsequently, their standard deviations were considered to evaluate the outcomes. In addition, the prediction power of the models was validated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), and R-Squared (R2). Finally, for all the six wells that did not participate in the training phase, the prediction functions of the trained models were run 10 times and their accuracy was assessed. The results indicate that LSTM (R2=95.6895, RMSE=0.4744 m, NRMSE=0.0558, MAE=0.3383 m) had a better performance compared to that of GRU (R2=95.2433, RMSE=0.4984 m, NRMSE=0.0586, MAE=0.3658 m) on the GWL modeling

Dietary inflammatory index and elevated serum C-reactive protein: A systematic review and meta-analysis

Diet can affect the inflammatory state of the body. Accordingly, the dietary inflammatory index (DII) has been developed to quantify the inflammatory properties of food items. This study sought to investigate the association between dietary inflammation index (DII) and the odds ratio of elevated CRP (E-CRP) through a systematic review and meta-analysis study. The International electronic databases of PubMed, Web of Science (ISI), and Scopus were searched until May 2023 to find related articles. From 719 studies found in the initial search, 14 studies, with a total sample size of 59,941 individuals, were included in the meta-analysis. The calculated pooled odds ratio (OR) of E-CRP in the highest DII category was 1.39 (95% CI: 1.06, 1.14, test for heterogeneity: p =.63, and I2 =.0%) in comparison with the lowest DII category. Also, the results of this study showed that each unit increase in DII as a continuous variable generally elicited a 10% increase in the odds of E-CRP (OR 1.10, 95% CI 1.06, 1.14, test for heterogeneity: p =.63, and I2 =.0%). Subgroup meta-analyses showed that there is a higher E-CRP odds ratio for the articles that reported energy-adjusted DII (E-DII) instead of DII, the studies that measured CRP instead of hs-CRP, and the studies that used 24-h recall instead of FFQ as the instrument of dietary intake data collection. Individuals with a higher DII were estimated to have higher chances of developing elevated serum CRP. This value was influenced by factors such as the participants' nationality, instruments of data collection, methods used to measure inflammatory biomarkers, study design, and data adjustments. However, future well-designed studies can help provide a more comprehensive understanding of the inflammatory properties of diet and inflammatory serum biomarkers

Assembly and Cleaning of CSPs for High, Low, and UltraLow Volume Applications

ABSTRACT A JPL-led CSP Consortium of enterprises, composed of representing government agencies and private companies, recently joined together to pool in-kind resources for developing the quality and reliability of chip scale packages (CSPs) for a variety of projects. Since last year, more than 150 test vehicles, single-and double-sided multilayer PWBs, have been assembled and are presently being subjected to various environmental tests. Recent reliability data, specifically the impact of assembly underfill on reliability, is being presented in another paper in this conference. This paper presents lessons learned on assemblies at three facilities with high, low, and ultralow volume production

Synthesis and characterization of ZnO nanostructures using palm olein as biotemplate

Background: A green approach to synthesize nanomaterials using biotemplates has been subjected to intense research due to several advantages. Palm olein as a biotemplate offers the benefits of eco-friendliness, low-cost and scale-up for large scale production. Therefore, the effect of palm olein on morphology and surface properties of ZnO nanostructures were investigated. Results: The results indicate that palm olein as a biotemplate can be used to modify the shape and size of ZnO particles synthesized by hydrothermal method. Different morphology including flake-, flower- and three dimensional star-like structures were obtained. FTIR study indicated the reaction between carboxyl group of palm olein and zinc species had taken place. Specific surface area enhanced while no considerable change were observed in optical properties. Conclusion: Phase-pure ZnO particles were successfully synthesized using palm olein as soft biotemplating agent by hydrothermal method. The physico-chemical properties of the resulting ZnO particles can be tuned using the ratio of palm olein to Zn cation

Automatic Building Detection Based on Supervised Classification Using High Resolution Google Earth Images

This paper presents a novel approach to detect the buildings by automization of the training area collecting stage for supervised classification. The method based on the fact that a 3d building structure should cast a shadow under suitable imaging conditions. Therefore, the methodology begins with the detection and masking out the shadow areas using luminance component of the LAB color space, which indicates the lightness of the image, and a novel double thresholding technique. Further, the training areas for supervised classification are selected by automatically determining a buffer zone on each building whose shadow is detected by using the shadow shape and the sun illumination direction. Thereafter, by calculating the statistic values of each buffer zone which is collected from the building areas the Improved Parallelepiped Supervised Classification is executed to detect the buildings. Standard deviation thresholding applied to the Parallelepiped classification method to improve its accuracy. Finally, simple morphological operations conducted for releasing the noises and increasing the accuracy of the results. The experiments were performed on set of high resolution Google Earth images. The performance of the proposed approach was assessed by comparing the results of the proposed approach with the reference data by using well-known quality measurements (Precision, Recall and F1-score) to evaluate the pixel-based and object-based performances of the proposed approach. Evaluation of the results illustrates that buildings detected from dense and suburban districts with divers characteristics and color combinations using our proposed method have 88.4 % and 853 % overall pixel-based and object-based precision performances, respectively