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Integrated Disaster Risk Management and Remote Sensing in the Age of Intelligence 2022

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (12 August 2022) | Viewed by 10666

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Prof. Dr. Jason Levy

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Disaster Preparedness and Emergency Management, University of Hawaii, 2540 Dole Street, Honolulu, HI 96822, USA
Interests: epidemiology and prevention of congenital anomalies; psychosis and affective psychosis; cancer epidemiology and prevention; molecular and human genome epidemiology; evidence synthesis related to public health and health services research
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Special Issue Information

Dear Colleagues,

This Special Issue focuses on advances in remote sensing and GIS for hazards analysis, emergency studies, and disaster risk reduction in the age of intelligence. Governments around the world are investing in remote sensing for integrated all-hazard management in the age of artificial intelligence. This Special Issue will focus on technological transformations and ways in which advances in GIS and remote sensing can reduce disaster risk and increase integrated, all hazards, and comprehensive emergency management. This Special Issue also emphasizes that reducing disaster risk and investing in remote sensing technology can boost overall economic productivity, save lives, minimize damage to critical infrastructure and revitalize the economy.

Prof. Dr. Jason Levy
Guest Editor

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Published Papers (4 papers)

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Research

16 pages, 3961 KiB

Open AccessArticle

An Intelligent Sensor Based Decision Support System for Diagnosing Pulmonary Ailment through Standardized Chest X-ray Scans

by Shivani Batra, Harsh Sharma, Wadii Boulila, Vaishali Arya, Prakash Srivastava, Mohammad Zubair Khan and Moez Krichen

Sensors 2022, 22(19), 7474; https://doi.org/10.3390/s22197474 - 02 Oct 2022

Cited by 18 | Viewed by 1649

Abstract

Academics and the health community are paying much attention to developing smart remote patient monitoring, sensors, and healthcare technology. For the analysis of medical scans, various studies integrate sophisticated deep learning strategies. A smart monitoring system is needed as a proactive diagnostic solution that may be employed in an epidemiological scenario such as COVID-19. Consequently, this work offers an intelligent medicare system that is an IoT-empowered, deep learning-based decision support system (DSS) for the automated detection and categorization of infectious diseases (COVID-19 and pneumothorax). The proposed DSS system was evaluated using three independent standard-based chest X-ray scans. The suggested DSS predictor has been used to identify and classify areas on whole X-ray scans with abnormalities thought to be attributable to COVID-19, reaching an identification and classification accuracy rate of 89.58% for normal images and 89.13% for COVID-19 and pneumothorax. With the suggested DSS system, a judgment depending on individual chest X-ray scans may be made in approximately 0.01 s. As a result, the DSS system described in this study can forecast at a pace of 95 frames per second (FPS) for both models, which is near to real-time. Full article

(This article belongs to the Special Issue Integrated Disaster Risk Management and Remote Sensing in the Age of Intelligence 2022)

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26 pages, 8959 KiB

Open AccessArticle

A Preliminary Contribution towards a Risk-Based Model for Flood Management Planning Using BIM: A Case Study of Lisbon

by Graziella Del Duca, Gustavo Rocha, Marta Orszt and Luis Mateus

Sensors 2022, 22(19), 7456; https://doi.org/10.3390/s22197456 - 01 Oct 2022

Cited by 2 | Viewed by 2052

Abstract

Preparing a city for the impact of global warming is becoming of major importance. Adopting climate-proof policies and strategies in response to climate change has become a fundamental element for city planning. To this end, this research considers a multidisciplinary approach, at the local scale, able to connect urban planning and architecture, as a vital base for considering a coastal cities’ ability to control the consequences of climate change, specifically floods. So far, there is a scarcity of research connecting sea ground and land surveys, and this study could become a foundational reference for coastline settlement management using BIM. We found in BIM (Building Information Modeling) a possible tool for managing coastal risk, since it can combine crowdsourced data for geometric and information modeling of the city. The proposed BIM model includes a topography used for 3D thematic maps, a riverbed model, and a waterway model. This model aims to facilitate coordination across separate actors and interests since the urban area model is always updatable and improvable. Focusing on a case study of Lisbon, we developed risk-based 3D maps of the area close to the shoreline of the Tagus River. Full article

(This article belongs to the Special Issue Integrated Disaster Risk Management and Remote Sensing in the Age of Intelligence 2022)

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17 pages, 3472 KiB

Open AccessArticle

Land Use Planning to Reduce Flood Risk: Opportunities, Challenges and Uncertainties in Developing Countries

by Rita Der Sarkissian, Mario J. Al Sayah, Chadi Abdallah, Jean-Marc Zaninetti and Rachid Nedjai

Sensors 2022, 22(18), 6957; https://doi.org/10.3390/s22186957 - 14 Sep 2022

Cited by 2 | Viewed by 4190

Abstract

Land use planning for flood risk reduction has been significantly addressed in literature. However, a clear methodology for flood mitigation oriented land-use planning and its implementation, particularly in developing countries like Lebanon, is still missing. Knowledge on land use planning is still in its earliest stages in Lebanon. A lack of hazard-informed land use planning coupled to random land cover pattern evolution characterize the country. In response, this study focuses on the opportunities, challenges and uncertainties resulting from the integration of land use planning into efficient Disaster Risk Reduction (DRR). For this purpose, GIS-based analyses were first conducted on the current land use/land cover (LU/LC) of the Assi floodplain. Then, the areas land cover was retraced and its evolution after several flood occurrences was assessed. Subsequently, a flood hazard-informed LU/LC plan was proposed. The latter is mainly based on the spatial allocation of land-uses with respect to different flood hazard levels. This approach resulted in the production of a land use planning matrix for flood risk reduction. The matrix approach can serve as a tool for designing sustainable and resilient land cover patterns in other similar contexts while simultaneously providing robust contributions to decision-making and risk communication. Full article

(This article belongs to the Special Issue Integrated Disaster Risk Management and Remote Sensing in the Age of Intelligence 2022)

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17 pages, 4468 KiB

Open AccessArticle

Comparison of Different Machine Learning Methods for Predicting Cation Exchange Capacity Using Environmental and Remote Sensing Data

by Sanaz Saidi, Shamsollah Ayoubi, Mehran Shirvani, Kamran Azizi and Mojtaba Zeraatpisheh

Sensors 2022, 22(18), 6890; https://doi.org/10.3390/s22186890 - 13 Sep 2022

Cited by 10 | Viewed by 1526

Abstract

This study was conducted to examine the capability of topographic features and remote sensing data in combination with other auxiliary environmental variables (geology and geomorphology) to predict CEC by using different machine learning models ((random forest (RF), k-nearest neighbors (kNNs), Cubist model (Cu), and support vector machines (SVMs)) in the west of Iran. Accordingly, the collection of ninety-seven soil samples was performed from the surface layer (0–20 cm), and a number of soil properties and X-ray analyses, as well as CEC, were determined in the laboratory. The X-ray analysis showed that the clay types as the main dominant factor on CEC varied from illite to smectite. The results of modeling also displayed that in the training dataset based on 10-fold cross-validation, RF was identified as the best model for predicting CEC (R² = 0.86; root mean square error: RMSE = 2.76; ratio of performance to deviation: RPD = 2.67), whereas the Cu model outperformed in the validation dataset (R² = 0.49; RMSE = 4.51; RPD = 1.43)). RF, the best and most accurate model, was thus used to prepare the CEC map. The results confirm higher CEC in the early Quaternary deposits along with higher soil development and enrichment with smectite and vermiculite. On the other hand, lower CEC was observed in mountainous and coarse-textured soils (silt loam and sandy loam). The important variable analysis also showed that some topographic attributes (valley depth, elevation, slope, terrain ruggedness index—TRI) and remotely sensed data (ferric oxides, normalized difference moisture index—NDMI, and salinity index) could be considered as the most imperative variables explaining the variability of CEC by the best model in the study area. Full article

(This article belongs to the Special Issue Integrated Disaster Risk Management and Remote Sensing in the Age of Intelligence 2022)

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