Remote Sensing and GIS Contribution to a Natural Hazard Database in Western Saudi Arabia

Theilen‐Willige, Barbara; Wenzel, Helmut

doi:10.3390/geosciences9090380

Cited by 14 publications

(5 citation statements)

References 24 publications

(26 reference statements)

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“…Even though it has no permanent yearround rivers, the country is not immune to flash flooding, increasing frequency and severity. Flash floods caused by intense, continuous rainfall is the most common type of natural hazard in the KSA (Alamri, 2010;Youssef et al, 2016;Theilen-Willige and Wenzel, 2019).…”

Section: Discussionmentioning

confidence: 99%

An Investigation into Emergency Planning Requirements and Challenges of Disaster Management in the Kingdom of Saudi Arabia

Alrehaili

2021

IJDM

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Every year, the Kingdom of Saudi Arabia (KSA) is exposed to different natural hazards. However, flash floods have been the most common hazard during the previous few decades. Between 2000 and 2020, over 1,000 people lost their lives from flash floods, resulting in an economic loss amounting to billions of US dollars. By focusing on flash floods, a systematic review of the extracted data was conducted. They were analyzed based on the suitability of their content and data for emergency planning requirements and disaster management challenges in the KSA. A total of 104 articles, papers, and plans were reviewed, of which only 18 complete papers met the inclusion criteria, including one plan and the General Directorate of Civil Defense (GDCD) website. The author has concluded that: 1) the essential requirements for emergency planning in the KSA include: conducting studies that show potential natural hazards, their locations, and their implications, and taking appropriate measures that reduce the possible causes of natural hazards; 2) The challenges facing the disaster management in the KSA are: lack of policies; the ambiguity of legislation and plans; poor coordination between stakeholders; lack of databases. This is the first investigation into emergency planning requirements and challenges of disaster management in the KSA. Furthermore, a scientific consensus predicts an increase in the frequency and magnitude of flash floods in the KSA. Therefore, the gaps need to be addressed in order to reduce the impact on inhabitants and infrastructure.

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Section: Discussionmentioning

confidence: 99%

An Investigation into Emergency Planning Requirements and Challenges of Disaster Management in the Kingdom of Saudi Arabia

Alrehaili

2021

IJDM

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“…Satellite imagery is an indispensable resource for understanding and solving the world's complex problems and disasters, such as FFs [31]. With its robust and distinctive perspective on the Earth's surface, it allows us to study and monitor the environment, natural resources, and human activities on a global and local scale.…”

Section: Satellite Imagerymentioning

confidence: 99%

Google Earth Engine and Machine Learning for Flash Flood Exposure Mapping—Case Study: Tetouan, Morocco

SELLAMI,

Rhinane

2024

Geosciences

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Recently, the earth’s climate has changed considerably, leading to several hazards, including flash floods (FFs). This study aims to introduce an innovative approach to mapping and identifying FF exposure in the city of Tetouan, Morocco. To address this problem, the study uses different machine learning methods applied to remote sensing imagery within the Google Earth Engine (GEE) platform. To achieve this, the first phase of this study was to map land use and land cover (LULC) using Random Forest (RF), a Support Vector Machine (SVM), and Classification and Regression Trees (CART). By comparing the results of five composite methods (mode, maximum, minimum, mean, and median) based on Sentinel images, LULC was generated for each method. In the second phase, the precise LULC was used as a related factor to others (Stream Power Index (SPI), Topographic Position Index (TPI), Slope, Profile Curvature, Plan Curvature, Aspect, Elevation, and Topographic Wetness Index (TWI)). In addition to 2024 non-flood and flood points to predict and detect FF susceptibility, 70% of the dataset was used to train the model by comparing different algorithms (RF, SVM, Logistic Regression (LR), Multilayer Perceptron (MLP), and Naive Bayes (NB)); the rest of the dataset (30%) was used for evaluation. Model performance was evaluated by five-fold cross-validation to assess the model’s ability on new data using metrics such as precision, score, kappa index, recall, and the receiver operating characteristic (ROC) curve. In the third phase, the high FF susceptibility areas were analyzed for two-way validation with inundated areas generated from Sentinel-1 SAR imagery with coherent change detection (CDD). Finally, the validated inundation map was intersected with the LULC areas and population density for FF exposure and assessment. The initial results of this study in terms of LULC mapping showed that the most appropriate method in this research region is the use of an SVM trained on a mean composite. Similarly, the results of the FF susceptibility assessment showed that the RF algorithm performed best with an accuracy of 96%. In the final analysis, the FF exposure map showed that 2465 hectares were affected and 198,913 inhabitants were at risk. In conclusion, the proposed approach not only allows us to assess the impact of FF in this study area but also provides a versatile approach that can be applied in different regions around the world and can help decision-makers plan FF mitigation strategies.

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“…The tide amplitude at the time of a potential tsunami directly affects the inundation height as well and, hence, the impact of the tsunami on the coastal areas. Even in the case of small amplitude tsunamis, the combination of the tsunami with a higher tide might result in a higher wave height [19]. 9 and 10).…”

Section: Contribution Of Landsat and Sentinel 2 Satellite Data To The...mentioning

confidence: 99%

Flooding Hazard Assessment Considering Climate Change in the Coastal Areas of Java / Indonesia Based on Remote Sensing and GIS Data

Theilen‐Willige¹,

Budiyanto²

2021

Proceedings of the International Joint Conference on Arts and Humanities 2021 (IJCAH 2021)

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Evaluations of different satellite data and meteorologic data as well as digital elevation data, help to identify critical areas in Java, Indonesia exposed to flooding due to flash floods, storm surge, cyclones, meteo-tsunamis or tsunami waves. Data mining and map creation is the prerequisite for flooding hazard preparedness for every community and for the strengthening of resilience. Case studies from the areas of Surabaya are presented to demonstrate an example how free available geo-and satellite data can be used systematically for flooding hazard preparedness. Sea level rise due to climate change has to be considered and the flooding hazard preparedness adapted according to the actual and future situation. Evaluations of optical satellite data such as Landsat 8 and Sentinel 2 and radar data contribute to a better under-standing of the development of currents at the coast and their interactions with the coastal morphology.

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Remote Sensing and GIS Contribution to a Natural Hazard Database in Western Saudi Arabia

Cited by 14 publications

References 24 publications

An Investigation into Emergency Planning Requirements and Challenges of Disaster Management in the Kingdom of Saudi Arabia

An Investigation into Emergency Planning Requirements and Challenges of Disaster Management in the Kingdom of Saudi Arabia

Google Earth Engine and Machine Learning for Flash Flood Exposure Mapping—Case Study: Tetouan, Morocco

Flooding Hazard Assessment Considering Climate Change in the Coastal Areas of Java / Indonesia Based on Remote Sensing and GIS Data

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