Hazard and vulnerability in urban flood risk mapping: Machine learning techniques and considering the role of urban districts

2020 ◽  
Vol 50 ◽  
pp. 101687 ◽  
Author(s):  
Mohammad Eini ◽  
Hesam Seyed Kaboli ◽  
Mohsen Rashidian ◽  
Hossein Hedayat
Keyword(s):  
Machine Learning ◽  
Flood Risk ◽  
Risk Mapping ◽  
Machine Learning Techniques ◽  
Urban Flood ◽  
Urban Districts ◽  
Learning Techniques ◽  
Flood Risk Mapping ◽  
Urban Flood Risk

scholarly journals Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques

2019 ◽  
Vol 569 ◽  
pp. 142-154 ◽  
Author(s):  
Hamid Darabi ◽  
Bahram Choubin ◽  
Omid Rahmati ◽  
Ali Torabi Haghighi ◽  
Biswajeet Pradhan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Flood Risk ◽  
Risk Mapping ◽  
Machine Learning Techniques ◽  
Urban Flood ◽  
Learning Techniques ◽  
Flood Risk Mapping ◽  
Urban Flood Risk

scholarly journals Managing Large Geodatasets for Urban Flood Risk Mapping: The Mexican Flood Risk Atlas

10.29007/l6jd ◽  
2018 ◽  
Author(s):  
Laurent Guillaume Courty ◽  
Jose Agustín Breña-Naranjo ◽  
Adrián Pedrozo-Acuña
Keyword(s):  
Time Series ◽  
Flood Risk ◽  
Urban Areas ◽  
Risk Mapping ◽  
Post Processing ◽  
Urban Flood ◽  
Gis Database ◽  
Flood Risk Mapping ◽  
Flood Maps ◽  
Urban Flood Risk

We present a flood risk mapping framework created in the context of the update of the Mexican flood risk atlas. This framework is based on a nation-wide GIS database of map time-series. Those maps are used as forcing for a deterministic, raster-based numerical model. For each catchment of interest, the model retrieves the data from the GIS and perform the computation on the specified area. The results are written directly in the GIS database, which facilitate their post-processing. This methodology allows 1) the generation of flood risk maps in cities located across the national territory, without too much effort in the pre and post-processing of information and 2) a very efficient process to create new flood maps for urban areas that have not been included in the original batch.

scholarly journals Evaluating urban flood risk using hybrid method of TOPSIS and machine learning

2021 ◽  
pp. 102614
Author(s):  
Elham Rafiei Sardooi ◽  
Ali Azareh ◽  
Bahram Choubin ◽  
Amir Mosavi ◽  
John J. Clague
Keyword(s):  
Machine Learning ◽  
Hybrid Method ◽  
Flood Risk ◽  
Urban Flood ◽  
Urban Flood Risk

Machine Learning Techniques for Underwater Wireless Sensor Networks

2021 ◽  
pp. 194-211
Author(s):  
Deepti Rani ◽  
Anju Sangwan ◽  
Anupma Sangwan ◽  
Tajinder Singh
Keyword(s):  
Machine Learning ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Information Seeking ◽  
Complex Problem ◽  
Machine Learning Techniques ◽  
Wireless Sensor ◽  
Learning Techniques ◽  
Primary Focus

With the enormous growth of sensor networks, information seeking from such networks has become an invaluable source of knowledge for various organizations to enhance the comprehension of people interests. Not only wireless sensor networks (WSNs) but its various classes also remain the hot topics of research. In this chapter, the primary focus is to understand the concept of sensor network in underwater scenario. Various mechanisms are used to recognize the activities underwater using sensor which examines the real-time events. With these features, a few challenges are also associated with sensor networks, which are addressed here. Machine learning (ML) techniques are the perfect key of success to resolve such issues due to their feasibility and adaption in complex problem environment. Therefore, various ML techniques have been explained to enhance the operational performance of WSNs, especially in underwater WSNs (UWSNs). The main objective of this chapter is to understand the concepts of UWSNs and role of ML to address the performance issues of UWSNs.

Potential role of machine learning techniques for modeling the hardness of OPH steels

2020 ◽  
pp. 101806
Author(s):  
Omid Khalaj ◽  
Moslem Ghobadi ◽  
Alireza Zarezadeh ◽  
Ehsan Saebnoori ◽  
Hana Jirková ◽  
...  
Keyword(s):  
Machine Learning ◽  
Potential Role ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals The Role of Green Infrastructure Solutions in Urban Flood Risk Management

10.1596/25112 ◽  
2016 ◽  
Author(s):  
Salman Anees Soz ◽  
Jolanta Kryspin-Watson ◽  
Zuzana Stanton-Geddes
Keyword(s):  
Risk Management ◽  
Green Infrastructure ◽  
Flood Risk ◽  
Flood Risk Management ◽  
Urban Flood ◽  
Urban Flood Risk

scholarly journals The Role of Medication Data to Enhance the Prediction of Alzheimer’s Progression Using Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Shaker El-Sappagh ◽  
Tamer Abuhmed ◽  
Bader Alouffi ◽  
Radhya Sahal ◽  
Naglaa Abdelhade ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Multimodal Data ◽  
Learning Techniques ◽  
Early Progression ◽  
Neuroimaging Data

Early detection of Alzheimer’s disease (AD) progression is crucial for proper disease management. Most studies concentrate on neuroimaging data analysis of baseline visits only. They ignore the fact that AD is a chronic disease and patient’s data are naturally longitudinal. In addition, there are no studies that examine the effect of dementia medicines on the behavior of the disease. In this paper, we propose a machine learning-based architecture for early progression detection of AD based on multimodal data of AD drugs and cognitive scores data. We compare the performance of five popular machine learning techniques including support vector machine, random forest, logistic regression, decision tree, and K-nearest neighbor to predict AD progression after 2.5 years. Extensive experiments are performed using an ADNI dataset of 1036 subjects. The cross-validation performance of most algorithms has been improved by fusing the drugs and cognitive scores data. The results indicate the important role of patient’s taken drugs on the progression of AD disease.

Sign in / Sign up

Export Citation Format

Share Document