Investigation of Soil Liquefaction and Flooding as a Hydrological Risk for Disaster Risk Reduction in the Construction of Water Reservoirs DOI
Pınar Sarı Çavdar

Опубликована: Янв. 1, 2024

Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI

Язык: Английский

Novel MCDA methods for flood hazard mapping: a case study in Hamadan, Iran DOI
Reza Bahramloo, Jun Wang, Mehdi Sepehri

и другие.

Stochastic Environmental Research and Risk Assessment, Год журнала: 2024, Номер 38(12), С. 4863 - 4881

Опубликована: Ноя. 13, 2024

Язык: Английский

Процитировано

3

Climate-resilient strategies for sustainable groundwater management in Mahanadi River basin of Eastern India DOI
Chiranjit Singha, Satiprasad Sahoo,

Nguyen Dang Tinh

и другие.

Acta Geophysica, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 20, 2024

Язык: Английский

Процитировано

2

Enhancing Urban Resilience to Flooding in Hydrogeological Risk Areas Through Big Data Analytics Using Deep Neuro-Fuzzy System DOI
Varun Malik,

John Martin,

Ruchi Mittal

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Июль 23, 2024

Abstract Urban areas worldwide are increasingly at risk from hydrogeological hazards, leading to severe consequences. flooding and mismanagement of water resources, resulting in riverine flooding, primary contributors this risk. Utilizing big data, including mobile phone signals collected high frequencies, alongside administrative is essential for developing exposure indicators smaller urban regions. Accurately assessing human traffic flows movements crucial mitigating the impacts natural disasters ensuring a quality life smart cities. However, comprehensive solutions these challenges lacking many countries. Therefore, study focuses on analyzing impact data flow analysis areas. The employs as analyze forecast risks aid decision-making. To ensure reliability, circle search integrated fully connected conditional neural network (CS-ConNN) used cleaning, categorizing signal into normal, empty, garbage. Additionally, uses deep recurrent neuro fuzzy system (DRNFS) compound seasonality circulation risks, providing alerts individuals transiting through affected model validated case "Mandolossa," developed area prone inundating near Brescia, using hourly September 2020 August 2021. Experimental results cross-validation demonstrate forecasting accuracy 98.975%.

Язык: Английский

Процитировано

1

Investigation of Soil Liquefaction and Flooding as a Hydrological Risk for Disaster Risk Reduction in the Construction of Water Reservoirs DOI
Pınar Sarı Çavdar

Опубликована: Янв. 1, 2024

Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI

Язык: Английский

Процитировано

0