Artificial intelligence (AI) holds significant promise for advancing natural disaster
management through the use of predictive models that analyze extensive datasets, identify …

Nature-based solutions (NBS) for hydro-meteorological risks (HMRs) reduction and
management are becoming increasingly popular, but challenges such as the lack of well …

The impact of blockchain technology (BCT) implementation on the accuracy, reliability,
visibility, incorruptibility, and timeliness of supply-chain processes and transactions, makes it …

Disasters can have devastating impacts on communities and economies, underscoring the
urgent need for effective strategic disaster risk management (DRM). Although Artificial …

Environmental models are used extensively to evaluate the effectiveness of a range of
design, planning, operational, management and policy options. However, the number of …

Flood risk assessment and map** is required for management and mitigation of flood in
mountain cities. However, the specific characteristics of population, society, economy …

The introduction of deep learning (DL)(LeCun et al., 2015) into hydrology around 2016–
2018 (Tao et al., 2016; Laloy et al., 2017, 2018; Shen, 2018; Shen et al., 2018), especially …

Despite increasing concern about wildland fire risk in Canada, there is little synthesis of
knowledge that could contribute to the development of a comprehensive risk framework for a …

Environmental decision support systems (EDSSs), or DSS applied in the environmental
field, have been developed for over 40 years now. However, most of these tools fail to find …

Hydrometeorological events are highly costly and have strong impacts on the human‐
environment system. Effective response requires effective risk management concepts and …