Flood hazard map** (FHM) has undergone significant development in terms of approach
and capacity of the result to meet the target of policymakers for accurate prediction and …

Floods are among the most destructive natural disasters, which are highly complex to model.
The research on the advancement of flood prediction models contributed to risk reduction …

Floods are one of the most devastating natural hazards, causing several deaths and
conspicuous damages all over the world. In this work, we explore the applicability of the …

Extreme weather conditions, as one of many effects of climate change, is expected to
increase the magnitude and frequency of environmental disasters. In parallel, urban centres …

Although machine learning (ML) techniques are increasingly used in rainfall-runoff models,
most of them are based on one-dimensional datasets. In this study, a rainfall-runoff model …

Monthly stream-flow forecasting can yield important information for hydrological applications
including sustainable design of rural and urban water management systems, optimization of …

Flood quantile estimation at sites with little or no data is important for the adequate planning
and management of water resources. Regional Hydrological Frequency Analysis (RFA) …

Accurately analyzing flood frequency is crucial for develo** effective flood management
strategies and designing flood protection infrastructure, but the complex and nonlinear …

The goal of quantile regression is to estimate conditional quantiles for specified values of
quantile probability using linear or nonlinear regression equations. These estimates are …

The occurrences of flash floods in sub-tropical climatic regions like India are ubiquitous
phenomena, particularly during the monsoon season. This type of flood occurs within a short …