AIMC Topic: Hydrology

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Global prediction of extreme floods in ungauged watersheds.

Nature
Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks. Accurate and timely warnings are critical for mitigating flood risks, but hydrological simula...

Integrating conceptual and machine learning models to enhance daily-Scale streamflow simulation and assessing climate change impact in the watersheds of the Godavari basin, India.

Environmental research
This study examined and addressed climate change's effects on hydrological patterns, particularly in critical places like the Godavari River basin. This study used daily gridded rainfall and temperature datasets from the Indian Meteorological Departm...

A unified deep learning framework for water quality prediction based on time-frequency feature extraction and data feature enhancement.

Journal of environmental management
Deep learning methods exhibited significant advantages in mapping highly nonlinear relationships with acceptable computational speed, and have been widely used to predict water quality. However, various model selection and construction methods result...

Anthropogenic fingerprints in daily precipitation revealed by deep learning.

Nature
According to twenty-first century climate-model projections, greenhouse warming will intensify rainfall variability and extremes across the globe. However, verifying this prediction using observations has remained a substantial challenge owing to lar...

Enhanced rainfall prediction performance via hybrid empirical-singular-wavelet-fuzzy approaches.

Environmental science and pollution research international
Rainfall is a vital process in the hydrological cycle of the globe. Accessing reliable and accurate rainfall data is crucial for water resources operation, flood control, drought warning, irrigation, and drainage. In the present study, the main objec...

Prediction of monthly precipitation using various artificial models and comparison with mathematical models.

Environmental science and pollution research international
Precipitation (PP) prediction is an interesting topic in the meteorology or hydrology field since it is directly related to agriculture, the management of water resources in hydrologic basins, and water scarcity. Selecting the right model to predict ...

Assessing spatial connectivity effects on daily streamflow forecasting using Bayesian-based graph neural network.

The Science of the total environment
Data-driven models have been widely developed and achieved impressive results in streamflow prediction. However, the existing data-driven models mostly focus on the selection of input features and the adjustment of model structure, and less on the im...

Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning.

Water research
We propose and demonstrate a new approach for fast and accurate surrogate modelling of urban drainage system hydraulics based on physics-guided machine learning. The surrogates are trained against a limited set of simulation results from a hydrodynam...

Drought Assessment Based on Data Fusion and Deep Learning.

Computational intelligence and neuroscience
Drought is a major factor affecting the sustainable development of society and the economy. Research on drought assessment is of great significance for formulating drought emergency policies and drought risk early warning and enhancing the ability to...

Rainfall prediction using multiple inclusive models and large climate indices.

Environmental science and pollution research international
Rainfall prediction is vital for the management of available water resources. Accordingly, this study used large lagged climate indices to predict rainfall in Iran's Sefidrood basin. A radial basis function neural network (RBFNN) and a multilayer per...