AIMC Topic: Hydrology

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Comparative analysis of machine learning methods for prediction of chlorophyll-a in a river with different hydrology characteristics: A case study in Fuchun River, China.

Journal of environmental management Jun 11, 2024
Eutrophication is a serious threat to water quality and human health, and chlorophyll-a (Chla) is a key indicator to represent eutrophication in rivers or lakes. Understanding the spatial-temporal distribution of Chla and its accurate prediction are ...
Water Quality Rivers Chlorophyll China Environmental Monitoring Chlorophyll A Hydrology Machine Learning Eutrophication
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Assessment of monthly runoff simulations based on a physics-informed machine learning framework: The effect of intermediate variables in its construction.

Journal of environmental management Jun 3, 2024
Hydrological forecasting is of great importance for water resources management and planning, especially given the increasing occurrence of extreme events such as floods and droughts. The physics-informed machine learning (PIML) models effectively int...
Machine Learning Models, Theoretical Hydrology
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Enhancing physically-based hydrological modeling with an ensemble of machine-learning reservoir operation modules under heavy human regulation using easily accessible data.

Journal of environmental management May 6, 2024
Dams and reservoirs have significantly altered river flow dynamics worldwide. Accurately representing reservoir operations in hydrological models is crucial yet challenging. Detailed reservoir operation data is often inaccessible, leading to relying ...
Humans Machine Learning Rivers Water Movements Hydrology China Models, Theoretical
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Predicting reservoir sedimentation using multilayer perceptron - Artificial neural network model with measured and forecasted hydrometeorological data in Gibe-III reservoir, Omo-Gibe River basin, Ethiopia.

Journal of environmental management May 6, 2024
The estimation and prediction of the amount of sediment accumulated in reservoirs are imperative for sustainable reservoir sedimentation planning and management and to minimize reservoir storage capacity loss. The main objective of this study was to ...
Rivers Environmental Monitoring Geologic Sediments Ethiopia Hydrology Neural Networks, Computer Models, Theoretical
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Enhancing short-term streamflow prediction in the Haihe River Basin through integrated machine learning with Lasso.

Water science and technology : a journal of the International Association on Water Pollution Research May 2, 2024
With the widespread application of machine learning in various fields, enhancing its accuracy in hydrological forecasting has become a focal point of interest for hydrologists. This study, set against the backdrop of the Haihe River Basin, focuses on...
Hydrology Rivers Water Movements Machine Learning China Models, Theoretical Forecasting
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Use of one-dimensional CNN for input data size reduction in LSTM for improved computational efficiency and accuracy in hourly rainfall-runoff modeling.

Journal of environmental management Apr 27, 2024
A deep learning architecture, denoted as CNNsLSTM, is proposed for hourly rainfall-runoff modeling in this study. The architecture involves a serial coupling of the one-dimensional convolutional neural network (1D-CNN) and the long short-term memory ...
Hydrology Neural Networks, Computer Japan Rain Deep Learning Models, Theoretical
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Global prediction of extreme floods in ungauged watersheds.

Nature Mar 20, 2024
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...
Artificial Intelligence Calibration Disaster Planning Hydrology Floods Reproducibility of Results Forecasting Time Factors Computer Simulation Rivers
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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 Feb 14, 2024
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...
Climate Change Rain Hydrology Water Movements Machine Learning Environmental Monitoring Temperature India Rivers Models, Theoretical
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A unified deep learning framework for water quality prediction based on time-frequency feature extraction and data feature enhancement.

Journal of environmental management Dec 27, 2023
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...
Deep Learning China Meteorology Oxygen Water Quality Hydrology
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Anthropogenic fingerprints in daily precipitation revealed by deep learning.

Nature Aug 30, 2023
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...
Tropical Climate Climate Models Neural Networks, Computer Global Warming Rain Temperature Pacific Ocean Hydrology Human Activities Deep Learning Weather
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