AIMC Topic: Water Movements

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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...
Forecasting China Machine Learning Water Movements Hydrology Models, Theoretical Rivers
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Estimation of instantaneous peak flows in Canadian rivers: an evaluation of conventional, nonlinear regression, and machine learning methods.

Water science and technology : a journal of the International Association on Water Pollution Research Apr 17, 2024
Instantaneous peak flows (IPFs) are often required to derive design values for sizing various hydraulic structures, such as culverts, bridges, and small dams/levees, in addition to informing several water resources management-related activities. Comp...
Canada Models, Theoretical Machine Learning Rivers Water Movements Regression Analysis Nonlinear Dynamics
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Assessment of rainfall-derived inflow and infiltration in sewer systems with machine learning approaches.

Water science and technology : a journal of the International Association on Water Pollution Research Apr 9, 2024
Rainfall-derived inflow/infiltration (RDII) modelling during heavy rainfall events is essential for sewer flow management. In this study, two machine learning algorithms, random forest (RF) and long short-term memory (LSTM), were developed for sewer ...
Water Movements Machine Learning Models, Theoretical Sewage Rain
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Statistical and machine learning analysis for the application of microbially induced carbonate precipitation as a physical barrier to control seawater intrusion.

Journal of contaminant hydrology Mar 20, 2024
Seawater intrusion in coastal aquifers is a significant problem that can be addressed through the construction of subsurface dams or physical cut-off barriers. An alternative method is the use of microbially induced carbonate precipitation (MICP) to ...
Seawater Water Movements Groundwater Chemical Precipitation Carbonates Machine Learning
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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...
Rain Water Movements Hydrology Rivers Climate Change Environmental Monitoring Machine Learning Temperature India Models, Theoretical
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Improved runoff forecasting based on time-varying model averaging method and deep learning.

PloS one Sep 15, 2022
In order to improve the accuracy and stability of runoff prediction. This study proposed a dynamic model averaging method with Time-varying weight (TV-DMA). Using this method, an integrated prediction model framework for runoff prediction was constru...
Water Movements Floods Rain Forecasting Deep Learning
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A Novel Interannual Rainfall Runoff Equation Derived from Ol'Dekop's Model Using Artificial Neural Networks.

Sensors (Basel, Switzerland) Jun 8, 2022
In water resources management, modeling water balance factors is necessary to control dams, agriculture, irrigation, and also to provide water supply for drinking and industries. Generally, conceptual and physical models present challenges to find mo...
Neural Networks, Computer Reproducibility of Results Water Supply Water Movements Water Agriculture
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A Hybrid Water Balance Machine Learning Model to Estimate Inter-Annual Rainfall-Runoff.

Sensors (Basel, Switzerland) Apr 23, 2022
Watershed climatic diversity poses a hard problem when it comes to finding suitable models to estimate inter-annual rainfall runoff (IARR). In this work, a hybrid model (dubbed MR-CART) is proposed, based on a combination of MR (multiple regression) ...
Multivariate Analysis Water Movements Reproducibility of Results Water Machine Learning
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Development of cluster analysis methodology for identification of model rainfall hyetographs and its application at an urban precipitation field scale.

The Science of the total environment Mar 16, 2022
Despite growing access to precipitation time series records at a high temporal scale, in hydrology, and particularly urban hydrology, engineers still design and model drainage systems using scenarios of rainfall temporal distributions predefined by m...
Artificial Intelligence Rain Hydrology Water Movements Cities Cluster Analysis
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Improving streamflow simulation by combining hydrological process-driven and artificial intelligence-based models.

Environmental science and pollution research international Jul 28, 2021
Accurate and timely monitoring of streamflow and its variation is crucial for water resources management in watersheds. This study aimed at evaluating the performance of two process-driven conceptual rainfall-runoff models (HBV: Hydrologiska Byråns V...
Water Movements Environmental Monitoring Artificial Intelligence Hydrology Rivers
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