AIMC Topic: Rivers

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A hybrid approach to improvement of watershed water quality modeling by coupling process-based and deep learning models.

Water environment research : a research publication of the Water Environment Federation
Watershed water quality modeling to predict changing water quality is an essential tool for devising effective management strategies within watersheds. Process-based models (PBMs) are typically used to simulate water quality modeling. In watershed mo...

Machine learning approaches for adequate prediction of flow resistance in alluvial channels with bedforms.

Water science and technology : a journal of the International Association on Water Pollution Research
In natural rivers, flow conditions are mainly dependent on flow resistance and type of roughness. The interactions among flow and bedforms are complex in nature as bedform dynamics primarily regulate the flow resistance. Manning's equation is the mos...

Assessing the impacts of climate change on streamflow dynamics: A machine learning perspective.

Water science and technology : a journal of the International Association on Water Pollution Research
This study investigates changes in river flow patterns, in the Hunza Basin, Pakistan, attributed to climate change. Given the anticipated rise in extreme weather events, accurate streamflow predictions are increasingly vital. We assess three machine ...

A novel approach to precipitation prediction using a coupled CEEMDAN-GRU-Transformer model with permutation entropy algorithm.

Water science and technology : a journal of the International Association on Water Pollution Research
The accurate forecasting of precipitation in the upper reaches of the Yellow River is imperative for enhancing water resources in both the local and broader Yellow River basin in the present and future. While many models exist for predicting precipit...

Streams, rivers and data lakes: an introduction to understanding modern electronic healthcare records.

Clinical medicine (London, England)
As foundation doctors, we have often found ourselves informing patients that a certain aspect of their medical information cannot be immediately found, either because it is on an electronic system we cannot access, or it is in a hospital that is unli...

Application of wavelet theory to enhance the performance of machine learning techniques in estimating water quality parameters (case study: Gao-Ping River).

Water science and technology : a journal of the International Association on Water Pollution Research
There are several methods for modeling water quality parameters, with data-based methods being the focus of research in recent decades. The current study aims to simulate water quality parameters using modern artificial intelligence techniques, to en...

Comparing machine-learning-based black box techniques and white box models to predict rainfall-runoff in a northern area of Iraq, the Little Khabur River.

Water science and technology : a journal of the International Association on Water Pollution Research
The rainfall-runoff process is one of the most complex hydrological phenomena. Estimating runoff in the basin is one of the main conditions for planning and optimal use of rainfall. Using machine learning models in various sciences to investigate phe...

Hybrid wavelet-gene expression programming and wavelet-support vector machine models for rainfall-runoff modeling.

Water science and technology : a journal of the International Association on Water Pollution Research
It is critical to use research methods to collect and regulate surface water to provide water while avoiding damage. Following accurate runoff prediction, principled planning for optimal runoff is implemented. In recent years, there has been an incre...

Estimating the incubated river water quality indicator based on machine learning and deep learning paradigms: BOD5 Prediction.

Mathematical biosciences and engineering : MBE
As an indicator measured by incubating organic material from water samples in rivers, the most typical characteristic of water quality items is biochemical oxygen demand (BOD) concentration, which is a stream pollutant with an extreme circumstance of...

Dissolved oxygen modelling of the Yamuna River using different ANFIS models.

Water science and technology : a journal of the International Association on Water Pollution Research
Dissolved oxygen (DO) is one of the prime parameters for assessing the water quality of any stream. Thus, the accurate estimation of DO is necessary to evolve measures for maintaining the riverine ecosystem and designing appropriate water quality imp...