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Rivers

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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...

Granular computing-neural network model for prediction of longitudinal dispersion coefficients in rivers.

Water science and technology : a journal of the International Association on Water Pollution Research
Successful application of one-dimensional advection-dispersion models in rivers depends on the accuracy of the longitudinal dispersion coefficient (LDC). In this regards, this study aims to introduce an appropriate approach to estimate LDC in natural ...

Modelling the effects of meteorological parameters on water temperature using artificial neural networks.

Water science and technology : a journal of the International Association on Water Pollution Research
Water temperature affects all biological and chemical processes in water; therefore, it is an extremely important water quality parameter. Meteorological factors are among the most important factors that affect water temperatures. The aim of this stu...

Prediction of biochemical oxygen demand at the upstream catchment of a reservoir using adaptive neuro fuzzy inference system.

Water science and technology : a journal of the International Association on Water Pollution Research
The aim of this study is to examine the potential of adaptive neuro fuzzy inference system (ANFIS) to estimate biochemical oxygen demand (BOD). To illustrate the applicability of ANFIS method, the upstream catchment of Feitsui Reservoir in Taiwan is ...

River flood prediction using fuzzy neural networks: an investigation on automated network architecture.

Water science and technology : a journal of the International Association on Water Pollution Research
Urban floods are one of the most devastating natural disasters globally and improved flood prediction is essential for better flood management. Today, high-resolution real-time datasets for flood-related variables are widely available. These data can...

Water quality of Danube Delta systems: ecological status and prediction using machine-learning algorithms.

Water science and technology : a journal of the International Association on Water Pollution Research
Environmental issues have a worldwide impact on water bodies, including the Danube Delta, the largest European wetland. The Water Framework Directive (2000/60/EC) implementation operates toward solving environmental issues from European and national ...