Application of novel binary optimized machine learning models for monthly streamflow prediction
Accurate measurements of available water resources play a key role in achieving a
sustainable environment of a society. Precise river flow estimation is an essential task for …
sustainable environment of a society. Precise river flow estimation is an essential task for …
Modelling groundwater level fluctuations by ELM merged advanced metaheuristic algorithms using hydroclimatic data
The accurate assessment of groundwater levels is critical to water resource management.
With global warming and climate change, its significance has become increasingly evident …
With global warming and climate change, its significance has become increasingly evident …
A soft-sensor for sustainable operation of coagulation and flocculation units
M Arab, H Akbarian, M Gheibi, M Akrami… - … Applications of Artificial …, 2022 - Elsevier
Abstract Nowadays, Machine Learning (ML) techniques have become one of the most
widely used engineering tools due to their numerous advantages, including their continuous …
widely used engineering tools due to their numerous advantages, including their continuous …
A novel interval decomposition correlation particle swarm optimization-extreme learning machine model for short-term and long-term water quality prediction
S Huan - Journal of Hydrology, 2023 - Elsevier
Water quality prediction plays a crucial role in pollution treatment. However, inaccurate long-
term prediction resulting from complex information patterns and insufficient feature extraction …
term prediction resulting from complex information patterns and insufficient feature extraction …
Ecological states of watercourses regarding water quality parameters and hydromorphological parameters: deriving empirical equations by machine learning models
Environmental biomonitoring techniques have been widely applied to assess the quality
states of toxic chemical compounds in surface freshwater quality. The methods based on …
states of toxic chemical compounds in surface freshwater quality. The methods based on …
Optimization of water quality monitoring programs by data mining
D da Silveira Barcellos, FT de Souza - Water Research, 2022 - Elsevier
Water quality monitoring programs are essential planning and management tools, but they
face many challenges in the developing world. The scarcity of financial and human …
face many challenges in the developing world. The scarcity of financial and human …
Remote sensing-enabled machine learning for river water quality modeling under multidimensional uncertainty
Two fundamental problems have inhibited progress in the simulation of river water quality
under climate (and other) uncertainty: 1) insufficient data, and 2) the inability of existing …
under climate (and other) uncertainty: 1) insufficient data, and 2) the inability of existing …
Comparison of the performance of decision tree (DT) algorithms and extreme learning machine (ELM) model in the prediction of water quality of the Upper Green …
J Anmala, V Turuganti - Water Environment Research, 2021 - Wiley Online Library
Stream waters play a crucial role in catering to the world's needs with the required quality of
water. Due to the discharges of wastewater from the various point and nonpoint sources …
water. Due to the discharges of wastewater from the various point and nonpoint sources …
Prediction of water turbidity in a marine environment using machine learning: A case study of Hong Kong
The water quality measurement of marine water is a key research topic for environmental
and ocean modelers in the past several decades. Marine water quality is mainly described …
and ocean modelers in the past several decades. Marine water quality is mainly described …
Application of artificial intelligence based on synchrosqueezed wavelet transform and improved deep extreme learning machine in water quality prediction
C Song, L Yao - Environmental Science and Pollution Research, 2022 - Springer
Water quality prediction is the basis for the prevention and control of water pollution. In this
paper, to address the problem of low prediction accuracy of existing empirical models due to …
paper, to address the problem of low prediction accuracy of existing empirical models due to …