Artificial intelligence-based single and hybrid models for prediction of water quality in rivers: A review
T Rajaee, S Khani, M Ravansalar - Chemometrics and Intelligent …, 2020 - Elsevier
The need for accurate predictions of water quality in rivers has encouraged researchers to
develop new methods and to improve the predictive ability of conventional models. In recent …
develop new methods and to improve the predictive ability of conventional models. In recent …
Applications of hybrid wavelet–artificial intelligence models in hydrology: a review
Accurate and reliable water resources planning and management to ensure sustainable use
of watershed resources cannot be achieved without precise and reliable models …
of watershed resources cannot be achieved without precise and reliable models …
[HTML][HTML] An integrated statistical-machine learning approach for runoff prediction
Nowadays, great attention has been attributed to the study of runoff and its fluctuation over
space and time. There is a crucial need for a good soil and water management system to …
space and time. There is a crucial need for a good soil and water management system to …
Evaluation of stacking and blending ensemble learning methods for estimating daily reference evapotranspiration
T Wu, W Zhang, X Jiao, W Guo, YA Hamoud - Computers and Electronics in …, 2021 - Elsevier
Precise reference evapotranspiration (ETo) estimation and prediction are the first steps to
realize efficient agricultural water resources management. As machine learning methods are …
realize efficient agricultural water resources management. As machine learning methods are …
Implementation of hybrid particle swarm optimization-differential evolution algorithms coupled with multi-layer perceptron for suspended sediment load estimation
River suspended sediment load (SSL) estimation is of importance in water resources
engineering and hydrological modeling. In this study, a novel hybrid approach is …
engineering and hydrological modeling. In this study, a novel hybrid approach is …
A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: Case studies of the USA and Turkey regions
In this study, the potential of six different machine learning models, gradient boosting tree
(GBT), multilayer perceptron neural network (MLPNN), two types of adaptive neuro-fuzzy …
(GBT), multilayer perceptron neural network (MLPNN), two types of adaptive neuro-fuzzy …
Two hybrid artificial intelligence approaches for modeling rainfall–runoff process
The need for accurate modeling of the rainfall–runoff process has grown rapidly in the past
decades. However, considering the high stochastic property of the process, many models …
decades. However, considering the high stochastic property of the process, many models …
Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates
Evaporation rate is one of the key parameters in determining the ecological conditions and it
has an irrefutable role in the proper management of water resources. In this paper, the …
has an irrefutable role in the proper management of water resources. In this paper, the …
Neurocomputing in surface water hydrology and hydraulics: A review of two decades retrospective, current status and future prospects
Neurocomputing methods have contributed significantly to the advancement of modelling
techniques in surface water hydrology and hydraulics in the last couple of decades, primarily …
techniques in surface water hydrology and hydraulics in the last couple of decades, primarily …
A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States
Accurate and reliable suspended sediment load (SSL) prediction models are necessary for
planning and management of water resource structures. More recently, soft computing …
planning and management of water resource structures. More recently, soft computing …