Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions

HR Maier, A Jain, GC Dandy, KP Sudheer - Environmental modelling & …, 2010 - Elsevier
Over the past 15 years, artificial neural networks (ANNs) have been used increasingly for
prediction and forecasting in water resources and environmental engineering. However …

Watershed modeling and its applications: A state-of-the-art review

EB Daniel, JV Camp, EJ LeBoeuf… - The Open Hydrology …, 2011 - benthamopen.com
Advances in the understanding of physical, chemical, and biological processes influencing
water quality, coupled with improvements in the collection and analysis of hydrologic data …

Evaluating the application of the statistical index method in flood susceptibility map** and its comparison with frequency ratio and logistic regression methods

M Shafapour Tehrany, L Kumar… - … , Natural Hazards and …, 2019 - Taylor & Francis
Statistical methods are the most popular techniques to model and map flood-prone areas.
Although a wide range of statistical methods have been used, application of the statistical …

River suspended sediment modelling using the CART model: A comparative study of machine learning techniques

B Choubin, H Darabi, O Rahmati… - Science of the Total …, 2018 - Elsevier
Suspended sediment load (SSL) modelling is an important issue in integrated
environmental and water resources management, as sediment affects water quality and …

HydroTest: a web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts

CW Dawson, RJ Abrahart, LM See - Environmental Modelling & Software, 2007 - Elsevier
This paper presents details of an open access web site that can be used by hydrologists and
other scientists to evaluate time series models. There is at present a general lack of …

Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting

RJ Abrahart, F Anctil, P Coulibaly… - Progress in …, 2012 - journals.sagepub.com
This paper traces two decades of neural network rainfall-runoff and streamflow modelling,
collectively termed 'river forecasting'. The field is now firmly established and the research …

Prediction the groundwater level of bastam plain (Iran) by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS)

S Emamgholizadeh, K Moslemi, G Karami - Water resources management, 2014 - Springer
Prediction of the groundwater level (GWL) fluctuations is very important in the water
resource management. This study investigates the potential of two intelligence models …

Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques

AK Lohani, R Kumar, RD Singh - Journal of Hydrology, 2012 - Elsevier
Time series modeling is necessary for the planning and management of reservoirs. More
recently, the soft computing techniques have been used in hydrological modeling and …

Prediction and simulation of monthly groundwater levels by genetic programming

E Fallah-Mehdipour, OB Haddad, MA Mariño - Journal of Hydro …, 2013 - Elsevier
Groundwater level is an effective parameter in the determination of accuracy in groundwater
modeling. Thus, application of simple tools to predict future groundwater levels and fill-in …

Development and evaluation of the cascade correlation neural network and the random forest models for river stage and river flow prediction in Australia

MA Ghorbani, RC Deo, S Kim, M Hasanpour Kashani… - Soft Computing, 2020 - Springer
Accurately predicting river flows over daily timescales is considered as an important task for
sustainable management of freshwater ecosystems, agricultural applications, and water …