A comparative study of population-based optimization algorithms for downstream river flow forecasting by a hybrid neural network model
Population-based optimization algorithms have been successfully applied to hydrological
forecasting recently owing to their powerful ability of global optimization. This paper …
forecasting recently owing to their powerful ability of global optimization. This paper …
A linked surface water-groundwater modelling approach to more realistically simulate rainfall-runoff non-stationarity in semi-arid regions
Interactions between surface water (SW) and groundwater (GW) have been identified as a
major contributing factor to non-stationarity in rainfall-runoff relationships. However, because …
major contributing factor to non-stationarity in rainfall-runoff relationships. However, because …
[HTML][HTML] Modeling of monthly rainfall and runoff of Urmia lake basin using “feed-forward neural network” and “time series analysis” model
J Farajzadeh, AF Fard, S Lotfi - Water Resources and Industry, 2014 - Elsevier
Urmia lake basin located in northwestern Iran is the second largest saline lake in the world.
Due to many reasons ie climate changes, several dam constructions, building a bridge …
Due to many reasons ie climate changes, several dam constructions, building a bridge …
Covariance matrix adaptation evolution strategy for improving machine learning approaches in streamflow prediction
Precise streamflow estimation plays a key role in optimal water resource use, reservoirs
operations, and designing and planning future hydropower projects. Machine learning …
operations, and designing and planning future hydropower projects. Machine learning …
Intermittent streamflow forecasting by using several data driven techniques
Forecasting intermittent streamflows is an important issue for water quality management,
water supplies, hydropower and irrigation systems. This paper compares the accuracy of …
water supplies, hydropower and irrigation systems. This paper compares the accuracy of …
[HTML][HTML] Runoff forecasting by artificial neural network and conventional model
AR Ghumman, YM Ghazaw, AR Sohail… - Alexandria Engineering …, 2011 - Elsevier
Rainfall runoff models are highly useful for water resources planning and development. In
the present study rainfall–runoff model based on Artificial Neural Networks (ANNs) was …
the present study rainfall–runoff model based on Artificial Neural Networks (ANNs) was …
Comparison of the performance of SWAT, IHACRES and artificial neural networks models in rainfall-runoff simulation (case study: Kan watershed, Iran)
M Ahmadi, A Moeini, H Ahmadi… - … of the Earth, Parts A/B/C, 2019 - Elsevier
The catchment area is essentially a heterogeneous dynamic, time and space hydrological
system, and so the process of rainfall-runoff transmission in the catchment area is a very …
system, and so the process of rainfall-runoff transmission in the catchment area is a very …
River suspended sediment prediction using various multilayer perceptron neural network training algorithms—a case study in Malaysia
MR Mustafa, RB Rezaur, S Saiedi, MH Isa - Water resources management, 2012 - Springer
Estimation of suspended sediment discharge in rivers has a vital role in dealing with water
resources problems and hydraulic structures. In this study, a Multilayer Perceptron (MLP) …
resources problems and hydraulic structures. In this study, a Multilayer Perceptron (MLP) …
Rainfall-runoff modeling for the Hoshangabad Basin of Narmada River using artificial neural network
Accurate modeling of the rainfall-runoff process is still a challenging job despite the
availability of various modeling methods, such as data-driven or knowledge-driven …
availability of various modeling methods, such as data-driven or knowledge-driven …
Modeling rainfall-runoff process using artificial neural network with emphasis on parameter sensitivity
The gradient descent (GD) and Levenberg–Marquardt (LM) algorithms are commonly
adopted methods for training artificial neural network (ANN) models for modeling various …
adopted methods for training artificial neural network (ANN) models for modeling various …