Artificial intelligence based models for stream-flow forecasting: 2000–2015
Summary The use of Artificial Intelligence (AI) has increased since the middle of the 20th
century as seen in its application in a wide range of engineering and science problems. The …
century as seen in its application in a wide range of engineering and science problems. The …
Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan
ABSTRACT “Panta Rhei–Everything Flows” is the science plan for the International
Association of Hydrological Sciences scientific decade 2013–2023. It is founded on the …
Association of Hydrological Sciences scientific decade 2013–2023. It is founded on the …
Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree
O Kisi - Journal of Hydrology, 2015 - Elsevier
Pan evaporation (Ep) modeling is an important issue in reservoir management, regional
water resources planning and evaluation of drinking-water supplies. The main purpose of …
water resources planning and evaluation of drinking-water supplies. The main purpose of …
Multi-station artificial intelligence based ensemble modeling of reference evapotranspiration using pan evaporation measurements
In this study, different Artificial Intelligence (AI) techniques including Feed Forward Neural
Network (FFNN), Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector …
Network (FFNN), Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector …
A comparison of methods to avoid overfitting in neural networks training in the case of catchment runoff modelling
AP Piotrowski, JJ Napiorkowski - Journal of Hydrology, 2013 - Elsevier
Artificial neural networks (ANNs) becomes very popular tool in hydrology, especially in
rainfall–runoff modelling. However, a number of issues should be addressed to apply this …
rainfall–runoff modelling. However, a number of issues should be addressed to apply this …
Artificial intelligence based ensemble model for prediction of vehicular traffic noise
Vehicular traffic noise is the main source of noise pollution in major cities around the globe.
A reliable and accurate method for the estimation of vehicular traffic noise is therefore …
A reliable and accurate method for the estimation of vehicular traffic noise is therefore …
Removal of boron by a modified resin in fixed bed column: Breakthrough curve analysis using dynamic adsorption models and artificial neural network model
S Bai, J Li, W Ding, S Chen, R Ya - Chemosphere, 2022 - Elsevier
Continuous removal of toxic element boron from aqueous solution was investigated with
new phenolic hydroxyl modified resin (T-resin) using a fixed bed column reactor operated …
new phenolic hydroxyl modified resin (T-resin) using a fixed bed column reactor operated …
Artificial neural network model for ozone concentration estimation and Monte Carlo analysis
M Gao, L Yin, J Ning - Atmospheric Environment, 2018 - Elsevier
Air pollution in urban atmosphere directly affects public-health; therefore, it is very essential
to predict air pollutant concentrations. Air quality is a complex function of emissions …
to predict air pollutant concentrations. Air quality is a complex function of emissions …
Evaluation of feature selection methods based on artificial neural network weights
Weight-based feature selection (WBFS) are methods used to measure the contribution of
input to output in a trained artificial neural network (ANN). Furthermore, algorithms such as …
input to output in a trained artificial neural network (ANN). Furthermore, algorithms such as …
An emotional artificial neural network for prediction of vehicular traffic noise
Road traffic is a leading source of environmental noise pollution in large cities, which greatly
affects the health and well-being of people. A reliable method for the prediction of road traffic …
affects the health and well-being of people. A reliable method for the prediction of road traffic …