Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions
Over the past 15 years, artificial neural networks (ANNs) have been used increasingly for
prediction and forecasting in water resources and environmental engineering. However …
prediction and forecasting in water resources and environmental engineering. However …
Long short-term memory (LSTM) recurrent neural network for low-flow hydrological time series forecasting
This article explores the suitability of a long short-term memory recurrent neural network
(LSTM-RNN) and artificial intelligence (AI) method for low-flow time series forecasting. The …
(LSTM-RNN) and artificial intelligence (AI) method for low-flow time series forecasting. The …
A practical review and taxonomy of fuzzy expert systems: methods and applications
M Tavana, V Hajipour - Benchmarking: An International Journal, 2020 - emerald.com
Purpose Expert systems are computer-based systems that mimic the logical processes of
human experts or organizations to give advice in a specific domain of knowledge. Fuzzy …
human experts or organizations to give advice in a specific domain of knowledge. Fuzzy …
Development of new machine learning model for streamflow prediction: Case studies in Pakistan
For accurate estimation of streamflow of a mountainous river basin, a novel hybrid method is
developed in this study, where gradient-based optimization (GBO) algorithm is employed to …
developed in this study, where gradient-based optimization (GBO) algorithm is employed to …
A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series
Developing a hydrological forecasting model based on past records is crucial to effective
hydropower reservoir management and scheduling. Traditionally, time series analysis and …
hydropower reservoir management and scheduling. Traditionally, time series analysis and …
Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS
DT Bui, B Pradhan, O Lofman, I Revhaug… - Computers & …, 2012 - Elsevier
The objective of this study is to investigate a potential application of the Adaptive Neuro-
Fuzzy Inference System (ANFIS) and the Geographic Information System (GIS) as a …
Fuzzy Inference System (ANFIS) and the Geographic Information System (GIS) as a …
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 …
collectively termed 'river forecasting'. The field is now firmly established and the research …
Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran)
The main aim of this paper is to develop a new hybrid method to assess landslide
susceptibility mapping (LSM) in neighboring provinces of Alborz Mountains in Iran. In the …
susceptibility mapping (LSM) in neighboring provinces of Alborz Mountains in Iran. In the …
Explore an evolutionary recurrent ANFIS for modelling multi-step-ahead flood forecasts
Reliable and precise multi-step-ahead flood forecasts are crucial and beneficial to decision
makers for mitigating flooding risks. For a river basin undergoing fast urban development, its …
makers for mitigating flooding risks. For a river basin undergoing fast urban development, its …
Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques
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 …
recently, the soft computing techniques have been used in hydrological modeling and …