An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research …
Despite the massive diversity in the modeling requirements for practical hydrological
applications, there remains a need to develop more reliable and intelligent expert systems …
applications, there remains a need to develop more reliable and intelligent expert systems …
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 …
Prediction of flow based on a CNN-LSTM combined deep learning approach
Although machine learning (ML) techniques are increasingly used in rainfall-runoff models,
most of them are based on one-dimensional datasets. In this study, a rainfall-runoff model …
most of them are based on one-dimensional datasets. In this study, a rainfall-runoff model …
Comparison of long short term memory networks and the hydrological model in runoff simulation
H Fan, M Jiang, L Xu, H Zhu, J Cheng, J Jiang - Water, 2020 - mdpi.com
Runoff modeling is one of the key challenges in the field of hydrology. Various approaches
exist, ranging from physically based over conceptual to fully data driven models. In this …
exist, ranging from physically based over conceptual to fully data driven models. In this …
Water level prediction model based on GRU and CNN
M Pan, H Zhou, J Cao, Y Liu, J Hao, S Li… - Ieee …, 2020 - ieeexplore.ieee.org
Massive amount of water level data has been collected by using Internet of Things (IoT)
techniques in the Yangtze River and other rivers. In this paper, utilizing these data to …
techniques in the Yangtze River and other rivers. In this paper, utilizing these data to …
Interpretable vs. noninterpretable machine learning models for data-driven hydro-climatological process modeling
Due to their enhanced predictive capabilities, noninterpretable machine learning (ML)
models (eg deep learning) have recently gained a growing interest in analyzing and …
models (eg deep learning) have recently gained a growing interest in analyzing and …
Concepts, procedures, and applications of artificial neural network models in streamflow forecasting
Artificial neural network (ANN) model involves computations and mathematics, which
simulate the human–brain processes. Many of the recently achieved advancements are …
simulate the human–brain processes. Many of the recently achieved advancements are …
Predicting monthly streamflow using data‐driven models coupled with data‐preprocessing techniques
CL Wu, KW Chau, YS Li - Water Resources Research, 2009 - Wiley Online Library
In this paper, the accuracy performance of monthly streamflow forecasts is discussed when
using data‐driven modeling techniques on the streamflow series. A crisp distributed support …
using data‐driven modeling techniques on the streamflow series. A crisp distributed support …
Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting
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 …
Prediction of water quality classification of the Kelantan River Basin, Malaysia, using machine learning techniques
Machine Learning (ML) has been used for a long time and has gained wide attention over
the last several years. It can handle a large amount of data and allow non-linear structures …
the last several years. It can handle a large amount of data and allow non-linear structures …