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 …
Genetic programming in water resources engineering: A state-of-the-art review
The state-of-the-art genetic programming (GP) method is an evolutionary algorithm for
automatic generation of computer programs. In recent decades, GP has been frequently …
automatic generation of computer programs. In recent decades, GP has been frequently …
Toward improved lumped groundwater level predictions at catchment scale: Mutual integration of water balance mechanism and deep learning method
Abstract Model development in groundwater simulation and physics informed deep learning
(DL) has been advancing separately with limited integration. This study develops a general …
(DL) has been advancing separately with limited integration. This study develops a general …
Data-driven modelling: some past experiences and new approaches
DP Solomatine, A Ostfeld - Journal of hydroinformatics, 2008 - iwaponline.com
Physically based (process) models based on mathematical descriptions of water motion are
widely used in river basin management. During the last decade the so-called data-driven …
widely used in river basin management. During the last decade the so-called data-driven …
[HTML][HTML] Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling
HMVV Herath, J Chadalawada… - Hydrology and Earth …, 2021 - hess.copernicus.org
Despite showing great success of applications in many commercial fields, machine learning
and data science models generally show limited success in many scientific fields, including …
and data science models generally show limited success in many scientific fields, including …
Modeling rainfall-runoff process using soft computing techniques
Rainfall-runoff process was modeled for a small catchment in Turkey, using 4 years (1987–
1991) of measurements of independent variables of rainfall and runoff values. The models …
1991) of measurements of independent variables of rainfall and runoff values. The models …
Estimating building energy consumption using extreme learning machine method
The current energy requirements of buildings comprise a large percentage of the total
energy consumed around the world. The demand of energy, as well as the construction …
energy consumed around the world. The demand of energy, as well as the construction …
Hydrologically informed machine learning for rainfall‐runoff modeling: A genetic programming‐based toolkit for automatic model induction
J Chadalawada, H Herath… - Water Resources …, 2020 - Wiley Online Library
Abstract Models of water resources systems are conceived to capture the underlying
environmental dynamics occurring within watersheds. All such models can be regarded as …
environmental dynamics occurring within watersheds. All such models can be regarded as …
Suspended sediment modeling using genetic programming and soft computing techniques
Modeling suspended sediment load is an important factor in water resources engineering as
it crucially affects the design and management of water resources structures. In this study the …
it crucially affects the design and management of water resources structures. In this study the …
Multi-objective management of saltwater intrusion in coastal aquifers using genetic programming and modular neural network based surrogate models
J Sreekanth, B Datta - Journal of hydrology, 2010 - Elsevier
Surrogate model based methodologies are developed for evolving multi-objective
management strategies for saltwater intrusion in coastal aquifers. Two different surrogate …
management strategies for saltwater intrusion in coastal aquifers. Two different surrogate …