A systematic literature review on lake water level prediction models
Global climate change has led to large fluctuations in lake levels in recent years as
meteorological and hydrological parameters have changed and water use has been …
meteorological and hydrological parameters have changed and water use has been …
A review of models for water level forecasting based on machine learning
WJ Wee, NB Zaini, AN Ahmed, A El-Shafie - Earth Science Informatics, 2021 - Springer
It is crucial to keep an eye on the water levels in reservoirs in order for them to perform at
peak, as they are one of the, if not, the most vital part in water resource management. The …
peak, as they are one of the, if not, the most vital part in water resource management. The …
Model-agnostic feature importance and effects with dependent features: a conditional subgroup approach
The interpretation of feature importance in machine learning models is challenging when
features are dependent. Permutation feature importance (PFI) ignores such dependencies …
features are dependent. Permutation feature importance (PFI) ignores such dependencies …
Prediction of tubular solar still performance by machine learning integrated with Bayesian optimization algorithm
Y Wang, AW Kandeal, A Swidan, SW Sharshir… - Applied Thermal …, 2021 - Elsevier
In this study, accurate and convenient prediction models of tubular solar still performance,
expressed as hourly production, were developed by utilizing machine learning. Based on …
expressed as hourly production, were developed by utilizing machine learning. Based on …
Relating the partial dependence plot and permutation feature importance to the data generating process
Scientists and practitioners increasingly rely on machine learning to model data and draw
conclusions. Compared to statistical modeling approaches, machine learning makes fewer …
conclusions. Compared to statistical modeling approaches, machine learning makes fewer …
Evaluation of random forests for short-term daily streamflow forecasting in rainfall-and snowmelt-driven watersheds
In the past decades, data-driven machine-learning (ML) models have emerged as promising
tools for short-term streamflow forecasting. Among other qualities, the popularity of ML …
tools for short-term streamflow forecasting. Among other qualities, the popularity of ML …
Reconstructing daily discharge in a megadelta using machine learning techniques
In this study, six machine learning (ML) models, namely, random forest (RF), Gaussian
process regression (GPR), support vector regression (SVR), decision tree (DT), least …
process regression (GPR), support vector regression (SVR), decision tree (DT), least …
Evaluating the climate sensitivity of coupled electricity-natural gas demand using a multivariate framework
Projected climate change will significantly influence the shape of the end-use energy
demand profiles for space conditioning—leading to a likely increase in cooling needs and a …
demand profiles for space conditioning—leading to a likely increase in cooling needs and a …
Integrating spatial clustering with predictive modeling of pipe failures in water distribution systems
AA Abokifa, L Sela - Urban Water Journal, 2023 - Taylor & Francis
Pipe failures in water distribution infrastructure (WDI) have significant economic,
environmental and public health impacts. To alleviate these impacts, repair and replacement …
environmental and public health impacts. To alleviate these impacts, repair and replacement …
The weighted values of solar evaporation's environment factors obtained by machine learning
Y Wang, G Peng, SW Sharshir… - ES Materials & …, 2021 - espublisher.com
Enhancing the efficiency of solar evaporation is important for solar stills. In this study, the
weighted values of environment factors (descriptors) on the efficiency of solar evaporation …
weighted values of environment factors (descriptors) on the efficiency of solar evaporation …