Artificial Intelligence models for prediction of the tide level in Venice

Stochastic Environmental Research and Risk Assessment, Journal Year: 2021, Volume and Issue: 35(12), P. 2537 - 2548

Published: April 9, 2021

Language: Английский

---

The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction

Hydrological Sciences Journal, Journal Year: 2021, Volume and Issue: 67(2), P. 161 - 174

Published: Nov. 30, 2021

This paper focuses on the development of a robust accurate streamflow prediction model by balancing abilities exploitation and exploration to find best parameters machine learning model. To do so, simulated annealing (SA) algorithm is integrated with mayfly optimization (MOA) as SAMOA determine optimal hyper-parameters support vector regression (SVR) overcome weakness MOA method. The proposed method compared classical SVR hybrid SVR-MOA. examine accuracy selected methods, monthly hydroclimatic data from Jhelum River Basin used predict basis RMSE, MAE, NSE, R2 indices. Test results show that SVR-SAMOA outperformed SVR-MOA models. reduced errors models decreasing RMSE MSE 21.4% 14.7% 21.7% 15.1%, respectively, in test stage.

Language: Английский

---

Advanced Machine Learning Model for Prediction of Drought Indices using Hybrid SVR-RSM

Water Resources Management, Journal Year: 2022, Volume and Issue: 37(2), P. 683 - 712

Published: Dec. 9, 2022

Language: Английский

---

Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms

Scientific Reports, Journal Year: 2022, Volume and Issue: 12(1)

Published: March 10, 2022

Abstract Floods and droughts are environmental phenomena that occur in Peninsular Malaysia due to extreme values of streamflow (SF). Due this, the study SF prediction is highly significant for purpose municipal damage mitigation. In present study, machine learning (ML) models based on support vector (SVM), artificial neural network (ANN), long short-term memory (LSTM), tested developed predict 11 different rivers throughout Malaysia. data sets were collected from Malaysian Department Irrigation Drainage. The main objective propose a universal model most capable predicting SFs within Based findings, ANN3 which was using ANN algorithm input scenario 3 (inputs consisting previous days SF) deduced as best overall ML it outperformed all other 4 out sets; obtained among highest average RMs with score 3.27, hence indicating very adaptable reliable accurately river case studies. Therefore, proposed

Language: Английский

---

Harris Hawk Optimization: A Survey onVariants and Applications

Computational Intelligence and Neuroscience, Journal Year: 2022, Volume and Issue: 2022, P. 1 - 20

Published: June 27, 2022

In this review, we intend to present a complete literature survey on the conception and variants of recent successful optimization algorithm, Harris Hawk optimizer (HHO), along with an updated set applications in well-established works. For purpose, first overview HHO, including its logic equations mathematical model. Next, focus reviewing different HHO from available literature. To provide readers deep vision foster application review state-of-the-art improvements focusing mainly fuzzy new intuitionistic algorithm. We also enhancing machine learning operations tackling engineering problems. This can cover aspects future basis for research development swarm intelligence paths use real-world

Language: Английский

---

A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 619, P. 129269 - 129269

Published: Feb. 13, 2023

Language: Английский

---

Intelligent optimization for modelling superhydrophobic ceramic membrane oil flux and oil-water separation efficiency: Evidence from wastewater treatment and experimental laboratory

Chemosphere, Journal Year: 2023, Volume and Issue: 331, P. 138726 - 138726

Published: April 26, 2023

Language: Английский

---

Application of novel binary optimized machine learning models for monthly streamflow prediction

Applied Water Science, Journal Year: 2023, Volume and Issue: 13(5)

Published: April 8, 2023

Abstract Accurate measurements of available water resources play a key role in achieving sustainable environment society. Precise river flow estimation is an essential task for optimal use hydropower generation, flood forecasting, and best utilization engineering. The current paper presents the development verification prediction abilities new hybrid extreme learning machine (ELM)-based models coupling with metaheuristic methods, e.g., Particle swarm optimization (PSO), Mayfly algorithm (MOA), Grey wolf (GWO), simulated annealing (SA) monthly streamflow prediction. Prediction precision standalone ELM model was compared two-phase optimized state-of-the-arts models, ELM–PSO, ELM–MOA, ELM–PSOGWO, ELM–SAMOA, respectively. Hydro-meteorological data acquired from Gorai Padma Hardinge Bridge stations at River Basin, northwestern Bangladesh, were utilized as inputs this study to employ form seven different input combinations. model’s performances are appraised using Nash–Sutcliffe efficiency, root-mean-square-error (RMSE), mean absolute error, percentage error determination coefficient. tested results both reported that ELM–SAMOA ELM–PSOGWO offered accuracy streamflows models. Based on local data, reduced RMSE ELM, by 31%, 27%, 19%, 14% station 29%, bridge station, testing stage, In contrast, based external improves 20%, 5.1%, 6.2%, 4.6% confirmed superiority over single model. overall suggest can be successfully applied modeling either or hydro-meteorological datasets.

Language: Английский

---

Elastic modulus prediction for high-temperature treated rock using multi-step hybrid ensemble model combined with coronavirus herd immunity optimizer

Measurement, Journal Year: 2024, Volume and Issue: 240, P. 115596 - 115596

Published: Aug. 25, 2024

Language: Английский

---

Towards greener futures: SVR-based CO2 prediction model boosted by SCMSSA algorithm

Heliyon, Journal Year: 2024, Volume and Issue: 10(11), P. e31766 - e31766

Published: May 22, 2024

This research presents the utilization of an enhanced Sine cosine perturbation with Chaotic and Mirror imaging strategy-based Salp Swarm Algorithm (SCMSSA), which incorporates three improvement mechanisms, to enhance convergence accuracy speed optimization algorithm. The study assesses SCMSSA algorithm's performance against other algorithms using six test functions show efficacy enhancement strategies. Furthermore, its in improving Support Vector Regression (SVR) models for CO

Language: Английский

---