Artificial Neural Networks Based Optimization Techniques: A Review

Electronics, Год журнала: 2021, Номер 10(21), С. 2689 - 2689

Опубликована: Ноя. 3, 2021

In the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. this paper, we present an extensive review of neural networks (ANNs) based algorithm techniques with some famous techniques, e.g., genetic (GA), particle swarm (PSO), bee colony (ABC), and backtracking search (BSA) modern developed lightning (LSA) whale (WOA), many more. The entire set such is classified as algorithms on a population where initial randomly created. Input parameters are initialized within specified range, they can provide optimal solutions. This paper emphasizes enhancing network via by manipulating its tuned or training obtain best structure pattern dissolve problems in way. includes results for improving ANN performance PSO, GA, ABC, BSA respectively, parameters, number neurons hidden layers learning rate. obtained net used solving energy management virtual power plant system.

Язык: Английский

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A comprehensive review of deep learning applications in hydrology and water resources

Water Science & Technology, Год журнала: 2020, Номер 82(12), С. 2635 - 2670

Опубликована: Авг. 5, 2020

Abstract The global volume of digital data is expected to reach 175 zettabytes by 2025. volume, variety and velocity water-related are increasing due large-scale sensor networks increased attention topics such as disaster response, water resources management, climate change. Combined with the growing availability computational popularity deep learning, these transformed into actionable practical knowledge, revolutionizing industry. In this article, a systematic review literature conducted identify existing research that incorporates learning methods in sector, regard monitoring, governance communication resources. study provides comprehensive state-of-the-art approaches used industry for generation, prediction, enhancement, classification tasks, serves guide how utilize available future challenges. Key issues challenges application techniques domain discussed, including ethics technologies decision-making management governance. Finally, we provide recommendations directions models hydrology

Язык: Английский

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COVID-19 Outbreak Prediction with Machine Learning

Algorithms, Год журнала: 2020, Номер 13(10), С. 249 - 249

Опубликована: Окт. 1, 2020

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed decisions and enforce relevant control measures. Among standard global pandemic prediction, simple epidemiological statistical have received more attention authorities, these popular in media. Due a high level of uncertainty lack essential data, shown low accuracy long-term prediction. Although literature includes several attempts address this issue, generalization robustness abilities existing need be improved. This paper presents comparative analysis machine learning soft computing predict as an alternative susceptible–infected–recovered (SIR) susceptible-exposed-infectious-removed (SEIR) models. wide range investigated, two showed promising results (i.e., multi-layered perceptron, MLP; adaptive network-based fuzzy inference system, ANFIS). Based on reported here, due highly complex nature variation its behavior across nations, study suggests effective tool model outbreak. provides initial benchmarking demonstrate potential future research. further that genuine novelty can realized integrating SEIR

Язык: Английский

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COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach

Mathematics, Год журнала: 2020, Номер 8(6), С. 890 - 890

Опубликована: Июнь 2, 2020

Several epidemiological models are being used around the world to project number of infected individuals and mortality rates COVID-19 outbreak. Advancing accurate prediction is utmost importance take proper actions. Due lack essential data uncertainty, have been challenged regarding delivery higher accuracy for long-term prediction. As an alternative susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach predict COVID-19, we exemplify its potential using from Hungary. The methods adaptive network-based fuzzy inference system (ANFIS) multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) proposed time series rate. that by late May, outbreak total morality will drop substantially. validation performed 9 days with promising results, which confirms model accuracy. It expected maintains as long no significant interruption occurs. This paper provides initial benchmarking demonstrate future research.

Язык: Английский

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Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods

Lecture notes in networks and systems, Год журнала: 2020, Номер unknown, С. 215 - 227

Опубликована: Янв. 1, 2020

Язык: Английский

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Estimation of daily maize transpiration using support vector machines, extreme gradient boosting, artificial and deep neural networks models

Agricultural Water Management, Год журнала: 2020, Номер 245, С. 106547 - 106547

Опубликована: Окт. 8, 2020

Язык: Английский

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A novel intelligent deep learning predictive model for meteorological drought forecasting

Journal of Ambient Intelligence and Humanized Computing, Год журнала: 2022, Номер 14(8), С. 10441 - 10455

Опубликована: Янв. 24, 2022

Язык: Английский

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Comparison of Deep Learning Techniques for River Streamflow Forecasting

IEEE Access, Год журнала: 2021, Номер 9, С. 71805 - 71820

Опубликована: Янв. 1, 2021

Recently, deep learning (DL) models, especially those based on long short-term memory (LSTM), have demonstrated their superior ability in resolving sequential data problems. This study investigated the performance of six models that belong to supervised category evaluate DL terms streamflow forecasting. They include a feed-forward neural network (FFNN), convolutional (CNN), and four LSTM-based models. Two standard with just one hidden layer-LSTM gated recurrent unit (GRU)-are used against two more complex models-the stacked LSTM (StackedLSTM) model Bidirectional (BiLSTM) model. The Red River basin-the largest river basin north Vietnam-was adopted as case because its geographic relevance since Hanoi city-the capital Vietnam-is located downstream River. Besides, input these are observed at seven hydrological stations three main branches system. indicates exhibited considerably better maintained stability than FFNN CNN However, complexity StackedLSTM BiLSTM is not accompanied by improvement results comparison illustrate respective higher models-LSTM GRU. findings this present can reach impressive forecasts even presence upstream dams reservoirs. For streamflow-forecasting problem, GRU simple architecture (one layer) sufficient produce highly reliable while minimizing computation time.

Язык: Английский

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Integrating Machine Learning and AI for Improved Hydrological Modeling and Water Resource Management

Advances in environmental engineering and green technologies book series, Год журнала: 2023, Номер unknown, С. 46 - 70

Опубликована: Июнь 9, 2023

The hydrological cycle is an important process that controls how and where water distributed on Earth. It includes processes including transpiration, evaporation, condensation, precipitation, runoff, infiltration. However, there are obstacles to understanding modelling the cycle, such as a lack of data, ambiguity, fluctuation, impact human activity natural balance. Techniques for accurate essential managing resources risk reduction. With potential uses in rainfall forecasting, streamflow flood modelling, machine learning artificial intelligence (AI) effective tools modelling. Case studies real-world examples show solutions problems like data quality, interpretability, scalability may be applied situations. Discussions future directions challenges emphasise new developments areas need more investigation cooperation.

Язык: Английский

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A critical review of machine learning algorithms in maritime, offshore, and oil & gas corrosion research: A comprehensive analysis of ANN and RF models

Ocean Engineering, Год журнала: 2024, Номер 295, С. 116796 - 116796

Опубликована: Янв. 30, 2024

Язык: Английский

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