Deep Learning and Machine Learning in Hydrological Processes Climate Change and Earth Systems a Systematic Review

Ardabili, Sina Faizollahzadeh; Mosavi, Amir; Dehghani, Majid; Várkonyi-Kóczy, Annamária R.

doi:10.1007/978-3-030-36841-8_5

Cited by 83 publications

(36 citation statements)

References 68 publications

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“…Energy demand and consumption, wind energy industry, and solar power modeling are the other application domains of LSTM. Further investigation is essential to explore the new deep learning methods and explore the application domains, as it has been done for machine Learning methods[102][103][104][105][106][107][108][109]. International Journal of Automation and Computing, 2019.…”

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confidence: 99%

List of Deep Learning Models

Mosavi

Ardabili²,

Várkonyi-Kóczy

2019

Preprint

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Deep learning (DL) algorithms have recently emerged from machine learning and soft computing techniques. Since then, several deep learning (DL) algorithms have been recently introduced to scientific communities and are applied in various application domains. Today the usage of DL has become essential due to their intelligence, efficient learning, accuracy and robustness in model building. However, in the scientific literature, a comprehensive list of DL algorithms has not been introduced yet. This paper provides a list of the most popular DL algorithms, along with their applications domains.

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confidence: 99%

List of Deep Learning Models

Mosavi

Ardabili²,

Várkonyi-Kóczy

2019

Preprint

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“…This paper further identified future trends in the advancement of learning algorithms for smart cities. The trend in smart cities have shown to follow the trend in the overall trend which is a shift toward the advancement of the more sophisticated hybrid, ensemble and deep learning models, as also shown in [80][81][82][83][84][85][86][87][88].…”

Section: Discussionmentioning

confidence: 99%

State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

Nosratabadi

Mosavi

Keivani

et al. 2020

Lecture Notes in Networks and Systems

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Deep learning (DL) and machine learning (ML) methods have recently contributed to the advancement of models in the various aspects of prediction, planning, and uncertainty analysis of smart cities and urban development. This paper presents the state of the art of DL and ML methods used in this realm. Through a novel taxonomy, the advances in model development and new application domains in urban sustainability and smart cities are presented. Findings reveal that five DL and ML methods have been most applied to address the different aspects of smart cities. These are artificial neural networks; support vector machines; decision trees; ensembles, Bayesians, hybrids, and neurofuzzy; and deep learning. It is also disclosed that energy, health, and urban transport are the main domains of smart cities that DL and ML methods contributed in to address their problems.

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“…The hybridization of machine learning methods has shown to be an essential approach to improve the performance of the prediction models. For the future research, advancement of hybrid and ensemble machine learning models, e.g., [23][24][25][26][27][28], and comparative analysis with deep learning models, e.g., [29][30][31][32] are proposed to identify models with higher efficiency.…”

Section: Discussionmentioning

confidence: 99%

Performance Analysis of Combine Harvester Using Hybrid Model of Artificial Neural Networks Particle Swarm Optimization

Nádai¹,

Felde²,

Ardabili³

et al. 2020

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Novel applications of artificial intelligence for tuning the parameters of industrial machines for optimal performance are emerging at a fast pace. Tuning the combine harvesters and improving the machine performance can dramatically minimize the wastes during harvesting, and it is also beneficial to machine maintenance. Literature includes several soft computing, machine learning and optimization methods that had been used to model the function of harvesters of various crops. Due to the complexity of the problem, machine learning methods had been recently proposed to predict the optimal performance with promising results. In this paper, through proposing a novel hybrid machine learning model based on artificial neural networks integrated with particle swarm optimization (ANN-PSO), the performance analysis of a common combine harvester is presented. The hybridization of machine learning methods with soft computing techniques has recently shown promising results to improve the performance of the combine harvesters. This research aims at improving the results further by providing more stable models with higher accuracy. Keywords-Combine harvester, hybrid machine learning, artificial neural networks (ANN), particle swarm optimization (PSO), ANN-PSO

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Deep Learning and Machine Learning in Hydrological Processes Climate Change and Earth Systems a Systematic Review

Cited by 83 publications

References 68 publications

List of Deep Learning Models

List of Deep Learning Models

State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

Performance Analysis of Combine Harvester Using Hybrid Model of Artificial Neural Networks Particle Swarm Optimization

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