Modeling Daily Pan Evaporation in Humid Climates Using Gaussian Process Regression

Shabani, Sevda; Samadianfard, Saeed; Sattari, Mohammad Taghi; Shamshirband, Shahab; Mosavi, Amir; Kmeť, Tibor; Várkonyi-Kóczy, Annamária R.

doi:10.20944/preprints201907.0351.v1

Cited by 24 publications

(13 citation statements)

References 19 publications

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“…Literature includes a number of review papers on machine learning and deep learning methods[30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46]. There exists a number of papers where the applications domains of the ML methods have been evaluated[47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62]. How-ever, there is a gap in investigating the algorithmic advancements and application domains considering the hydrological processes, climate change, and earth systems.…”

mentioning

confidence: 99%

Deep Learning and Machine Learning in Hydrological Processes, Climate Change and Earth Systems: A Systematic Review

Ardabili¹,

Mosavi

Dehghani

et al. 2019

Preprint

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Artificial intelligence methods and application have recently shown great contribution in modeling and prediction of the hydrological processes, climate change, and earth systems. Among them, deep learning and machine learning methods mainly have reported being essential for achieving higher accuracy, robustness, efficiency, computation cost, and overall model performance. This paper presents the state of the art of machine learning and deep learning methods and applications in this realm and the current state, and future trends are discussed. The survey of the advances in machine learning and deep learning are presented through a novel classification of methods. The paper concludes that deep learning is still in the first stages of development, and the research is still progressing. On the other hand, machine learning methods are already established in the fields, and novel methods with higher performance are emerging through ensemble techniques and hybridization.

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mentioning

confidence: 99%

Deep Learning and Machine Learning in Hydrological Processes, Climate Change and Earth Systems: A Systematic Review

Ardabili¹,

Mosavi

Dehghani

et al. 2019

Preprint

Self Cite

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“…Literature includes a vast number of machine learning methods used for the purpose of the modeling and prediction [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Machine learning models generally out-perform most of the statistical and mathematical models in term of computation cost, efficiency and accuracy [27][28][29][30][31][32][33][34][35][36][37][38][39][40]. ANNs are considered as an efficient methods for developing reliable models.…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks

Ardabili¹,

Mosavi²,

Mahmoudi³

et al. 2020

Lecture Notes in Networks and Systems

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The recent developments of computer and electronic systems have made the use of intelligent systems for the automation of agricultural industries. In this study, the temperature variation of the mushroom growing room was modeled by multi-layered perceptron and radial basis function networks based on independent parameters including ambient temperature, water temperature, fresh air and circulation air dampers, and water tap. According to the obtained results from the networks, the best network for MLP was in the second repetition with 12 neurons in the hidden layer and in 20 neurons in the hidden layer for radial basis function network. The obtained results from comparative parameters for two networks showed the highest correlation coefficient (0.966), the lowest root mean square error (RMSE) (0.787) and the lowest mean absolute error (MAE) (0.02746) for radial basis function. Therefore, the neural network with radial basis function was selected as a predictor of the behavior of the system for the temperature of mushroom growing halls controlling system.

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“…However, so far, an optimization study with this dependent parameters has not been performed with the RSM method, and this is the main novelty of present work. Modeling with machine learning methods have been studied in a vast number of studies covering a wide range of applications [31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49]. Among the machine learning method the hybrid and ensemble are reported to outperform other conventional machine learning methods .…”

Section: Introductionmentioning

confidence: 99%

Prediction of Combine Harvester Performance Using Hybrid Machine Learning Modeling and Response Surface Methodology<strong> </strong>

Gundoshmian

Ardabili²,

Mosavi

et al. 2019

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Automated controlling the harvesting systems can significantly increase the efficiency of the agricultural practices and prevent food wastes. Modeling and improvement of the combine harvester can increase the overall performance. Machine learning methods provide the opportunity of advanced modeling for accurate prediction of the highest performance of the machine. In this study, the modeling of combine harvesting id performed using radial basis function (RBF) and the hybrid machine learning method of adaptive neuro-fuzzy inference system (ANFIS) to predict various variables of the combine harvester for the optimal performance. Response surface methodology (RSM) is also used to optimize the models. The comparative study shows that the ANFIS method outperforms the RBF method.

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Modeling Daily Pan Evaporation in Humid Climates Using Gaussian Process Regression

Cited by 24 publications

References 19 publications

Deep Learning and Machine Learning in Hydrological Processes, Climate Change and Earth Systems: A Systematic Review

Deep Learning and Machine Learning in Hydrological Processes, Climate Change and Earth Systems: A Systematic Review

Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks

Prediction of Combine Harvester Performance Using Hybrid Machine Learning Modeling and Response Surface Methodology<strong> </strong>

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