Flood Estimation at Ungauged Sites Using a New Hybrid Model

Kashani, Mahsa Hassanpour; Montaseri, Majid; Yaghin, Mohammad Ali Lotfollahi

doi:10.3923/jas.2008.1744.1749

Cited by 10 publications

(3 citation statements)

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“…In the MLPNN model, starting from the input information in the first layer (independent variables), the information flows in only one direction and enters the output layer (dependent variable) by transferring from the hidden layer. The training process of MLPNN model involves adjusting and modifying the weights of the interface between neurons using different network training methods [ 91 ]. In this study, Broyden-Fletcher-Goldfarb-Shanno (BFGS) training algorithm has been used.…”

Section: Modeling Techniquesmentioning

confidence: 99%

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Predictive modeling of Persian walnut (Juglans regia L.) in vitro proliferation media using machine learning approaches: a comparative study of ANN, KNN and GEP models

et al. 2022

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Background Optimizing plant tissue culture media is a complicated process, which is easily influenced by genotype, mineral nutrients, plant growth regulators (PGRs), vitamins and other factors, leading to undesirable and inefficient medium composition. Facing incidence of different physiological disorders such as callusing, shoot tip necrosis (STN) and vitrification (Vit) in walnut proliferation, it is necessary to develop prediction models for identifying the impact of different factors involving in this process. In the present study, three machine learning (ML) approaches including multi-layer perceptron neural network (MLPNN), k-nearest neighbors (KNN) and gene expression programming (GEP) were implemented and compared to multiple linear regression (MLR) to develop models for prediction of in vitro proliferation of Persian walnut (Juglans regia L.). The accuracy of developed models was evaluated using coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE). With the aim of optimizing the selected prediction models, multi-objective evolutionary optimization algorithm using particle swarm optimization (PSO) technique was applied. Results Our results indicated that all three ML techniques had higher accuracy of prediction than MLR, for example, calculated R2 of MLPNN, KNN and GEP vs. MLR was 0.695, 0.672 and 0.802 vs. 0.412 in Chandler and 0.358, 0.377 and 0.428 vs. 0.178 in Rayen, respectively. The GEP models were further selected to be optimized using PSO. The comparison of modeling procedures provides a new insight into in vitro culture medium composition prediction models. Based on the results, hybrid GEP-PSO technique displays good performance for modeling walnut tissue culture media, while MLPNN and KNN have also shown strong estimation capability. Conclusion Here, besides MLPNN and GEP, KNN also is introduced, for the first time, as a simple technique with high accuracy to be used for developing prediction models in optimizing plant tissue culture media composition studies. Therefore, selection of the modeling technique to study depends on the researcher’s desire regarding the simplicity of the procedure, obtaining clear results as entire formula and/or less time to analyze.

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Section: Modeling Techniquesmentioning

confidence: 99%

“…The number of hidden layers was also determined by trial and error by reaching the minimum error rate. See [ 91 , 92 ] for more information.…”

Section: Modeling Techniquesmentioning

confidence: 99%

Predictive modeling of Persian walnut (Juglans regia L.) in vitro proliferation media using machine learning approaches: a comparative study of ANN, KNN and GEP models

et al. 2022

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“…Os neurônios de uma camada estão ligados aos neurônios de outra pela conexão chamada "pesos", os quais são responsáveis por transportar os resultados de uma camada para outra. Para processar a entrada de dados entre neurônios de cada camada é usada a função de ativação (HASSANPOUR KASHANI et al, 2008).…”

Section: Algoritmos De Machine Learningunclassified

Uso de machine learning no manejo da irrigação e estimativa da produtividade de milho em Piracicaba, SP

Queiroz

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Aos meus avós Romeu e Terezinha Queiroz, à minha avó Maria Aparecida Marques e ao meu padrinho Ivan Marques que hoje estão na eternidade e sempre me apoiaram. Suas lembranças me inspiram e me fazem persistir. AGRADECIMENTOSA Deus e a Nossa Senhora pela proteção e iluminação.Aos meus pais, Josete e Carlos, eu devo a vida e todas as oportunidades que nela recebi e espero um dia poder-lhes retribuir todo amor e confiança.Ao meu irmão Diogo, pelo apoio e companheirismo, principalmente nos momentos mais difíceis.Ao meu avô José, pela força, carinho e palavras de sabedoria durante toda essa trajetória. Aos meus familiares.

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Comparative Assessment of Reference Evapotranspiration Estimation Using Conventional Method and Machine Learning Algorithms in Four Climatic Regions

Raza

Shoaib

Faiz

et al. 2020

Pure Appl. Geophys.

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Flood Estimation at Ungauged Sites Using a New Hybrid Model

Cited by 10 publications

References 3 publications

Predictive modeling of Persian walnut (Juglans regia L.) in vitro proliferation media using machine learning approaches: a comparative study of ANN, KNN and GEP models

Predictive modeling of Persian walnut (Juglans regia L.) in vitro proliferation media using machine learning approaches: a comparative study of ANN, KNN and GEP models

Uso de machine learning no manejo da irrigação e estimativa da produtividade de milho em Piracicaba, SP

Comparative Assessment of Reference Evapotranspiration Estimation Using Conventional Method and Machine Learning Algorithms in Four Climatic Regions

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