A comparative study of the performance of artificial neural network and multivariate regression in simulating springs discharge in the Caspian Southern Watersheds, Iran

Gholami, Vahid; Khaleghi, Mohammad Reza

doi:10.1007/s13201-018-0886-4

Cited by 8 publications

(3 citation statements)

References 19 publications

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“…Comparison of the predicted values of the model and observed values proved the linear model's efficiencies for simulating TPC, tannin, and protein in grain sorghum. Commonly, the neural network has the potential to mine the dominant rules of data, even in the case of scrambled data and this can be mentioned as the most magnificent characteristic of the technique, compared to the other methods (Gholami & Khaleghi, 2019).…”

Section: Resultsmentioning

confidence: 99%

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Prediction of tannin, protein, and total phenolic content of grain sorghum using image analysis and machine learning

2022

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Background and Objectives: Sorghum is an alternative crop where poor soil is a limiting factor for the production of corn. Laboratory measurements of chemical compounds of grains are expensive, time-consuming predominantly done on powdered samples. Therefore, a rapid and reliable method integrating image processing and machine learning was evaluated for the prediction of total phenolic compounds (TPCs), tannin, and protein.Findings: The highest TPC (0.83%) was obtained for genotype KGS36 (the brown pericarp) followed by KGS23 (0.54%). The concentration of tannin ranged from 0.008% to 0.616%. Mean comparison revealed variation for the protein concentration (15.17%-11.53%); the highest content obtained by KGS25 and the lowest by KGS36 (p < .01). Multilayer perceptron (MLP) as one of the common artificial neural networks (ANNs) applied to simulate the chemical of the grains was evaluated through the textural features extracted from grain images. For the MLP network, the minimum number of hidden networks was set to 8 and the maximum number was set to 25. For learning and saving, the number of networks was set to 20 and the number of saved networks was set to 5. The best MLP models were selected based on performance (R) and error values for the train, test, and validation sets. Conclusions: An ANN for the prediction of the chemical concentration was suggested. The correlations between predicted and observed data were higher than .915.Significance and Novelty: These models are of great significance for the prediction of chemical concentrations of grain sorghum in plant breeding and food industry.

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Section: Resultsmentioning

confidence: 99%

“…MLP is a basic type of neural network consisting of three layers including input, hidden, and output. The flow of information in the MLP network is in the forward direction (Gholami & Khaleghi, 2019).…”

Section: Overview Of the Anns And Statistical Analysismentioning

confidence: 99%

Prediction of tannin, protein, and total phenolic content of grain sorghum using image analysis and machine learning

2022

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“…The study revealed that ANFIS could accurately predict soil nutrients, which can help farmers in fertilizer management. The use of ANFIS to predict dairy cow milk yield based on environmental factors and management practices was also studied (Gholami et al, 2018). They showed that ANFIS can accurately predict milk yield, thereby helping farmers optimize feeding and management practices.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks and Adaptive Neuro Fuzzy Inference System for Wheat Yield Analysis and Prediction

Rao Agari,

Vishal,

Krishnan

2023

Rev. Agric. Neotrop.

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The current study evaluated the prediction of the yield of wheat crops in the Bagalkot district of Karnataka State, India. The study aimed to provide crop yield predictions to help farmers optimize their cultivation and marketing strategies. The model used various independent variables, such as temperature, humidity of air, and water resources, to predict growth in the yield of wheat crops. The correlation analysis helps determine the strength and direction of the relationship between the variables based on the results. The statistical analysis identifies the variables that have a significant impact on crop yield growth. The work developed and tested two different models (the Artificial Neural Network (ANN) model and the Adaptive Neuro-fuzzy Interference System (ANFIS) to predict crop yield growth based on the selected independent variables. The ANFIS model was particularly interesting as it can predict a mapping between the input and output parameters, which can be useful for understanding the relationships between different variables. ANFIS was considered a better predictor than ANN as the error percentage ranged from 0-3%. Overall, the work highlighted the importance of crop yield predictions and the potential benefits that simulations can generate for farmers and the agriculture sector in general.

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Climate change impacts on the Nahavand karstic springs using the data mining techniques

Fasihi,

Tizro,

Marofi

2024

Theor Appl Climatol

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A comparative study of the performance of artificial neural network and multivariate regression in simulating springs discharge in the Caspian Southern Watersheds, Iran

Cited by 8 publications

References 19 publications

Prediction of tannin, protein, and total phenolic content of grain sorghum using image analysis and machine learning

Prediction of tannin, protein, and total phenolic content of grain sorghum using image analysis and machine learning

Artificial Neural Networks and Adaptive Neuro Fuzzy Inference System for Wheat Yield Analysis and Prediction

Climate change impacts on the Nahavand karstic springs using the data mining techniques

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