Comparative study of multilayer perceptron-stochastic gradient descent and gradient boosted trees for predicting daily suspended sediment load: The case study of the Mississippi River, U.S.

Shadkani, Sadra; Abbaspour, Akram; Samadianfard, Saeed; Hashemi, Sajjad; Mosavi, Amir; Band, Shahab S.

doi:10.1016/j.ijsrc.2020.10.001

Cited by 50 publications

(11 citation statements)

References 38 publications

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“…A multilayer perceptron (MLP), also known as an artificial neural network (ANN), is a forward structure of artificial neural networks. MLP is one of the most popular networks and possesses a powerful ability to solve nonlinear problems and is highly efficient at calculation [37][38][39].…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Analysis and Prediction of Gestational Diabetes Mellitus by the Ensemble Learning Method

Wang

Zhang

et al. 2022

Int J Comput Intell Syst

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Gestational diabetes mellitus (GDM) is the most common disease in pregnancy and can cause a series of maternal and infant complications. A new study shows that GDM affects one in six deliveries. Identifying and screening for risk factors for GDM can effectively help intervene and improve the condition of women and their children. Therefore, the aim of this paper is to determine the risk factors for GDM and to use the ensemble learning method to judge whether pregnant women suffer from GDM more accurately. First, this study involves six commonly used machine learning algorithms to analyze the GDM data from the Tianchi competition, selects the risk factors according to the ranking of each model, and uses the Shapley additive interpreter method to determine the importance of the selected risk factors. Second, the combined weighting method was used to analyze and evaluate the risk factors for gestational diabetes and to determine a group of important factors. Lastly, a new integrated light gradient-boosting machine-extreme gradient boosting-gradient boosting tree (LightGBM-Xgboost-GB) learning method is proposed to determine whether pregnant women have gestational diabetes mellitus. We used the gray correlation degree to calculate the weight and used a genetic algorithm for optimization. In terms of prediction accuracy and comprehensive effects, the final model is better than the commonly used machine learning model. The ensemble learning model is comprehensive and flexible and can be used to determine whether pregnant women suffer from GDM. In addition to disease prediction, the model can also be extended for use to many other areas of research.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Analysis and Prediction of Gestational Diabetes Mellitus by the Ensemble Learning Method

Wang

Zhang

et al. 2022

Int J Comput Intell Syst

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“…The BP algorithm optimally adjusts the weight between nodes and minimizes errors in the training process. For training and optimization of MLP network weights and error calculation, optimization methods based on descent gradient, mean squared error (MSE), Levenberg_Marquardt (LM) and scaled conjugate gradient (SCG) have been used [41]. To solve the function of the LM algorithm, a Hessian matrix is considered [42].…”

Section: Model Input Informationmentioning

confidence: 99%

Optimal Location of Water Quality Monitoring Stations Using an Artificial Neural Network Modeling in the Qarah-Chay River Basin, Iran

Goudarzi

Hedayatiaghmashhadi

Kazemi

et al. 2022

Water

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The economic development, livelihood and drinking water of millions of people in the central plateau of Iran depend on the Qarah-Chay River, but due to a lack of inappropriate monitoring, it has been exposed to destruction and pollution. Consequently, an assessment of the river’s water quality is of utmost importance for both the management of human health and the maintenance of a safe environment, which can be achieved by determining the best locations for pollution monitoring stations along rivers. In this study, artificial neural networks (ANNs) has been used to optimize the locations for Qarah-Chay River monitoring stations in Markazi province, Iran. The data are collected based on the Iranian Water Quality Index (IRWQI), the US National Sanitation Foundation Water Quality Index (NSFWQI) and the Oregon Water Quality Index (OWQI). The database is given to a multilayer perceptron (MLP) neural network along with a geographic information system (GIS). The output of this study identified six pollution monitoring stations on the river, which are mainly downstream due to the accumulation of land uses and the concentration of pollution. The gradient of the MLP network training courses model from the proposed monitoring stations is 0.062299. In addition, the performance evaluation criteria of the proposed MLP model for F1-score, recall, precision and accuracy were 0.85, 0.84, 0.88 and 0.88, respectively. The results obtained help managers to properly monitor the river’s water resources with accuracy, efficiency and lower cost; furthermore, the findings were able to provide scientific references for river water quality monitoring and river ecosystem protection.

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“…Moreover, it does not depend on the mathematical model of the research object and has good robustness to the system parameter changes and external interference of the controlled object. Terefore, it is suitable for dealing with complex multiinput and multioutput nonlinear systems [30].…”

Section: Multilayer Perceptron (Mlp) Model Prediction Improvesmentioning

confidence: 99%

A Hyperparameter Optimization Algorithm for the LSTM Temperature Prediction Model in Data Center

Wang

et al. 2022

Scientific Programming

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As the main tool to realize data mining and efficient knowledge acquisition in the era of big data, machine learning is widely used in data center energy-saving research. The temperature prediction model based on machine learning predicts the state of the data center according to the upcoming tasks. It can adjust the refrigeration equipment in advance to avoid temperature regulation lag and set the air conditioning temperature according to the actual demand to avoid excessive refrigeration. Task scheduling and migration algorithm based on temperature prediction can effectively avoid hot spots. However, the choice of hyperparameter of machine learning model has a great impact on its performance. In this study, a hyperparameter optimization algorithm based on MLP is proposed. On the basis of trying certain hyperparameters, the MLP model is used to predict the value of all hyperparameters’ space, and then, a certain number of high-quality hyperparameters are selected to train the model repeatedly. In each iteration, the amount of training data decreases gradually, while the accuracy of the model improves rapidly, and finally, the appropriate hyperparameter are obtained. We use the idea of mutation in the genetic algorithm to improve the probability of high-quality solutions and the loss function weighting method to select the solution with the best stability. Experiments are carried out on two representative machine learning models, LSTM and Random Forest, and compared with the standard Gaussian Bayes and Random Search method. The results show that the method proposed in this study can obtain high-precision and high-stability hyperparameter through one run and can greatly improve the operation efficiency. This algorithm is not only effective for LSTM but also suitable for other machine learning models.

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Comparative study of multilayer perceptron-stochastic gradient descent and gradient boosted trees for predicting daily suspended sediment load: The case study of the Mississippi River, U.S.

Cited by 50 publications

References 38 publications

Analysis and Prediction of Gestational Diabetes Mellitus by the Ensemble Learning Method

Analysis and Prediction of Gestational Diabetes Mellitus by the Ensemble Learning Method

Optimal Location of Water Quality Monitoring Stations Using an Artificial Neural Network Modeling in the Qarah-Chay River Basin, Iran

A Hyperparameter Optimization Algorithm for the LSTM Temperature Prediction Model in Data Center

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