An automatic hyperparameter optimization DNN model for precipitation prediction

Peng, Yuzhong; DaoQing, Gong; Deng, Chao; Hong-ya, LI; Cai, Hongguo; Zhang, Hao

doi:10.1007/s10489-021-02507-y

Cited by 19 publications

(12 citation statements)

References 34 publications

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“…Hyperparameters are artificially set parameters before machine learning begins, whose values cannot be estimated from the data, and are often used to help estimate model parameters. Common hyperparameter optimization methods include grid search, random search and Bayesian optimization [ 63 , 64 ]. As there are few hyperparameters that need to be tuned in this study, grid search combined with five-fold cross validation is used to optimize the hyperparameters.…”

Section: Model Results and Discussionmentioning

confidence: 99%

Ensemble Machine-Learning-Based Prediction Models for the Compressive Strength of Recycled Powder Mortar

et al. 2023

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Recycled powder (RP) serves as a potential and prospective substitute for cementitious materials in concrete. The compressive strength of RP mortar is a pivotal factor affecting the mechanical properties of RP concrete. The application of machine learning (ML) approaches in the engineering problems, particularly for predicting the mechanical properties of construction materials, leads to high prediction accuracy and low experimental costs. In this study, 204 groups of RP mortar compression experimental data are collected from the literature to establish a dataset for ML, including 163 groups in the training set and 41 groups in the test set. Four ensemble ML models, namely eXtreme Gradient-Boosting (XGBoost), Random Forest (RF), Light Gradient-Boosting Machine (LightGBM) and Adaptive Boosting (AdaBoost), were selected to predict the compressive strength of RP mortar. The comparative results demonstrate that XGBoost has the highest prediction accuracy when the a10-index, MAE, RMSE and R2 of the training set are 0.926, 1.596, 2.155 and 0.950 and the a10-index, MAE, RMSE and R2 of the test set are 0.659, 3.182, 4.285 and 0.842, respectively. SHapley Additive exPlanation (SHAP) is adopted to interpret the prediction process of XGBoost and explain the influence of influencing factors on the compressive strength of RP mortar. According to the importance of influencing factors, the order is the mass replacement rate of RP, the size of RP, the kind of RP and the water binder ratio of RP. The compressive strength of RP mortar decreases with the increase in the RP mass replacement rate. The compressive strength of RBP mortar is slightly higher than that of RCP mortar. Machine learning technologies will benefit the construction industry by facilitating the rapid and cost-effective evaluation of RP material properties.

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Section: Model Results and Discussionmentioning

confidence: 99%

Ensemble Machine-Learning-Based Prediction Models for the Compressive Strength of Recycled Powder Mortar

et al. 2023

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“…It is interesting to note that the results of all of these papers were compared using the Bayesian optimization algorithm, in [90] and [88] they use simulated annealing and random search algorithm for comparison. In [91], [92] they Compare their results with the use of novel methods ( univariate dynamic encoding algorithm and the using an improved Gene Expression Programming) to enhance hyperparameters sequentially by GA and PSO algorithms. All results were significant improvement by choosing the right hyperparameters.…”

Section: Another Techniquementioning

confidence: 99%

Artificial Neural Network Hyperparameters Optimization: A Survey

Kadhim¹,

Abdullah²,

Ghathwan³

2022

Int. J. Onl. Eng.

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Machine-learning (ML) methods often utilized in applications like computer vision, recommendation systems, natural language processing (NLP), as well as user behavior analytics. Neural Networks (NNs) are one of the most es-sential ways to ML; the most challenging element of designing a NN is de-termining which hyperparameters to employ to generate the optimal model, in which hyperparameter optimization improves NN performance. This study includes a brief explanation regarding a few types of NN as well as some methods for hyperparameter optimization, as well as previous work results in enhancing ANN performance using optimization methods that aid research-ers and data analysts in developing better ML models via identifying the ap-propriate hyperparameter configurations.

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“…Given the nature of the simulation, which requires high efficiency and limited data, this area needs to be improved. In this study, we devised a method that can produce high prediction performance with a small number of input factors through the combination of a deep neural network (DNN) and an evolution algorithm (Peng et al, 2022).…”

Section: Road Temperature and Moisture Modellingmentioning

confidence: 99%

Development of black ice prediction model using GIS-based multi-sensor model validation

Hong

Yun

Yum³

et al. 2022

Nat. Hazards Earth Syst. Sci.

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Abstract. Fog, freezing rain, and snow (melt) quickly condense on road surfaces, forming black ice that is difficult to identify and causes major accidents on highways. As a countermeasure to prevent icing car accidents, it is necessary to predict the amount and location of black ice. This study advanced previous models through machine learning and multi-sensor-verified results. Using spatial (hill shade, river system, bridge, and highway) and meteorological (air temperature, cloudiness, vapour pressure, wind speed, precipitation, snow cover, specific heat, latent heat, and solar radiation energy) data from the study area (Suncheon–Wanju Highway in Gurye-gun, Jeollanam-do, South Korea), the amount and location of black ice were modelled based on system dynamics to predict black ice and then simulated with a geographic information system in units of square metres. The intermediate factors calculated as input factors were road temperature and road moisture, modelled using a deep neural network (DNN) and numerical methods. Considering the results of the DNN, the root mean square error was improved by 148.6 % and reliability by 11.43 % compared to a previous study (linear regression). Based on the model results, multiple sensors were buried at four selected points in the study area. The model was compared with sensor data and verified with the upper-tailed test (with a significance level of 0.05) and fast Fourier transform (freezing does not occur when frequency = 0.00001 Hz). Results of the verified simulation can provide valuable data for government agencies like road traffic authorities to prevent traffic accidents caused by black ice.

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An automatic hyperparameter optimization DNN model for precipitation prediction

Cited by 19 publications

References 34 publications

Ensemble Machine-Learning-Based Prediction Models for the Compressive Strength of Recycled Powder Mortar

Ensemble Machine-Learning-Based Prediction Models for the Compressive Strength of Recycled Powder Mortar

Artificial Neural Network Hyperparameters Optimization: A Survey

Development of black ice prediction model using GIS-based multi-sensor model validation

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