Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil

Nguyen, Quang Hung; Ly, Haï‐Bang; Ho, Lanh Si; Al‐Ansari, Nadhir; Le, Hiep Van; Tran, Van Quan; Prakash, Indra; Pham, Binh Thai

doi:10.1155/2021/4832864

Cited by 322 publications

(165 citation statements)

References 95 publications

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“…In this study, we have followed the influence of data Splitting performance (Nguyen et al, 2021) to divided the entire dataset was splitting into 70:30 ratio. Some of the field photographs in this study area are shown in Figure 3.…”

Section: Ls Inventory Mapmentioning

confidence: 99%

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

et al. 2021

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The optimal prediction of land subsidence (LS) is very much difficult because of limitations in proper monitoring techniques, field-base surveys and knowledge related to functioning and behavior of LS. Thus, due to the lack of LS susceptibility maps it is almost impossible to identify LS prone areas and as a result it influences severe economic and human losses. Hence, preparation of LS susceptibility mapping (LSSM) can help to prevent natural and human catastrophes and reduce the economic damages significantly. Machine learning (ML) techniques are becoming increasingly proficient in modeling purpose of such kinds of occurrences and they are increasing used for LSSM. This study compares the performances of single and hybrid ML models to preparation of LSSM for future prediction of performance analysis. In this study, the spatial prediction of LS was assessed using four ML models of maximum entropy (MaxEnt), general linear model (GLM), artificial neural network (ANN) and support vector machine (SVM). Alongside, the possible numbers of novel ensemble models were integrated through the aforementioned four ML models for optimal analysis of LSSM. An inventory LS map was prepared based on the previous occurrences of LS points and the dataset were divvied into 70:30 ratios for training and validating of the modeling process. To identify the robust and best LSSMs, receiver operating characteristic-area under curve (ROC-AUC) curve was employed. The ROC-AUC result indicated that ANN model gives the highest ROC-AUC (0.924) in training accuracy. The highest AUC (0.823) of the LSSMs was determined based on validation datasets identified by SVM followed by ANN-SVM (0.812).

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Section: Ls Inventory Mapmentioning

confidence: 99%

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

et al. 2021

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“…The training process was carried out with the amount of data as much as 70% of the dataset, while testing was carried out with 30% of the data from the dataset ( Nguyen et al, 2021 ). Both the training and testing processes for each topology were repeated ten times by varying the initial weight values.…”

Section: Methodsmentioning

confidence: 99%

Determining the number of hidden layer and hidden neuron of neural network for wind speed prediction

Rachmatullah

Santoso

Surendro

2021

PeerJ Computer Science

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Artificial neural network (ANN) is one of the techniques in artificial intelligence, which has been widely applied in many fields for prediction purposes, including wind speed prediction. The aims of this research is to determine the topology of neural network that are used to predict wind speed. Topology determination means finding the hidden layers number and the hidden neurons number for corresponding hidden layer in the neural network. The difference between this research and previous research is that the objective function of this research is regression, while the objective function of previous research is classification. Determination of the topology of the neural network using principal component analysis (PCA) and K-means clustering. PCA is used to determine the hidden layers number, while clustering is used to determine the hidden neurons number for corresponding hidden layer. The selected topology is then used to predict wind speed. Then the performance of topology determination using PCA and clustering is then compared with several other methods. The results of the experiment show that the performance of the neural network topology determined using PCA and clustering has better performance than the other methods being compared. Performance is determined based on the RMSE value, the smaller the RMSE value, the better the neural network performance. In future research, it is necessary to apply a correlation or relationship between input attribute and output attribute and then analyzed, prior to conducting PCA and clustering analysis.

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“…In [5], Nguyen Q. et al have evaluated and compared the performance of different machine learning models, such as artificial neural network, extreme learning machine, and boosting trees (Boosted) models, considering the influence of various training to testing ratios in predicting the soil shear strength. Although the researchers are environmental engineers, they have done a great job in their investigation by dividing the datasets into training and testing datasets to assess the models' performance.…”

Section: Related Workmentioning

confidence: 99%

Algorithmic Splitting: A Method for Dataset Preparation

Kahloot¹,

Ekler²

2021

IEEE Access

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Datasets that appear in publications are curated and split into training, testing, and validation sub-datasets by domain experts. Consequently, machine learning models typically perform well on such split-by-hand datasets, whereas preparing real-world datasets into curated splits, i.e., training, testing, and validation sub-datasets, require extensive effort. Usually, random repetitive splitting is carried out, practiced, and evaluated until a better score is reached on the evaluation metrics. In this paper, a novel algorithmic method is proposed for splitting datasets for machine learning models. Algorithmic Splitting utilizes deterministic dimension reduction and density-based clustering techniques for estimating the data distribution. Afterward, splitting the dataset is carried out based on the estimated data distribution. Our objective is to achieve evenly representative splits of a given dataset in a standard and algorithmic way that reduces the perplexity of random splitting using the thorough splitting method. Experiments demonstrate the potential of Algorithmic Splitting through qualitative and quantitative evaluation of MNIST, Fashion MNIST, CIFAR-10, SmallNORB, and Shapes3D.

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Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil

Cited by 322 publications

References 95 publications

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

Determining the number of hidden layer and hidden neuron of neural network for wind speed prediction

Algorithmic Splitting: A Method for Dataset Preparation

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