A Meta-Learning Approach of Optimisation for Spatial Prediction of Landslides

Optimisation plays a key role in the application of machine learning in the spatial prediction of landslides. The common practice in optimising landslide prediction models is to search for optimal/suboptimal hyperparameter values in a number of predetermined hyperparameter configurations based on an...

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Autores principales:	Biswajeet Pradhan, Maher Ibrahim Sameen, Husam A. H. Al-Najjar, Daichao Sheng, Abdullah M. Alamri, Hyuck-Jin Park
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	landslide susceptibility machine learning bayesian optimisation meta-learning GIS LiDAR Science Q
Acceso en línea:	https://doaj.org/article/d9f6413b0fc947bcb2897e97b34baabd
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spelling	oai:doaj.org-article:d9f6413b0fc947bcb2897e97b34baabd2021-11-25T18:53:53ZA Meta-Learning Approach of Optimisation for Spatial Prediction of Landslides10.3390/rs132245212072-4292https://doaj.org/article/d9f6413b0fc947bcb2897e97b34baabd2021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4521https://doaj.org/toc/2072-4292Optimisation plays a key role in the application of machine learning in the spatial prediction of landslides. The common practice in optimising landslide prediction models is to search for optimal/suboptimal hyperparameter values in a number of predetermined hyperparameter configurations based on an objective function, i.e., k-fold cross-validation accuracy. However, the overhead of hyperparameter optimisation can be prohibitive, especially for computationally expensive algorithms. This paper introduces an optimisation approach based on meta-learning for the spatial prediction of landslides. The proposed approach is tested in a dense tropical forested area of Cameron Highlands, Malaysia. Instead of optimising prediction models with a large number of hyperparameter configurations, the proposed approach begins with promising configurations based on several basic and statistical meta-features. The proposed meta-learning approach was tested based on Bayesian optimisation as a hyperparameter tuning algorithm and random forest (RF) as a prediction model. The spatial database was established with a total of 63 historical landslides and 15 conditioning factors. Three RF models were constructed based on (1) default parameters as suggested by the sklearn library, (2) parameters suggested by the Bayesian optimisation (BO), and (3) parameters suggested by the proposed meta-learning approach (BO-ML). Based on five-fold cross-validation accuracy, the Bayesian method achieved the best performance for both the training (0.810) and test (0.802) datasets. The meta-learning approach achieved slightly lower accuracies than the Bayesian method for the training (0.769) and test (0.800) datasets. Similarly, based on F1-score and area under the receiving operating characteristic curves (AUROC), the models with optimised parameters either by the Bayesian or meta-learning methods produced more accurate landslide susceptibility assessment than the model with the default parameters. In the present approach, instead of learning from scratch, the meta-learning would begin with hyperparameter configurations optimal for the most similar previous datasets, which can be considerably helpful and time-saving for landslide modelings.Biswajeet PradhanMaher Ibrahim SameenHusam A. H. Al-NajjarDaichao ShengAbdullah M. AlamriHyuck-Jin ParkMDPI AGarticlelandslide susceptibilitymachine learningbayesian optimisationmeta-learningGISLiDARScienceQENRemote Sensing, Vol 13, Iss 4521, p 4521 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	landslide susceptibility machine learning bayesian optimisation meta-learning GIS LiDAR Science Q
spellingShingle	landslide susceptibility machine learning bayesian optimisation meta-learning GIS LiDAR Science Q Biswajeet Pradhan Maher Ibrahim Sameen Husam A. H. Al-Najjar Daichao Sheng Abdullah M. Alamri Hyuck-Jin Park A Meta-Learning Approach of Optimisation for Spatial Prediction of Landslides
description	Optimisation plays a key role in the application of machine learning in the spatial prediction of landslides. The common practice in optimising landslide prediction models is to search for optimal/suboptimal hyperparameter values in a number of predetermined hyperparameter configurations based on an objective function, i.e., k-fold cross-validation accuracy. However, the overhead of hyperparameter optimisation can be prohibitive, especially for computationally expensive algorithms. This paper introduces an optimisation approach based on meta-learning for the spatial prediction of landslides. The proposed approach is tested in a dense tropical forested area of Cameron Highlands, Malaysia. Instead of optimising prediction models with a large number of hyperparameter configurations, the proposed approach begins with promising configurations based on several basic and statistical meta-features. The proposed meta-learning approach was tested based on Bayesian optimisation as a hyperparameter tuning algorithm and random forest (RF) as a prediction model. The spatial database was established with a total of 63 historical landslides and 15 conditioning factors. Three RF models were constructed based on (1) default parameters as suggested by the sklearn library, (2) parameters suggested by the Bayesian optimisation (BO), and (3) parameters suggested by the proposed meta-learning approach (BO-ML). Based on five-fold cross-validation accuracy, the Bayesian method achieved the best performance for both the training (0.810) and test (0.802) datasets. The meta-learning approach achieved slightly lower accuracies than the Bayesian method for the training (0.769) and test (0.800) datasets. Similarly, based on F1-score and area under the receiving operating characteristic curves (AUROC), the models with optimised parameters either by the Bayesian or meta-learning methods produced more accurate landslide susceptibility assessment than the model with the default parameters. In the present approach, instead of learning from scratch, the meta-learning would begin with hyperparameter configurations optimal for the most similar previous datasets, which can be considerably helpful and time-saving for landslide modelings.
format	article
author	Biswajeet Pradhan Maher Ibrahim Sameen Husam A. H. Al-Najjar Daichao Sheng Abdullah M. Alamri Hyuck-Jin Park
author_facet	Biswajeet Pradhan Maher Ibrahim Sameen Husam A. H. Al-Najjar Daichao Sheng Abdullah M. Alamri Hyuck-Jin Park
author_sort	Biswajeet Pradhan
title	A Meta-Learning Approach of Optimisation for Spatial Prediction of Landslides
title_short	A Meta-Learning Approach of Optimisation for Spatial Prediction of Landslides
title_full	A Meta-Learning Approach of Optimisation for Spatial Prediction of Landslides
title_fullStr	A Meta-Learning Approach of Optimisation for Spatial Prediction of Landslides
title_full_unstemmed	A Meta-Learning Approach of Optimisation for Spatial Prediction of Landslides
title_sort	meta-learning approach of optimisation for spatial prediction of landslides
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d9f6413b0fc947bcb2897e97b34baabd
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A Meta-Learning Approach of Optimisation for Spatial Prediction of Landslides

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