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Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China

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SCIENCE OF THE TOTAL ENVIRONMENT 2018年 626卷 1121-1135页

作者： Chen, Wei Peng, Jianbing Hong, Haoyuan Shahabi, Himan Pradhan, Biswajeet Liu, Junzhi Zhu, A-Xing Pei, Xiangjun Duan, Zhao Xian Univ Sci & Technol Coll Geol & Environm Xian 710054 Shaanxi Peoples R China Changan Univ Dept Geol Engn Xian 710054 Shaanxi Peoples R China Nanjing Normal Univ Key Lab Virtual Geog Environm Nanjing 210023 Jiangsu Peoples R China State Key Lab Cultivat Base Geog Environm Evolut Nanjing 210023 Jiangsu Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing 210023 Jiangsu Peoples R China Univ Kurdistan Fac Nat Resources Dept Geomorphol Sanandaj Iran Univ Technol Sydney Sch Syst Management & Leadership Fac Engn & IT CB11-06-217Bldg 1181 BroadwayPOB 123 Ultimo NSW 2007 Australia Sejong Univ Dept Energy & Mineral Resources Engn 209 Neungdong Ro Seoul 05006 South Korea Chengdu Univ Technol State Key Lab Geohazard Prevent & Geoenvironm Pro Chengdu 610059 Sichuan Peoples R China

The preparation of a landslide susceptibility map is considered to be the first step for landslide hazard mitigation and risk assessment. However, these maps are accepted as end products that can be used for land use planning. The main goal of this study is to assess and compare four advanced machine learning techniques, namely the Bayes' net (BN), radical basis function (RBF) classifier, logisticmodel tree (LMT), and randomforest (RF) models, for landslide susceptibility modelling in Chongren County, China. A total of 222 landslide locations were identified in the study area using historical reports, interpretation of aerial photographs, and extensive field surveys. The landslide inventory data was randomly split into two groups with a ratio of 70/30 for training and validation purposes. Fifteen landslide conditioning factors were prepared for landslide susceptibility modelling. The spatial correlation between landslides and conditioning factors was analyzed using the information gain (IG) method. The BN, RBF classifier, LMT, and RF models were constructed using the training dataset. Finally, the receiver operating characteristic (ROC) and statistical measures, including sensitivity, specificity, and accuracy, were employed to validate and compare the predictive capabilities of the models. Out of the tested models, the RF model had the highest sensitivity, specificity, and accuracy values of 0.787, 0.716, and 0.752, respectively, for the training dataset. Overall, the RF model produced an optimized balance for the training and validation datasets in terms of AUC values and statistical measures. The results of this study also demonstrate the benefit of selecting optimal machine learning techniques with proper conditioning selection methods for landslide susceptibility modelling. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Landslide susceptibility Bayes' net Radical basis function classifier logistic model tree Random forest China

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A novel hybrid artificial intelligence approach for flood susceptibility assessment

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ENVIRONMENTAL modelLING & SOFTWARE 2017年第Sep.期95卷 229-245页

作者： Chapi, Kamran Singh, Vijay P. Shirzadi, Ataollah Shahabi, Himan Dieu Tien Bui Binh Thai Pham Khosravi, Khabat Univ Kurdistan Fac Nat Resources Dept Rangeland & Watershed Management Sanandaj Iran Texas A&M Univ Dept Biol & Agr Engn Coll Agr & Life Sci College Stn TX 77843 USA Univ Kurdistan Fac Nat Resources Dept Geomorphol Sanandaj Iran Univ Coll Southeast Norway Dept Business Adm & Comp Sci Geog Informat Syst Grp Hallvard Eikas Plass 1 N-3800 Boi Telemark Norway Univ Transport Technol Dept Geotech Engn 54 TrieuKhuc Hanoi Vietnam Univ Agr Sci & Nat Resources Sari Fac Nat Resources Dept Watershed Sci Engn Mazandaran Iran

A new artificial intelligence (AI) model, called Bagging-LMT - a combination of bagging ensemble and logistic model tree (LMT) - is introduced for mapping flood susceptibility. A spatial database was generated for the Haraz watershed, northern Iran, that included a flood inventory map and eleven flood conditioning factors based on the Information Gain Ratio (IGR). The model was evaluated using precision, sensitivity, specificity, accuracy, Root Mean Square Error, Mean Absolute Error, Kappa and area under the receiver operating characteristic curve criteria. The model was also compared with four state-of-the-art benchmark soft computing models, including LMT, logistic regression, Bayesian logistic regression, and random forest. Results revealed that the proposed model outperformed all these models and indicate that the proposed model can be used for sustainable management of flood-prone areas. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Flood susceptibility Bagging-LMT Bayesian logistic regression logistic model tree Iran

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Combining Sequence Entropy and Subgraph Topology for Complex Prediction in Protein Protein Interaction (PPI) Network

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CURRENT BIOINFORMATICS 2019年第6期14卷 516-523页

作者： Sikandar, Aisha Anwar, Waqas Sikandar, Misba COMSATS Inst Informat Technol Dept Comp Sci Abbottabad Pakistan COMSATS Inst Informat Technol Dept Comp Sci Lahore Pakistan

Background: Complex prediction from interaction network of proteins has become a challenging task. Most of the computational approaches focus on topological structures of protein complexes and fewer of them consider important biological information contained within amino acid sequences. Objective: To capture the essence of information contained within protein sequences we have computed sequence entropy and length. Proteins interact with each other and form different sub graph topologies. Methods: We integrate biological features with sub graph topological features and model complexes by using a logistic model tree. Results: The experimental results demonstrated that our method out performs other four state-of-art computational methods in terms of the number of detecting known protein complexes correctly. Conclusion: In addition, our framework provides insights into future biological study and might be helpful in predicting other types of sub graph topologies.

关键词： Protein Protein Interaction (PPI) sequence entropy sub graph topology biological features logistic model tree cluster

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Flood-prone area mapping using machine learning techniques: a case study of Quang Binh province, Vietnam

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NATURAL HAZARDS 2021年第3期108卷 3229-3251页

作者： Chinh Luu Quynh Duy Bui Costache, Romulus Luan Thanh Nguyen Thu Thuy Nguyen Tran Van Phong Hiep Van Le Binh Thai Pham Natl Univ Civil Engn Fac Hydraul Engn Hanoi Vietnam Natl Univ Civil Engn Dept Geodesy Hanoi Vietnam Transilvania Univ Brasov Dept Civil Engn 5 Turnului Str Brasov 500152 Romania Vietnam Acad Water Resources Key Lab River & Coastal Engn Hanoi Vietnam Univ Technol Sydney Ctr Technol Water & Wastewater Sch Civil & Environm Engn Sydney NSW 2007 Australia Vietnam Acad Sci & Technol Inst Geol Sci Hanoi Vietnam Univ Transport Technol Hanoi Vietnam

Vietnam's central coastal region is the most vulnerable and always at flood risk, severely affecting people's livelihoods and socio-economic development. In particular, Quang Binh province is often affected by floods and storms over the year. However, it still lacks studies on flood hazard estimation and prediction tools in this area. This study aims to develop a flooding susceptibility assessment tool using various machine learning (ML) techniques namely alternating decision tree (AD tree), logistic model tree (LM tree), reduced-error pruning tree (REP tree), J48 decision tree (J48) and Naive Bayes tree (NB tree);historical flood marks;and available data of topography, hydrology, geology, and environment considering Quang Binh province as a study area. We used flood mark locations of major flooding events in the years 2007, 2010, and 2016;and ten flood conditioning factors to construct and validate the ML models. Various validation methods, including area under the ROC curve (AUC), were used to validate and compare the models. The result of the models' validation suggests that all models have good performance: AD tree (AUC = 0.968), LM tree (AUC = 0.967), REP tree (AUC = 0.897), J48 (AUC = 0.953), and NB tree (AUC = 0.986). Out of these, NB tree managed to achieve the best performance in terms of flood prediction with an accuracy higher than 92 %. The final flood susceptibility map highlights 6,265 km(2) (78.8 % area) with a very low flooding hazard, 391 km(2) (4.9 % area) with a low flooding hazard, 224 km(2) (2.8 % area) with a moderate flooding hazard, 243 km(2) (3.1 %) with a high flooding hazard, and 829 km(2) (10.4 % area) with very high flooding hazard. The final flooding susceptibility assessment map could add a valuable source for flood risk reduction and management activities of Quang Binh province.

关键词： Flood susceptibility map Alternating decision tree logistic model tree Reduced-error pruning tree J48 Naive Bayes tree

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A Comparison of Different Machine Learning Algorithms for the Prediction of Anti-HIV-1 Peptides Based on Their Sequence-Related Properties

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INTERNATIONAL JOURNAL OF PEPTIDE RESEARCH AND THERAPEUTICS 2015年第1期21卷 57-62页

作者： Poorinmohammad, Naghmeh Mohabatkar, Hassan Univ Isfahan Fac Adv Sci & Technol Dept Biotechnol Esfahan Iran

Human immunodeficiency virus-1 (HIV-1) is the etiological agent for the global concerning disease "AIDS". The virus infects 35 million people globally and after 30 years, the disease remains a challenge. Despite great efforts in finding efficient treatment strategies, the pandemic of AIDS is continuing and the rate of new infections has not been diminished. Therefore, the need for finding novel treatment strategies is still of great importance. Peptide-based therapeutics has shown promise in the treatment of many challenging diseases such as various types of cancers and also HIV. Since time and money are the two restricting factors in any experimental researches, computer-aided techniques can dramatically reduce time and costs. In the present study, we developed a method based on pseudo amino acid composition of amino acid sequences to classify the anti-HIV-1 peptides using different machine learning algorithms. The performance of each algorithm was investigated and after comparing the performance parameters, the most accurate algorithm was proposed for predicting anti-HIV-1 activity of any given peptide. Having the accuracies of 96.15 and 83.71 % respectively, multilayer perceptron (MLP) and logistic model tree algorithms were primarily shown to be the most accurate ones in classifying anti-HIV-1 peptides. Final results demonstrate that model generated by MLP can be a valuable tool for the classification and prediction of anti-HIV-1 peptides in order to have a preliminary prediction which can be further coupled with experimental assays while reducing time and costs.

关键词： Human immunodeficiency virus-1 Machine learning Anti-HIV-1 peptide Multilayer perceptron logistic model tree

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Improved tree-based machine learning algorithms combining with bagging strategy for landslide susceptibility modeling

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ARABIAN JOURNAL OF GEOSCIENCES 2022年第2期15卷 1-19页

作者： Zhang, Tingyu Quevedo, Renata Pacheco Wang, Huanyuan Fu, Quan Luo, Dan Wang, Tao de Oliveira, Guilherme Garcia Guasselli, Laurindo Antonio Renno, Camilo Daleles Minist Nat Resources Key Lab Degraded & Unused Land Consolidat Engn Xian 710064 Shaanxi Peoples R China Shaanxi Prov Land Engn Construct Grp Co Ltd Inst Land Engn & Technol Xian 710064 Shaanxi Peoples R China Natl Inst Space Res INPE Earth Observat & Geoinformat Div BR-12227010 Sao Jose Dos Campos SP Brazil Design Inst Co Ltd Shaanxi Prov Land Engn Construct Grp Land Survey Xian 710075 Shaanxi Peoples R China Univ Fed Rio Grande do Sul State Ctr Res Remote Sensing & Meteorol BR-90501970 Porto Alegre RS Brazil

Landslide is considered one of the most dangerous natural hazards. Reasonable landslide susceptibility mapping can aid decision makers in landslide prevention. For this reason, based on the field survey data of landslide in Chenggu County, Shaanxi Province, China, 15 conditioning factors (altitude, slope, aspect, plan curvature, profile curvature, SPI, TWI, distance to roads, distance to rivers, distance to faults, rainfall, NDVI, soil, lithology, and land use) were selected and quantified by the certainty factor index. Then, 184 landslides data were divided into training and validation datasets according to the ratio of 7/3. Based on the GIS platform, three hybrid tree-based models, namely decision tree (DT), logistic model tree (LMT), and reduced error pruning tree (REPT), were established. Additionally, the bagging method was applied to build three bag-hybrid tree-based models: Bag-DT, Bag-LMT, and Bag-REPT. Finally, the landslide susceptibility maps were produced, and statistical indexes, seed cell area index and the ROC curve, were used for model validation and comparison. The results showed that the bagging method can significantly improve the classification ability of hybrid models. Furthermore, the Bag-REPT presented the best performance, with an accuracy value of 92.5%, being a suitable model for landslide susceptibility mapping in the study area.

关键词： Landslide susceptibility Decision tree logistic model tree Reduced error pruning tree Hybrid models Bagging strategy

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Significant factors associated with malaria spread in Thailand: a cross-sectional study

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JOURNAL OF HEALTH RESEARCH 2022年第3期36卷 515-523页

作者： Krainara, Patcharaporn Dumrongrojwatthana, Pongchai Bhattarakosol, Pattarasinee Chulalongkorn Univ Fac Sci Bangkok Thailand Chulalongkorn Univ Fac Sci Math & Comp Sci Bangkok Thailand

Purpose This paper aims to uncover new factors that influence the spread of malaria. Design/methodology/approach The historical data related to malaria were collected from government agencies. Later, the data were cleaned and standardized before passing through the analysis process. To obtain the simplicity of these numerous factors, the first procedure involved in executing the factor analysis where factors' groups related to malaria distribution were determined. Therefore, machine learning was deployed, and the confusion matrices are computed. The results from machine learning techniques were further analyzed with logistic regression to study the relationship of variables affecting malaria distribution. Findings This research can detect 28 new noteworthy factors. With all the defined factors, the logistics model tree was constructed. The precision and recall of this tree are 78% and 82.1%, respectively. However, when considering the significance of all 28 factors under the logistic regression technique using forward stepwise, the indispensable factors have been found as the number of houses without electricity (houses), number of irrigation canals (canals), number of shallow wells (places) and number of migrated persons (persons). However, all 28 factors must be included to obtain high accuracy in the logistics model tree. Originality/value This paper may lead to highly-efficient government development plans, including proper financial management for malaria control sections. Consequently, the spread of malaria can be reduced naturally.

关键词： Malaria distribution Malaria control logistic model tree Risk factors Risk model Thailand

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Friction stir welding tool condition monitoring using vibration signals and Random forest algorithm-A Machine learning approach 28

Friction stir welding tool condition monitoring using vibrat...

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28th International Conference on Processing and Fabrication of Advanced Materials (PFAM)

作者： Balachandar, K. Jegadeeshwaran, R. Vellore Inst Technol Sch Mech Engn Chennai 600127 Tamil Nadu India Prathyusha Engn Coll Dept Mech Engn Thiruvallur 602025 Tamil Nadu India

Friction stir welding (FSW) is a new kind of solid-state welding technique. Rigid and reliable joints in intricate shapes are possible with FSW. This type of welding process is frequently used in many commercial applications like automobile, shipbuilding, aerospace, and many more. The monitoring of the welding tool condition is essential for the inline process to identify and avoid the early defect of the workpiece and the breakdown due to the increase of the ideal condition of the machinery. Condition monitoring of the FSW tool is an advanced and novel predictive maintenance technique, in which the real-time vibration data are collected from the FSW machine under different operating conditions using an accelerometer sensor. The acquired vibration signals were analyzed using the machine learning approaches through feature extraction and feature classification. This paper aims the vibration analysis based on FSW tool condition monitoring using machine learning algorithms such as decision tree, logistic model tree (LMT), Hoeffeding, and Random forest. Among all classifiers, Random Forest gives better results. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 28th International Conference on Processing and Fabrication of Advanced Materials.

关键词： Friction stir welding logistic model tree Statistical features Random forest Confusion matrix

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Predicting circRNA-disease associations using deep generative adversarial network based on multi-source fusion information

Predicting circRNA-disease associations using deep generativ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wang, Lei You, Zhu-Hong Li, Li-Ping Zheng, Kai Wang, Yan-Bin Chinese Acad Sci Xinjiang Tech Inst Phys & Chem Urumqi 830011 Peoples R China China Univ Min & Technol Sch Comp Sci & Technol Xuzhou Jiangsu Peoples R China

ISBN: (纸本)9781728118673

Circular RNA (circRNA) is a kind of novel discovered non-coding RNA molecule with a closed loop structure, which plays a critical regulatory role in human diseases. Identifying the association between circRNAs and diseases has important potential value for the diagnosis and treatment of complex human diseases. Although biological experiments can more accurately identify the association between circRNAs and diseases, they are usually blind and limited by small scale and high cost. Therefore, there is an urgent need for efficient and feasible computational methods to predict the potential circRNA-disease associations on a large scale, so as to provide the most promising candidate for biological experiments. In this paper, we propose a novel computational method based on the deep Generative Adversarial Network (GAN) algorithm combined with the multi-source similarity information to predict the circRNA-disease associations. Firstly, we fuse the multi-source information of disease semantic similarity, disease and circRNA Gaussian interaction profile kernel similarity, and then use GAN to extract the hidden features of fusion information objectively and effectively in the way of confrontation learning, and finally send them to logistic model tree (LMT) classifier for accurate prediction. The 5-fold cross-validation experiment of the proposed model achieved 89.2% accuracy with 89.4% precision at the AUC of 90.6% on the CIRCR2Disease dataset. Compared with the state-of-the-art SVM classifier and other feature extraction methods, the proposed model shows strong competitiveness. In addition, the predicted results of this model are supported by the biological experiments, and 9 of the top 15 circRNA-disease associations with the highest scores were confirmed by recently published literature. These promising results indicate that the proposed model is an effective tool for predicting circRNA- disease associations and can provide reliable candidates for biological experiments.

关键词： Circular RNA Complex diseases CircRNA-disease association prediction Machine learning Generative adversarial network logistic model tree Biological networks

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A Comparative Analysis of Android Malware 5th

A Comparative Analysis of Android Malware

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5th International Conference on Information Systems Security and Privacy (ICISSP)

作者： Chavan, Neeraj Di Troia, Fabio Stamp, Mark San Jose State Univ Dept Comp Sci San Jose CA 95192 USA

ISBN: (纸本)9789897583599

In this paper, we present a comparative analysis of benign and malicious Android applications, based on static features. In particular, we focus our attention on the permissions requested by an application. We consider both binary classification of malware versus benign, as well as the multiclass problem, where we classify malware samples into their respective families. Our experiments are based on substantial malware datasets and we employ a wide variety of machine learning techniques, including decision trees and random forests, support vector machines, logistic model trees, AdaBoost, and artificial neural networks. We find that permissions are a strong feature and that by careful feature engineering, we can significantly reduce the number of features needed for highly accurate detection and classification.

关键词： Malware Android Machine Learning Random Forest logistic model tree Artificial Neural Network

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