[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127111-en":3,"doc-seo-127111-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127111,5909887254083,"Miles","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Computer model for detecting tsunami wave hazard on built-up land using machine learning and sentinel 2A satellite imagery - Research summary","Research develops a tsunami wave hazard scale for built-up land by extracting built-up density and classifying it with machine learning using Sentinel-2A satellite imagery combined with a digital elevation model (DEM). The workflow covers Sentinel-2A and DEM pre-processing, classification of VI data with machine learning, spatial prediction via ordinary kriging, field validation using a confusion matrix, and construction of a tsunami decision matrix. k-NN provides the most accurate built-up classification, with normalized difference built-up index (NDBI) achieving MSE 0.073 and MAE 0.003. DEM indicates 0–15 m elevation corresponds to high-to-medium vulnerability. Validation reports user accuracy 91.11%, producer/manufacturer accuracy 92.16%, and overall accuracy 91%.","Computer model for detecting tsunami wave hazard on built-upland using machine learning and sentinel 2A satellite imagery  \nSri Yulianto Joko Prasetyo1, Wiwin Sulistyo1, Erwin Christanto1, Bistok Hasiholan Simanjuntak2  \n1Department of Informatic Engineering, Information Technology Faculty, Satya Wacana Christian University, Salatiga, Indonesia 2Bussines and Agriculture Faculty, Satya Wacana Christian University , Salatiga, Indonesia  \n\n| Article history:\u003Cbr>Received Maret 5, 2023 Revised Sep 20, 2023 Accepted Nov 7, 2023 | The aim of this research is to compile a tsunami wave hazard scale based on built-up land density extracted and classified by machine learning from Sentinel 2A satellite and digital elevation model (DEM) imageries. This research was carried out in 5 stages, namely: (i) pre-processing of Sentinel 2A and DEM images, (ii) Classification of VI data using the machine learning algorithms, (iii) Spatial prediction using the ordinary kriging method,(iv) Field testing using the confusion matrix method, (v) Preparation of decision matrix for tsunami wave hazard. The results of the study show that the most accurate classification algorithm for classifying built-up indices data is the k-nearest neighbor (k-NN) algorithm. The results of the statistical accuracy test show that the most accurate is normalized difference built-up index (NDBI) with a mean of square error (MSE) value of 0.073 and a mean of absolute error (MAE) of 0.003. DEM analysis shows that the research area is at an altitude of 0–15 meters above sea level so it is in the high vulnerability to medium vulnerability category. Field testing showed user accuracy of 91.11%, manufacturer accuracy of 92.16%, and overall average accuracy of 91% .\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Build-up Computer model Machine learning Spatial interpolation Tsunami Vegetation indices |  |\n\nCorresponding Author:  \nSri Yulianto Joko Prasetyo  \nDepartment of Informatic Engineering, Information Technology Faculty Satya Wacana Christian University  \nSalatiga, Indonesia  \nEmail: [sri.yulianto@uksw.edu](sri.yulianto@uksw.edu)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nMachine learning (ML) is a type of artificial intelligence that uses certain algorithms through the process of analyzing large amounts of data which their nature is multidimensional and uses patterns to produce new values as predictive data based on the provided historical data features [1]–[3] . ML is an algorithm for the process of predicting and detecting a phenomenon that is described in remote sensing imagery and occurs in locations or positions that are not accessible to human vision, namely the support vector machine (SVM), random forest (RF), k-nearest neighbour (k-NN), multivariate adaptive regression splines (MARS) and artificial neural networks (ANN) [4]–[6] . ML is used as a method for computing, classifying and predicting data in the form of pixels or digital number (DN) derived from Landsat 8 OLI and Sentinel 2A satellite images [7]–[10] . The research on the classification of buildings destroyed as the impact of tsunami wave in Japan in 2011 and the impact of earthquake and tsunami wave in 2016 was done using the SVM algorithm [11] . Prediction of inundation on coastal tsunami wave is made using the ANN algorithm [12] . Tsunami wave vulnerability modelling on the coast and residential areas is created using data from normalized difference vegetation index (NDVI), modified soil adjusted vegetation index (MSAVI), normalized difference water index (NDWI), modified normalized difference water index (MNDWI), and  \nnormalized difference built-up index (NDBI) extracted from Sentinel 2A satellite images. The Sentinel-2AMSI images has 13 spectral bands in the visible, NIR, and SWIR wavelength region with spatial resolutions of 10–60 m as shown in Table 1 [13] . The SVM aims to achieve optimal separation between hyper planesand/or hyper planes located in","cbCaiaYubHtvmSf0","https://ap.wps.com/l/cbCaiaYubHtvmSf0","pdf",731565,1,12,"English","en",105,"# Article Info\n## Abstract\n## Introduction\n## Method Overview\n## Results and Accuracy Assessment\n## Field Testing and Validation\n## Decision Matrix Preparation","[{\"question\":\"What is the main goal of the research?\",\"answer\":\"To compile a tsunami wave hazard scale for built-up land by extracting and classifying built-up density using machine learning and Sentinel-2A imagery together with DEM data.\"},{\"question\":\"Which machine learning algorithm and index perform best?\",\"answer\":\"The k-nearest neighbor (k-NN) algorithm gives the most accurate classification. The normalized difference built-up index (NDBI) achieves the best statistical accuracy with MSE 0.073 and MAE 0.003.\"},{\"question\":\"How is model accuracy validated and what accuracy values are reported?\",\"answer\":\"Field testing uses a confusion matrix method. Reported user accuracy is 91.11%, manufacturer/producer accuracy is 92.16%, and overall average accuracy is about 91%.\"}]","Computer model for detecting tsunami wave hazard on built-up land using machine learning and sentinel 2A satellite imagery - Research summary | PDF",1785936897,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"computer-model-for-detecting-tsunami-wave-hazard-on-built-up-land-using-machine-learning-and-sentinel-2a-satellite-imagery-research-summary","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/computer-model-for-detecting-tsunami-wave-hazard-on-built-up-land-using-machine-learning-and-sentinel-2a-satellite-imagery-research-summary/127111/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the research?","Question",{"text":75,"@type":76},"To compile a tsunami wave hazard scale for built-up land by extracting and classifying built-up density using machine learning and Sentinel-2A imagery together with DEM data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithm and index perform best?",{"text":80,"@type":76},"The k-nearest neighbor (k-NN) algorithm gives the most accurate classification. The normalized difference built-up index (NDBI) achieves the best statistical accuracy with MSE 0.073 and MAE 0.003.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model accuracy validated and what accuracy values are reported?",{"text":84,"@type":76},"Field testing uses a confusion matrix method. 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