{"title":"IDENTIFYING CLIMATE CHANGE VULNERABILITY BASED ON LAND COVER INDICATORS: A CASE STUDY IN SURABAYA, INDONESIA","authors":"F. Binarti, A. Santoso","doi":"10.21163/gt_2023.181.06","DOIUrl":"https://doi.org/10.21163/gt_2023.181.06","url":null,"abstract":"","PeriodicalId":45100,"journal":{"name":"Geographia Technica","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44625595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"USING DIGITAL TOOLS FOR MONITORING AND ANALYSING SPATIAL VARIATIONS OF POPULATION DISTRIBUTION IN THE CITY OF AL-MADINAH AL-MUNAWARAH, KINGDOM OF SAUDI ARABIA, 2004-2020","authors":"Mohamed Ahmed Aly Hassanien","doi":"10.21163/gt_2023.181.07","DOIUrl":"https://doi.org/10.21163/gt_2023.181.07","url":null,"abstract":"","PeriodicalId":45100,"journal":{"name":"Geographia Technica","volume":"10 15","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41276218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"GIS-BASED ANALYTICAL HIERARCHY PROCESS MODELING FOR FLOOD‑PRONE AREA MAPPING IN VIETNAM","authors":"H. Nguyen, G. Șerban","doi":"10.21163/gt_2023.181.05","DOIUrl":"https://doi.org/10.21163/gt_2023.181.05","url":null,"abstract":"","PeriodicalId":45100,"journal":{"name":"Geographia Technica","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45989588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"DROUGHT HAZARD ASSESSMENT USING ANOMALY DROUGHT INDEX AND GEOGRAPHIC INFORMATION SYSTEM IN THE CHI RIVER BASIN, THAILAND","authors":"Sarunphas Iamampai, Jirawat Kanasut, Banramee Kantawong, Prem Rangsiwanichpong","doi":"10.21163/gt_2023.181.04","DOIUrl":"https://doi.org/10.21163/gt_2023.181.04","url":null,"abstract":"","PeriodicalId":45100,"journal":{"name":"Geographia Technica","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49059038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"THERMAL REGIME OF THE NORTHWESTERN PART OF THE BLACK SEA","authors":"V. Vyshnevskyi, A. Matygin, V. Komorin","doi":"10.21163/gt_2023.181.03","DOIUrl":"https://doi.org/10.21163/gt_2023.181.03","url":null,"abstract":"","PeriodicalId":45100,"journal":{"name":"Geographia Technica","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41731536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"INVESTIGATION OF SOIL EROSION IN AGRO-TOURISM AREA: GUIDELINE FOR ENVIRONMENTAL CONSERVATION PLANNING","authors":"N. M. Trigunasih, Moh Saifulloh","doi":"10.21163/gt_2023.181.02","DOIUrl":"https://doi.org/10.21163/gt_2023.181.02","url":null,"abstract":"","PeriodicalId":45100,"journal":{"name":"Geographia Technica","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44216593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
: Floods are natural disasters that can damage lives, property, and the economy. Therefore, it is necessary to have a reliable and accurate flood forecasting system to provide early warning in time. Although several Mathematical models have been developed and used to forecast floods continuously for decades, most require up-to-date and specific physical data, including a high experience user, to provide and interpret the result. It is an obstacle for use in remote areas with incomplete information and a lack of specialists. This study, therefore, developed a real-time flood forecasting system with Machine Learning by applying a 2-variable sliding window technique to restructure the data, which can solve the problem of data limitation. Thung Song District Nakhon Si Thammarat Province was selected to test this newly developed model. By importing the water level data of two water level observed stations, SWR025 at the upstream and NKO001 at Thung Song Municipality, into five machine learning algorithms (Linear Regression, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Random Forest) for forecasting the water level every 30 minutes for the next 5 hours. Their performance was compared by the MSE, MAE, and R 2, which ranged from 0.006-0.013, 0.044-0.063, and 0.518-0.750, respectively. The Random Forest was the most efficient algorithm for the 3-hour forecast with an efficiency value of MSE 0.006, MAE 0.044, and R 2 0.75. The developed ML flood forecasting model was validated by the flood data in November 2021 and showed good agreement. Then, the extent of the inundation area was evaluated by the mathematical model. Next, the water depth and surface elevation were transformed and applied to GIS. Finally, the flood risk areas on Google Maps under that specific rainfall are promptly notified to the people three hours before the flood occurs.
{"title":"DEVELOPING A FLOOD FORECASTING SYSTEM WITH MACHINE LEARNING AND APPLYING TO GEOGRAPHIC INFORMATION SYSTEM","authors":"Jirayu Pungching, Sitang Pilailar","doi":"10.21163/gt_2023.181.01","DOIUrl":"https://doi.org/10.21163/gt_2023.181.01","url":null,"abstract":": Floods are natural disasters that can damage lives, property, and the economy. Therefore, it is necessary to have a reliable and accurate flood forecasting system to provide early warning in time. Although several Mathematical models have been developed and used to forecast floods continuously for decades, most require up-to-date and specific physical data, including a high experience user, to provide and interpret the result. It is an obstacle for use in remote areas with incomplete information and a lack of specialists. This study, therefore, developed a real-time flood forecasting system with Machine Learning by applying a 2-variable sliding window technique to restructure the data, which can solve the problem of data limitation. Thung Song District Nakhon Si Thammarat Province was selected to test this newly developed model. By importing the water level data of two water level observed stations, SWR025 at the upstream and NKO001 at Thung Song Municipality, into five machine learning algorithms (Linear Regression, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Random Forest) for forecasting the water level every 30 minutes for the next 5 hours. Their performance was compared by the MSE, MAE, and R 2, which ranged from 0.006-0.013, 0.044-0.063, and 0.518-0.750, respectively. The Random Forest was the most efficient algorithm for the 3-hour forecast with an efficiency value of MSE 0.006, MAE 0.044, and R 2 0.75. The developed ML flood forecasting model was validated by the flood data in November 2021 and showed good agreement. Then, the extent of the inundation area was evaluated by the mathematical model. Next, the water depth and surface elevation were transformed and applied to GIS. Finally, the flood risk areas on Google Maps under that specific rainfall are promptly notified to the people three hours before the flood occurs.","PeriodicalId":45100,"journal":{"name":"Geographia Technica","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49032599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"CATEGORIZING THE CAUSES OF OCCURRENCE OF CHATEAU BROWNFIELDS: A CASE STUDY ON THE CZECH REPUBLIC","authors":"Kamila Turečková","doi":"10.21163/gt_2022.172.18","DOIUrl":"https://doi.org/10.21163/gt_2022.172.18","url":null,"abstract":"","PeriodicalId":45100,"journal":{"name":"Geographia Technica","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42438849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"MAPPING OF SUBAK AREA BOUNDARIES AND SOIL FERTILITY FOR AGRICULTURAL LAND CONSERVATION","authors":"Ida Bagus Putu Bhayunagiri, Moh Saifulloh","doi":"10.21163/gt_2022.172.17","DOIUrl":"https://doi.org/10.21163/gt_2022.172.17","url":null,"abstract":"","PeriodicalId":45100,"journal":{"name":"Geographia Technica","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46442148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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