Geographia Technica最新文献_第4页

INVESTIGATION OF SOIL EROSION IN AGRO-TOURISM AREA: GUIDELINE FOR ENVIRONMENTAL CONSERVATION PLANNING 农牧交错带土壤侵蚀调查&环境保护规划指南

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Geographia Technica Pub Date : 2022-11-03 DOI: 10.21163/gt_2023.181.02

N. M. Trigunasih, Moh Saifulloh

引用次数: 3

DEVELOPING A FLOOD FORECASTING SYSTEM WITH MACHINE LEARNING AND APPLYING TO GEOGRAPHIC INFORMATION SYSTEM 基于机器学习的洪水预报系统开发及其在地理信息系统中的应用

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Geographia Technica Pub Date : 2022-10-27 DOI: 10.21163/gt_2023.181.01

Jirayu Pungching, Sitang Pilailar

{"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}

引用次数: 0

MAPPING OF SUBAK AREA BOUNDARIES AND SOIL FERTILITY FOR AGRICULTURAL LAND CONSERVATION 苏巴克区边界测绘与土壤肥力研究

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Geographia Technica Pub Date : 2022-10-18 DOI: 10.21163/gt_2022.172.17

Ida Bagus Putu Bhayunagiri, Moh Saifulloh

引用次数: 0

NIGHTTIME AND DAYTIME POPULATION ESTIMATION FROM OPEN DATA 从公开数据估计夜间和白天的人口

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Geographia Technica Pub Date : 2022-10-17 DOI: 10.21163/gt_2022.172.15

Nelson Mileu, M. Queirós

引用次数: 0

ALGORITHMS DEVELOPMENT OF THE FIELD MANGROVE CHLOROPHYLL-a BIOMASS, CARBON BASED ON SENTINEL-2A DATA AT CAWAN ISLAND, SUMATERA, INDONESIA 基于SENTINEL-2A数据的印尼苏门答腊加万岛红树林叶绿素-a生物量、碳计算算法的发展

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Geographia Technica Pub Date : 2022-10-06 DOI: 10.21163/gt_2022.172.11

Agus Hartoko, Aulia Rahim, N. Latifah

{"title":"ALGORITHMS DEVELOPMENT OF THE FIELD MANGROVE CHLOROPHYLL-a BIOMASS, CARBON BASED ON SENTINEL-2A DATA AT CAWAN ISLAND, SUMATERA, INDONESIA","authors":"Agus Hartoko, Aulia Rahim, N. Latifah","doi":"10.21163/gt_2022.172.11","DOIUrl":"https://doi.org/10.21163/gt_2022.172.11","url":null,"abstract":": The study develop of algorithms for the tropical mangrove chlorophyll-a, biomass and carbon based on the field data measurements at Cawan Island Sumatera Indonesia and Sentinel-2A satellite data. Samples of mangrove leaf were used for chlorophylla-a measurements using spectrometry method. Field sampling data using purposive sampling method. Data of mangrove tree diameter at breast height (DBH) was processed using allometric equation to estimate the mangrove biomass and carbon content. Algorithms were developed after performing a series of polynomial regressions of field and Sentinel-2A satellite data and then select the highest correlation coefficient. The dominant mangrove is Rhizophora apiculata . The field mangrove leaf chlorophyll-a content ranged from 14.03-15.77 mg.ml - 3 , while the estimated chlorophyll-a from algorithm is in the range of 13.714-16 mg.ml -3 . Calculated field mangrove biomass is in the range of 66.31-85.05 tons.ha -1 , while the value from algorithms is in the range of 51-90 tons.ha -1 . The highest biomass and carbon storage is in the trunks. This study produces the algorithm of mangrove leaf chlorophylll-a = 0.0002((B 4 + B 2 )/2) 2 – 0.057((B 4 + B 2 )/2) + 16.79, with RMSE of 0.072 mg.m -3 . Algorithm for mangrove biomass = 24.69(B 4 /Band 2 ) 2 - 47.41(B 4 /B 2 ) + 36.06, with RMSE of 0.337 tons/0.2ha and algorithm for mangrove carbon = 10.071(B 4 /B 2 ) 2 – 23.159(B 4 /B 2 ) + 44.233; with RMSE of 0.235 tonsC/0.2ha. The new insight in this study is that the algorithm developments can be applied for mangrove chlorophyll-a content, biomass and carbon content estimation using any optical satellite data based on its relevant spectral range. This algorithm development is an open approach method based on highest correlation coefficient on regression equation of the field and the satellite spectral value. The algorithms resulted from this study can be applied over wide and in any area in the tropics.","PeriodicalId":45100,"journal":{"name":"Geographia Technica","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45349147","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}

引用次数: 0

TIDAL FLOOD MODEL PROJECTION USING LAND SUBSIDENCE PARAMETER IN PONTIANAK, INDONESIA 基于地面沉降参数的印尼蓬蒂亚纳克潮汐洪水模型投影

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Geographia Technica Pub Date : 2022-10-06 DOI: 10.21163/gt_2022.172.12

Randy Ardianto, A. Ismanto, J. Sampurno, S. Widada

引用次数: 0

JOINT DISTRIBUTION AND COINCIDENCE PROBABILITY OF THE NUMBER OF DRY DAYS AND THE TOTAL AMOUNT OF PRECIPITATION IN SOUTHERN SUMATRA FIRE-PRONE AREA 苏门答腊岛南部火灾易发区干旱日数与降水总量的联合分布及其重合概率

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Geographia Technica Pub Date : 2022-09-30 DOI: 10.21163/gt_2022.172.10

S. Nurdiati, M. Najib, Achmad Syarief Thalib

{"title":"JOINT DISTRIBUTION AND COINCIDENCE PROBABILITY OF THE NUMBER OF DRY DAYS AND THE TOTAL AMOUNT OF PRECIPITATION IN SOUTHERN SUMATRA FIRE-PRONE AREA","authors":"S. Nurdiati, M. Najib, Achmad Syarief Thalib","doi":"10.21163/gt_2022.172.10","DOIUrl":"https://doi.org/10.21163/gt_2022.172.10","url":null,"abstract":": El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) can affect the increase in rainfall intensity and the number of dry days, also known as dry spells that can cause drought and increase the potential for forest fires. This study examines the effect of ENSO and IOD conditions on the joint distribution of the number of dry days and total precipitation in a fire-prone area in southern Sumatra, Indonesia. The joint distribution is constructed using rotated copulas from several families, including Gaussian, student’s t, Clayton, Gumbel, Frank, Joe, Galambos, BB1, BB6, BB7, and BB8. Fire-prone areas are defined using k-mean clustering, while the copula parameters are estimated using the inference of function for margins (IFM) method. Based on the peak of joint probability density functions (PDFs), ENSO and IOD conditions had a significant effect in the dry season but had no significant effect in the rainy season. The peak of joint PDFs is getting to the dry-dry conditions when the ENSO and IOD indexes increase in the dry season. However, based on coincidence probability, ENSO conditions still influence the joint distribution between the number of dry days and total precipitation during the rainy season but not with IOD conditions. The lower the ENSO index, the higher the probability of wet conditions co-occurring in the number of dry days and total precipitation. Meanwhile, ENSO and IOD conditions significantly affect the coincidence probability between the number of dry days and total precipitation. Moderate-Strong El Niño has the most considerable coincidence probability of 68.5%, followed by Positive IOD with 62.6%. The two conditions had similar effects on the joint distribution of the number of dry days and total precipitation. Moreover, the association between the number of dry days and the total precipitation was stronger in the dry season than in the rainy","PeriodicalId":45100,"journal":{"name":"Geographia Technica","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44225543","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}

引用次数: 0

HOW THE CLIMATE MIGRATES. CASE STUDY FOR FOUR LOCATIONS IN THE CARPATHIAN-BASIN 气候是如何迁移的。喀尔巴阡盆地四个地点的案例研究

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Geographia Technica Pub Date : 2022-09-08 DOI: 10.21163/gt_2022.172.09

Zsolt Magyari-Saska

引用次数: 0

GIS-BASED ASSESSMENT OF COASTAL VULNERABILITY IN THE JATABEK (JAKARTA, TANGERANG, AND BEKASI) REGION, INDONESIA 基于GIS的印尼雅加达、唐格朗和贝卡西地区海岸脆弱性评估

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Geographia Technica Pub Date : 2022-09-01 DOI: 10.21163/gt_2022.172.08

G. A. Rahmawan, R. Dhiauddin, U. J. Wisha, W. A. Gemilang, A. Syetiawan, W. Ambarwulan, A. Rahadiati

引用次数: 0

ACCURACY PERFORMANCE OF SATELLITE-DERIVED SEA SURFACE TEMPERATURE PRODUCTS FOR THE INDONESIAN SEAS 印度尼西亚海域卫星海面温度产品的精度性能