International Journal of Geoinformatics

Application of the HEC-RAS Model in Flood Modeling on the Code River Segment in Sewon District, Bantul Regency, Indonesia

2025-02-14T01:59:29-08:00

Research on flood hazard and risk mapping has been widely conducted and is crucial for flood disaster management and mitigation. These studies often involve flood modeling using HEC-RAS, which has been applied to major rivers in the Special Region of Yogyakarta Province. This modeling analyses the area and depth of inundation using parameters such as flood hydrographs, Manning’s coefficient, and Digital Elevation Model (DEM) data. This study aims to compare the discharge simulated by the GAMA I Synthetic Unit Hydrograph (HSS GAMA I) model with observed discharges using loggers and to evaluate flood modeling with HEC-RAS in Sewon District, Bantul Regency. The results of flood modeling predictions using two different discharge inputs are not too different in terms of discharge and inundation area. Peak discharge values using HSS Gama I are determined as 29.70 m³/s, 52.11 m³/s, and 64.20 m³/s for 2, 5, and 10-year return periods respectively, while values obtained using loggers are 26.16 m³/s, 41.53 m³/s, and 49.56 m³/s. It can be concluded that discharge data from the HSS GAMA I method can still be considered relevant to be used as an alternative method if discharge data is not obtained from direct measurements. Geometry data obtained from the results of aerial photography that is regressed with terrestrial data is also important in this study because it can adjust the basic data and the latest data into one good geometry data. At least the data produced can represent the geometry of the modeled river as closely as possible to the conditions in the field and the results of this study can be a new reference for further research to be able to apply the model and adjust it to the character of each region, so that flood modeling using HEC-RAS becomes more innovative and adaptable to the modeled area.

Industrial Activities Impacts on the Settlement Environmental Quality: A Case Study of Kwadungan Village, Kediri Regency, Indonesia

2025-02-14T02:05:43-08:00

Around the world, there has been tremendous industrial development, greatly aided by a variety of industries, including manufacturing, services, and infrastructure. Globalization, technological advances, and government initiatives to combat poverty and promote economic development are some of the causes driving this increase. Industrial activities have an impact on various aspects of the settlement environmental quality. Kwadungan Village is in the industrial area of Kediri Regency. Its location in the industrial area makes the settlements directly exposed to industrial activities. Therefore, a study on the environmental quality of settlements that includes an assessment of the industrial activity impacts is needed to formulate regional settlements policies. This study aims to (1) assess the settlement environmental quality, (2) analyze the effect of industrial activities on settlement environmental quality. Samples were selected using stratified random sampling method and the samples size was 306 samples. Data processing used scoring techniques from the questionnaire data. While the effect of industrial activity on the settlement environmental quality is known from the ordinal logistic regression test with a 5% error rate. The results showed that the environmental quality of settlements in Kwadungan Village was dominated by the moderate settlement environmental quality class, which is 35%. Then, the good class is 33% and the bad class is 32%. In addition, industrial activities strongly influence by 52.4% on settlement environmental quality in Kwadungan Village.

Mapping of Nearshore Bathymetry Based on Random Forest Machine Learning in Kemujan Island Waters

2025-02-14T02:09:16-08:00

The availability of shallow water bathymetry data is essential for support shipping safety, marine spatial planning, and conservation. Conventional bathymetric data acquisition generally requires complex instruments and is expensive. Remote sensing through multispectral image data allows the application of satellite-derived bathymetry (SDB) to obtain shallow water bathymetry data quickly and efficiently. Through this study, we tested the influence of seasonal variations across Indonesian waters: Northwest Monsoon (NWM), TS1 (Transition Season 1), Southeast Monsoon (SEM), and TS2 (Transition Season 2) on the accuracy produced by SDB through an empirical approach using Random Forest (RF) algorithm. The RF algorithm is applied to multispectral multiscenario images, which scale to surface reflectance (SR), deglint, and band ratio. The main variable is input depth data, which automatically divided into training and testing. The entire process is carried out on a cloud computing device called Google Earth Engine (GEE), thereby reducing processing residue and significantly saving processing time. We use two approaches to assessing accuracy: quantitatively using R², MSE, and RMSE, and qualitatively descriptive using a 1:1 plot approach and underwater topographic profile via transect, which compares reference data with model depth. Overall, the resulting RMSE range is 0.86–1.7 m; for MSE, it is 0.84–2.9 m; and R² is in the range of 0.63–0.86 m. This study found that seasonal variations have a systematic effect on accuracy, where NWM produces the lowest accuracy, which is thought to be due to atmospheric factors, and SEM TS2, respectively, produces the best accuracy. Through depth distribution, the SDB model is able to show maximum performance up to a total depth of 4 m. The underwater topographic profile shows that the overall scenario can replicate the depth well. This study provides comprehensive insight into the influence of seasonal variations on the accuracy of shallow-water bathymetric mapping.

Spatial Machine Learning Algorithms to Discover Prospective Oil and Gas Wells Locations Based on Surface Driving Factors

2025-02-14T02:34:43-08:00

Indonesia faces challenges in transitioning its energy sector, aiming to shift from coal to natural gas, achieve net zero emissions with renewable energy, and overcome geographical complexity obstacles, diverse cultural perspectives, and a developing regulatory framework. To address these issues, the government actively studies the Grand National Energy Strategy in enhancing petroleum and fuel refineries. This research aims to expand the academic approach by utilizing spatial technology and machine learning to optimize the new oil and gas well placement determination and meet the high-demand resources. Four algorithms, support vector machine (SVM), random forest (RF), artificial neural network (ANN), and k-nearest neighbor (KNN), with four training and testing splitting scenarios (80:20, 75:25, 60:40, and 50:50) are used to produce probability map of the wells site suitability along with fourteen surface driving factors related to the environmental agreement. The outcome indicates that the 80:20 RF model demonstrated excellence, achieving a 0.95 accuracy, 1.00 sensitivity, 0.90 specificity and Cohen’s Kappa, 0.91 precision, and 0.99 area under the curve, showcasing the optimal fit with validation data. The four surface driving factors with the highest important index indicate that the well placement is sensitive to historical disaster, ease of accessibility, and hydrocarbon sourcing.

Spatial Modeling of Tidal Flood Hazard and Mitigation Efforts in the Coastal Area of Bandar Lampung City, Indonesia

2025-02-14T02:38:38-08:00

Tidal flooding represents a natural phenomenon that presents a significant risk to coastal regions in Indonesia. It occurs when sea tides inundate an area with a topography that is below sea level, such as Bandar Lampung City's coastal areas. In order to mitigate the adverse effects of tidal floods in these regions, it is essential to develop a comprehensive model of the tidal flood hazard, coupled with effective mitigation strategies. In order to evaluate the potential for tidal flooding, inundation modelling was conducted using the minimum height of tidal flooding (10 cm), the value of the low tide level (43 cm), and the highest tide during full moon conditions (160 cm) with the Digital Elevation Model (DEM). The ArcGIS software was employed to create a model of inundation through the use of the raster calculator tool, resulting in the generation of a map that delineates the extent of tidal flood hazard levels and the areas that would be affected. The modeling analysis indicates that the tidal flood hazard affects an area of 172.95 hectares. The land uses most susceptible to impact are shrubs and settlements. Panjang Sub-district experienced the most extensive inundation area due to its residential, industrial, trade, and port activities in the 160 cm inundation scenario. In order to mitigate the risk of tidal flooding, various measures can be implemented, such as raising house floors, constructing multi-storey houses, implementing residential conservation, and developing mitigation plans through spatial planning and control strategies. The level of tidal flood hazard, existing adaptation and mitigation measures, and characteristics of tidal flooding can be used as a reference in the formulation of coastal management strategies to mitigate the impact of tidal flooding in coastal regions.

Landslide Susceptibility Mapping Utilizing the Weighted Frequency Ratio Technique: A Case Study of Klang Valley, Malaysia

2025-02-14T02:42:02-08:00

The escalating impacts of climate change have intensified slope instability and increased landslide occurrences in the rapidly urbanizing Klang Valley, Malaysia. With more intense rainfall and rising temperatures, the region faces unprecedented challenges to soil and slope stability due to rapid urbanization. This study evaluates landslide susceptibility by analyzing rainfall and temperature as primary triggering factors, alongside parameters such as elevation, slope angle, aspect, curvature, lithology, land use, soil properties, and NDVI. Key findings highlight that land use, particularly in commercial, industrial, and infrastructure areas with high FR (9.44) and LSI (2.627), significantly influences landslide susceptibility due to construction and terrain alterations. Steep slopes are especially vulnerable as they accelerate runoff, while areas with low NDVI, indicative of sparse vegetation, are more prone to slope failures due to the stabilizing role of vegetation. Regions characterized by vein quartz (FR=6.31; LSI=0.801), known for its brittle structure, and mined lands disturbed by human activities, also exhibit heightened geological vulnerabilities. Utilizing bivariate regression and the Weighted Frequency Ratio (WFR) method in ArcGIS, the study integrates high-resolution LiDAR and digital terrain models (DTMs) to develop a detailed and accurate landslide susceptibility map. These findings offer critical insights for disaster risk reduction strategies and climate-resilient urban planning in the Klang Valley, aligning with United Nations Sustainable Development Goal (SDG) 13.

Comparison of Detailed Land Cover Mapping Based on SNI Classification Scheme using Conventional and Machine Learning Multispectral Classification

2025-02-14T02:46:37-08:00

Accurate and efficient land cover mapping remains a critical challenge in remote sensing, particularly when adhering to standardized classification schemes. In Indonesia, the Standar Nasional Indonesia (SNI) 7645-1:2014 classification scheme serves as the national standard for land cover mapping but is inherently complex, having been designed for visual interpretation, rather a digital classification approach. This study attempted to applied the SNI classification scheme for digital image classification by comparing the result of two commonly used algorithm in digital image classification, namely Maximum Likelihood (ML) and Random Decision Forest (RDF). Based on preliminary land cover map and fieldwork conducted, we successfully identified 18 land cover classes based on SNI classification scheme with the scale of 1:250.000. Because single date imagery and spectral information were only used, modification to the classification is conducted by grouping the classes into a new class based on its basis land cover type. Due to various spectral response of classes, the imagery is classified into 29 classes which later will be combine in the post processing process. Aside from the percent correct, the quantity disagreement and allocation disagreement were also used for assessing the result of both algorithms. Accuracy assessment of the land cover maps shows 81.17% overall accuracy for ML algorithm and 82.92% for the RDF algorithm. The ML algorithm shows higher allocation disagreement, compared to RDF algorithm, indicating higher miss-classification were detected in the ML algorithm. On the other hand, RDF algorithm shows higher quantity disagreement, indicating that this algorithm overestimated the land cover produced. This approach served as an alternative approach for mapping land cover using the SNI classification scheme, aside from the established approached.

Ethnic Disparities and Demographic Shifts in Sarawak's Aging Population: A Comprehensive Longitudinal Analysis (1980-2020)

2025-02-14T02:48:54-08:00

The aging population constitutes a pivotal demographic shift with substantial implications across social, economic, and healthcare spheres, notably in ethnically diverse territories such as Sarawak, Malaysia. This investigation delivers an exhaustive longitudinal examination of the distribution and demographic trends of the elderly among various ethnic communities in Sarawak from 1980 to 2020. Despite abundant scholarship on population aging, the confluence of ethnicity and aging has received scant attention, particularly within the Malaysian milieu. This research endeavors to bridge this gap by exploring the following question: How have the proportions of senior citizens within Sarawak's diverse ethnic groups evolved from 1980 to 2020? Employing secondary data sourced from the Department of Statistics Malaysia, this study utilises descriptive analysis, linear regression models, and Geographic Information System (GIS) techniques to delineate and expound these trends. The analysis uncovers pronounced disparities in aging trajectories among the ethnic groups. Notably, the Chinese community exhibits the most pronounced and consistent increase in the elderly population, while the Iban and other indigenous cohorts show a decline, possibly attributable to migration and diminished fertility rates. Conversely, the burgeoning proportion of elderly Malays indicates enhancements in health and socio-economic statuses, contrasting with the steady figures observed within the Chinese and Indian communities, which likely reflect enduring cultural traditions such as filial piety. These findings underscore the imperative for culturally attuned policies and interventions tailored to the distinct needs of each ethnic group as Sarawak's demographic landscape ages. This study underscores the importance of recognizing ethnic-specific aging patterns for efficacious public health and social service strategy formulation in a culturally heterogeneous region like Sarawak. By integrating insights into the spatial dynamics of aging, anchored by Tobler's First Law of Geography, this research contributes novel perspectives to the body of knowledge and supports the creation of more effective and inclusive policies.

Spatio-Temporal Characteristics of UHI in Coastal Urban Area and its Impact on Land Unit: A Case Study in the Southeast Sulawesi Province, Indonesia

2025-02-14T02:54:02-08:00

The dynamics of urban heat island (UHI) in urban thermal environments will be more complex and crucial along with the region rapid growth, coupled with increasingly an unforeseen global temperature condition. This study aims to observe the characteristics of UHI and its effects on land units in coastal cities in Southeast Sulawesi province from 2019 to 2023. Landsat 8/9 imagery is used for Land Surface Temperature (LST) retrieval. Urban Landscape Composition (ULC) is developed based on the spectral index method through VrNIR-BI, NDVI and MNDWI. Land units are developed using overlay techniques from four input data including ULC, terrain slope, soil type and geological formation. The results of the study reveal that LST variations significantly affect UHI formation throughout the study area. Surface temperatures in both regions continued to increase throughout 2021-2023, is getting warmer in 2023 between August and November. Meanwhile, the ULC conditions in both regions experienced major transformations, especially in the areas of impervious surface and green space. Nevertheless, over 70% of the total area in both regions is covered by vegetation. Spatially, the UHI changes in Kendari city tends to moved towards the western region and are more massive to the southern region. Meanwhile, the UHI area in Baubau city is still relatively in the downtown area, but several areas in the northern region of Baubau are beginning to show substantial UHI concentration. Global warming that occurred in 2023 further worsened the UHI in both regions. The UHI area is typically affect more land units categorized as impervious surface with relatively flat terrain slopes and hapludalfs soil types. This study emphasizes the importance of studying the dynamics of microclimate in urban thermal environments in the disaster mitigation efforts, providing valuable insights for regional development planning strategies to create environmentally friendly, resilient and sustainable cities