Ratna Sari Dewi

Land suitability assessment is an important stage in land use planning that guides the direction of optimal land use. The objective of this study was to select a suitable location for settlements in earthquake-prone areas using the integration of the Analytical Hierarchy Process (AHP) and Geographical Information System (GIS). In total, six maps were considered to determine a suitable location for settlements, namely topography, soil, geology, land cover/land use, a regional spatial planning pattern map, and an earthquake vulnerability map. The results showed that in medium earthquake-prone areas, the suitable land area which are available for settlement was 90.25 km2 (46.36% of the total land area available – 194.68 km2). Whereas in highly earthquake-prone areas, the suitable and available land area was 528.11 km2 (70.25% of the total land area in the high vulnerability zone – 751.81 km2). The research proved that AHP and GIS integration is very effective and robust for mapping land suitability in earthquake-prone areas. The results of the analysis can be used by planners to prioritize settlement development in the Sukabumi regency. The methodology developed is recommended to be applied in selecting locations for settlements in other parts of Indonesia.

The design of a foundation is inseparable from the type and strength of the soil, for example, soft or medium clay soils have poor properties for structures due to their low bearing capacity. Therefore, it is necessary to modify the model of a foundation to increase its bearing capacity and one of the popular methods is the application of folded plate on a flat foundation with angles and flange lengths on both sides. Therefore, this research presented its application at different flange lengths of 0.5B, 0.75B, 1B, 1.25B, and 1.5B where B is the width of the flat plate and the results showed the bearing capacity of the folded plate was higher than the flat foundation. Moreover, an increment in the folded length led to a bigger ultimate load and smaller settlement but the ultimate load and settlement were observed to be almost constant at over 1B. This means the optimum variation where the flange length is equal to the foundation width is 1B and it was observed to have increased by 129.52 % using the Tangent Method and 148.4 7% with Butler Hoy Method. Meanwhile, the lowest settlement factor for the folded plate foundation was 0.22 with the highest bearing capacity of 61.19 kN/m2.

Background: The outbreak of the novel coronavirus (COVID-19) has rapidly spread, causing million confirmed cases, thousands of deaths, and economic losses. The number of cases of COVID-19 in Jakarta is the largest in Indonesia. Furthermore, Jakarta is the capital city of Indonesia which has the densest population in the country. There is need for geospatial analysis to evaluate the demand in contrast to the capacity of Referral Hospitals and to model the spreading case of Covid-19 in order to support and organize an effective health service. Methods: We used the data from local government publicity for COVID-19 as trusted available sources. By using the verifiable data by observation from the local government, we estimated the spatial pattern of distribution of cases to estimate the growing cases. We performed service area and Origin-Destination (OD) Cost Matrix in support to existing referral hospital, and to create Standard Deviational Ellipse (SDE) model to determine the spatial distribution of COVID-19. Results: We identified more than 12.4 million people (86.7%) based on distance-based service area, live in the well served area of the referral hospital. A total 2637 positive-infected cases were identified and highly concentrated in West Jakarta (1096 cases). The results of OD cost matrix in a range of 10 km show a total 908 unassigned cases from 24 patient’s centroid which was highly concentrated in West Jakarta. Conclusions: Our results indicate the needs for additional referral hospitals specializing in the treatment of COVID-19 and spatial illustration map of the growth of COVID-19′ case in support to the implementation of social distancing in Jakarta.

The drivers for coastal flooding may vary from extremely high intensity and persistent rainfall, morphological factors of the coastal area, to extreme waves from the ocean. This means that the flood vulnerability of a coastal area does not solely depend on a single driver but can be a combination with others. A national standard for coastal flooding based on rainfall drivers has been developed. As an evaluation, this study aimed to develop a method for coastal flood-prone mapping by combining rainfall with tidal waves. The steps included the assessment of the coastal flood-prone areas driven by rainfall (CFR) and the coastal flood-prone areas by combined drivers (CFC), which was developed by employing the analytic hierarchy process (AHP), spatial-overlaid, weighted-scored, and logical tests. The coastal area of Mataram City on the Island of Lombok in Indonesia was selected as the study area, since it is frequently affected by flooding. The findings determined the essentiality of the CFC method for identifying flood vulnerability areas. Thus, the minimum standard for CFC parameters can be defined with climatic and land characteristic factors. Further, the findings also identified the need for expert judgment in the development of the CFC weighted score-based method.

Shoreline is defined as the intersection of land and water which provides economic and social security to coastal communities. Semarang is having approximately 20 km shorelines. It faces multiple threats as a result of for instance urban development, industrialization, land reclamation, land subsidence and erosion. In the present study, an attempt was made to monitor the accretion and erosion processes from multi-temporal images from 1988 up to 2017. Fuzzy approach and OTSU method were made to extract the shoreline and DSAS (Digital Shoreline Analysis System) was used to analyse the changes and estimated the shoreline change rate. From change detection results, a general trend of continuous changes of shoreline can be detected representing a vulnerable shoreline affected by human activities. The results of this study can be used to prioritize action and to develop a suitable adaptation measure.

This paper addresses uncertainty modelling of shorelines by comparing fuzzy sets and random sets. Both methods quantify extensional uncertainty of shorelines extracted from remote sensing images. Two datasets were tested: pan-sharpened Pleiades with four bands (Pleiades) and pan-sharpened Pleiades stacked with elevation data as the fifth band (Pleiades + DTM). Both fuzzy sets and random sets model the spatial extent of shoreline including its uncertainty. Fuzzy sets represent shorelines as a margin determined by upper and lower thresholds and their uncertainty as confusion indices. They do not consider randomness. Random sets fit the mixed Gaussian model to the image histogram. It represents shorelines as a transition zone between water and non-water. Their extensional uncertainty is assessed by the covering function. The results show that fuzzy sets and random sets resulted in shorelines that were closely similar. Kappa (κ) values were slightly different and McNemar’s test showed high p-values indicating a similar accuracy. Inclusion of the DTM (digital terrain model) improved the classification results, especially for roofs, inundated houses and inundated land. The shoreline model using Pleiades + DTM performed better than that of using Pleiades only, when using either fuzzy sets or random sets. It achieved κ values above 80%.

Mapping of shorelines and monitoring of their changes is challenging due to the large variation in shoreline position related to seasonal and tidal patterns. This study focused on a flood-prone area in the north of Java. We show the possibility of using fuzzy-crisp objects to derive shoreline positions as the transition zone between the classes water and non-water. Fuzzy c-means classification (FCM) was used to estimate the membership of pixels to these classes. A transition zone between the classes represents the shoreline, and its spatial extent was estimated using fuzzy-crisp objects. In change vector analysis (CVA) applied to water membership of successive shorelines, a change category was defined if the change magnitude between two years, T1 and T2, differed from zero, while zero magnitude corresponded to no-change category. Over several years, overall change magnitude and change directions of the shoreline allowed us to identify the trend of the fluctuating shoreline and the uncertainty distribution. The fuzzy error matrix (FERM) showed overall accuracies between 0.84 and 0.91. Multi-year patterns of water membership changes could indicate coastal processes such as: (a) high change direction and high change magnitude with a consistent positive direction probably corresponding to land subsidence and coastal inundation, while a consistent negative direction probably indicates a success in a shoreline protection scheme; (b) low change direction and high change magnitude indicating an abrupt change which may result from spring tides, extreme waves and winds; (c) high change direction and low change magnitude which could be due to cyclical tides and coastal processes; and (d) low change direction and low change magnitude probably indicating an undisturbed environment, such as changes in water turbidity or changes in soil moisture. The proposed method provided a way to analyze changes of shorelines as fuzzy objects and could be well-suited to apply to coastal areas around the globe.