Areas at risk of land subsidence in Jakarta can be identified using a land subsidence susceptibility map. This study evaluates the quality of a susceptibility map made using functional (logistic regression and multilayer perceptron) and meta-ensemble (AdaBoost and LogitBoost) machine learning algorithms based on a land subsidence inventory map generated using the Sentinel-1 synthetic aperture radar (SAR) dataset from 2017 to 2020. The land subsidence locations were assessed using the time-series interferometry synthetic aperture radar (InSAR) method based on the Stanford Method for Persistent Scatterers (StaMPS) algorithm. The mean vertical deformation maps from ascending and descending tracks were compared and showed a good correlation between displacement patterns. Persistent scatterer points with mean vertical deformation value were randomly divided into two datasets: 50% for training the susceptibility model and 50% for validating the model in terms of accuracy and reliability. Additionally, 14 land subsidence conditioning factors correlated with subsidence occurrence were used to generate land subsidence susceptibility maps from the four algorithms. The receiver operating characteristic (ROC) curve analysis showed that the AdaBoost algorithm has higher subsidence susceptibility prediction accuracy (81.1%) than the multilayer perceptron (80%), logistic regression (79.4%), and LogitBoost (79.1%) algorithms. The land subsidence susceptibility map can be used to mitigate disasters caused by land subsidence in Jakarta, and our method can be applied to other study areas.

Hakim, W.L.; Achmad, A.R.; Eom, J., and Lee, C.-W., 2020. Land subsidence measurement in Jakarta coastal area using time series interferometry with Sentinel-1 SAR data. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 75-81. Coconut Creek (Florida), ISSN 0749-0208.Jakarta coastal area is a part of North Jakarta which has a boundary to the Java Sea. Coastal flood from the Java Sea was not a new case in North Jakarta, the situations occurred due to sea-level rise. The conditions of coastal flood in Jakarta can be worsening by land subsidence in Jakarta coastal area. The land subsidence in Jakarta coastal area was occurred in many years and detected by geodetic measurement methods such as GPS and leveling surveys. The study about land subsidence in Jakarta coastal area needed to be updated because it could affect the urban development. This study presents the mean vertical deformation map using Stanford Methods for Persistent Scatterer (StaMPS) for time-series analysis on the Sentinel-1 data archives in the period of March 2017 through April 2020 in both ascending and descending track. The comparison of mean vertical deformation map between two tracks shows a good correlation in terms of displacement patterns with slightly different in 4 individual chosen points that show a high deformation at rates between 30 mm/year and 40 mm/year. The area chosen in this study known as a reclaimed area and the subsidence occurred due to young alluvium consolidation that not supported the maximum compression from a lot of buildings. These approach methods in this study that using StaMPS could be used to monitor land subsidence from all-terrain.

Floods in Pekalongan, Indonesia, often occur due to river water overflowing during heavy monsoon rain. Simultaneously, the northern coastal area of Pekalongan, located adjacent to the Java Sea, has been affected by coastal floods due to sea level rise. The flood conditions in this area were exacerbated by land subsidence, leading to coastal inundation. Monitoring land subsidence in Pekalongan has become essential in predicting other possible land subsidence occurrence areas and mitigating the possible hazards caused by land subsidence. The analysis of land subsidence has been much easier since the introduction of radar satellites. In this study, 124 synthetic aperture radar (SAR) datasets from the Sentinel-1 radar satellite between 2017 and 2022 in descending tracks were used. The data were processed through a time-series interferometry SAR (InSAR) method based on the improved combined scatterers interferometry with optimized point scatterers (ICOPS) algorithm to provide accurate measurements over large areas by improving the selection of measurement points from persistent scatterer (PS) and distributed scatterer (DS) points using a deep learning algorithm based on a convolutional neural network (CNN), and the resulting optimized measurement points were then spatially clustered using optimized hot spot analysis (OHSA) to estimate significant points statistically and define them as hot spot points. The results of time-series deformation in Pekalongan were compared with the GPS station measurements. From the comparison, a good correlation in terms of deformation patterns between time-series InSAR and GPS measurements was observed. Our study revealed that land subsidence in Pekalongan has occurred mostly in settlement areas under the young alluvium soil, which cannot support many buildings’ maximum compression. Another cause of land subsidence in Pekalongan is excessive groundwater extraction in settlement areas. Thus, compaction in the aquifer areas may occur as a result of the reduced effective stress of the pore pressure. Further analysis of this study would involve monitoring groundwater activity using data from the GRACE satellite and comparing them with weather station data. The analysis of the two datasets aims to understand the relationships between groundwater storage data and the monthly precipitation in Pekalongan. Finally, the potential outcomes of land subsidence in Pekalongan will be assessed using the geographic information system (GIS) method based on susceptibility mapping.

Turbidity has an indication that the liquid has been contaminated. In the testing process, turbidity in water can only be measured by sampling. To be able to maintain the quality of water, required a tool that can monitor and measure the level of turbidity of water in real time. water turbidity sensor SEN0189 is a sensor that works by measuring the amount of light from infrared led into the phototransistor that will produce the output voltage on the sensor. The study was conducted to be able to characterize the ability of sensors in detecting water turbidity. The method used is to test the sensors using sediment soil that has been filtered with a diameter of <60μm to be added into the pond containing 1 liter of water. The results show that the greater the concentration of sediment dissolved in the water pool the sensor output voltage will be smaller. The sensor has a sensitivity of -0.0008 and the output voltage when the sensor detects 0 NTU is 3.9994 volts with 5V operating voltage and the sensor can detect water turbidity linearly within the test range 1.873 NTU to 1011.93 NTU.