One hundred seventeen landslides occurred in Malang Regency throughout 2021, triggering the need for practical hazard assessments to strengthen the disaster mitigation process. In terms of providing a solution for investigating the location of landslides more precisely, this research aims to compare machine learning algorithms to produce an accurate landslide susceptibility model. This research applies three machine learning algorithms composed of RF (random forest), NB (naïve Bayes), and KNN (k-nearest neighbor) and 12 conditioning factors. The conditioning factors consist of slope, elevation, aspect, NDVI, geological type, soil type, distance from the fault, distance from the river, river density, TWI, land cover, and annual rainfall. This research performs seven models over three ratios between the training and testing dataset encompassing 50:50, 60:40, and 70:30 for KNN and NB algorithms and 70:30 for the RF algorithm. This research measures the performance of each model using eight parameters (ROC, AUC, ACC, SN, SP, BA, GM, CK, and MCC). The results indicate that RF 70:30 generates the best performance, witnessed by the evaluation parameters ACC (0.884), SN (0.765), GM (0.863), BA (0.857), CK (0.749), MCC (0.876), and AUC (0.943). Overall, seven models have reasonably good accuracy, ranging between 0.806 and 0.884. Furthermore, based on the best model, the study area is dominated by high susceptibility with an area coverage of 51%, which occurs in the areas with high slopes. This research is expected to improve the quality of landslide susceptibility maps in the study area as a foundation for mitigation planning. Furthermore, it can provide recommendations for further research in splitting ratio scenarios between training and testing data.

According to the Indonesian Disaster Management Agency (BNPB), Indonesia’s losses due to landslides were estimated around hundreds of billion rupiah in 2017. Making landslide as one of the catastrophes with the greatest risk of loss and leaving a couple regions prone to landslides in Indonesia, Pacitan region is one of them. Landslide delineation therefore represents a particularly beneficial application of evolving research trend in disaster reduction, especially for the vulnerable region. In the present times of open-access satellite data, cloud computing and machine-learning algorithms is frequently used for disaster prevention monitoring. By employing Google Earth Engine, this study focuses on the susceptibility of landslide occurrence using a random forest machine-learning framework applied to digital topographic data such as elevation, slope and aspect as the independent variables and landslide inventory data obtained from Ministry of Energy and Mineral Resources Republic of Indonesia as the dependent variable. This study data sets composed from 1000 random points in Pacitan region with 70:30 ratio for training and testing sample points. The model produced good result, with overall accuracy values of 0.94, kappa values of 0.79 and 0.80 for AUC value. This model also showed that elevation is the most important variable in the landslide susceptible area. The results of this study can be used to evaluate the potential future impacts of landslide and help to optimize the management of disaster reduction in the region of Pacitan.

This paper proposes to identify the variation of accessibility to healthcare facilities based on vulnerability assessments of floods by using open source data. The open source data comprises Open Street Map (OSM), world population, and statistical data. The accessibility analysis is more focused on vulnerable populations that might be affected by floods. Therefore, a vulnerability assessment is conducted beforehand to identify the location where the vulnerable population is located. A before and after scenario of floods is applied to evaluate the changes of healthcare accessibility. A GIS Network Analyst is chosen as the accessibility analysis tool. The results indicate that the most vulnerable population lives in the Asemrowo district. The service area analysis showed that 94% of the West of Surabaya was well-serviced in the before scenario. Otherwise, the decrement of service area occurs at the city center in the after scenario. Thus, the disaster manager can understand which vulnerable area is to be more prioritized in the evacuation process.

Several techniques have been proposed to observe long-term land surface deformation using phase information of synthetic aperture radar (SAR), for example, InSAR, DInSAR, PS-InSAR, SBAS using images of JERS-1, ALOS-1/2, ENVISAT, ERS-1/2, RADARSAT-1/2, TerraSAR-X, SENTINEL-1A/B, etc. These methods have been also employed to investigate land deformation and the impact caused by the hot mudflow accident at the study area of Regency of Sidoarjo, East Java province, Indonesia, where this disaster happened on 29 May 2006 and has been flowing until now. But these methods only derived the land surface deformation and did not figure geological situation in the Earth, especially the current of geological material outflow. Therefore, this research proposed the continuity equation of the law of conservation of material to estimate the current of material outflow to investigate the impact of the land surface disaster in the study area. The information of vertical deformation or subsidence is derived by the consecutive DInSAR using multi-spaceborne SAR and substituted into the proposed equation to estimate the volume and current of hot mud outflow. The differential GPS data collected since 2006 was employed to validate the analysis result of land surface deformation, and the RMS error is 0.46 m. The result obtained the current at the center of the study area at the beginning of the mudflow period was 6,800 m3/day and decreased from 2018 until now that matched well with the local reports. The proposed equation also could be applied for observation of land deformation, volcano, fault activity, underground water, etc using remote sensing technologies.

Surabaya City is the second-largest city based on the population in Indonesia that is affected by the development of urban planning in the city. The development of the urban areas affected the increase of socio-economic development and peri-urban demographics but often took over land or green open spaces. It is an unavoidable possibility of air pollution due to the imbalance between oxygen producers and existing oxygen users. It is necessary to calculate the prediction of green open space requirements using the Gerarkis Method, which predicts green open spaces based on oxygen demand by using the number of residents, motor vehicles, livestock, industrial factories, and hotels. With the Gerarkis Method, the prediction of the need for green open space in Surabaya City for 2010–2020 is 22,088.89 hectares to 31,950.84 with the increased majority value in the area, except in 2015 and 2020, due to decreased users. This method proves that the need for oxygen produced from plants is increasing, making Surabaya City needs more green open space to make Surabaya City livable.

Several techniques have proposed to observe long-term land deformation using phase information of synthetic aperture radar (SAR), for example, InSAR, DInSAR, PS-InSAR, SBAS using spaceborne SAR images. But every mission of spaceborne SAR has an orbit duty period less than ten years and other problems of discontinuity or blank period of observation using the similar specification of sensors. Hence land deformation with an observed time of more than ten years is not available to be monitored continuously using a single mission. This research proposed a method called Bridging Consecutive DInSAR (BC-DInSAR) to connect Consecutive DInSAR. These methods were employed to investigate land deformation and the impact caused by hot mudflow accident at Regency of Sidoarjo, Indonesia, where this disaster happened on 29 May 2006 and is flowing until now. The differential GPS data since 2006 was employed to validate the analysis result of BC-DInSAR, which obtained 0.46 m RMS error.