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.

The hot mudflow released by Lapindo Mud Volcano periodically requires a large storage space. It is resulting the change in the main function of Kali Porong which is the channel for mud to the river mouth. This causes changes in water quality at Kali Porong estuary. The purpose of this study was to monitor water quality at Kali Porong estuary using Sentinel 2 image data with Total Suspended Solid (TSS) and chlorophyll-a analysis. Cloud computing technology can process image data into useful information. One of the open source cloud computing platforms is Google Earth Engine (GEE). In this platform, there is a database for storing satellite image data, including Sentinel-2. In addition to storing remote sensing data, GEE can process images quickly using the Java scripting language. In this study, monitoring was carried out in February-June 2021. The results show the average value of chlorophyll-a each month from February to June was 2.78 μg/m3, 2.76 μg/m3, 2.74 μg/m3, 2.98 μg/m3, and 3.2 mg/l. The average monthly TSS values from February to June were 16.11 μg/m3, 15.91 μg/m3, 15.76 μg/m3, 17.45 μg/m3, and 19.86 μg/m3, respectively. The correlation test result for chlorophyll-a estimation is 0.654. In the other hand, the correlation test result for the estimated TSS is 0.652. The trophic status of the waters at Kali Porong estuary is in the eutrophic class or has been polluted. The results show a tendency for the area with polluted trophic status to increase from February to June.

Landslides are disasters that cause huge losses to both human life and infrastructure. Therefore, this research purpose of carrying out landslide susceptibility spatial modelling using a random forest (RF) algorithm. This research uses 12 landslide conditioning factors to generate a landslide susceptibility map, which comprises elevation, slope, aspect, soil type, geological type, distance to river, NDVI (Normalized Different Index), river density, TWI (Topographic Wetness Index), annual rainfall, and land use. Each model was evaluated by 9 parameters including ROC (Receiver Operator Characteristic)-AUC (Area Under Curve), accuracy (acc), sensitivity (sn), specificity (sp), balanced accuracy (ba), g-mean (gm), cohen’s kappa (CK), and Matthew’s correlation coefficient (MCC). A total of 88 landslide locations were identified in Malang District using the regional disaster management authority of Malang District data. Of the 88 landslide inventories, 30% of the data were used for validation, and the remaining 70% were used for training purposes. The results show the ACC value of 0.884, 0.765 for SN, 0.962 for SP, 0.863 for GM, 0.857 for BA, 0.749 for CK, 0.876 for MCC, and 0.943 for AUC. From the entire landslide conditioning factors, the elevation parameter has the highest relative contribution level value, which is 100%. Moreover, the susceptibility map indicates that Malang District is dominated by a high susceptibility with an area of 177,208.83 ha (51% of the coverage area). 13 sub-districts that are dominated by high susceptibility levels area, including Ngantang, Kasembon, Apelgading, Pujon, Tirtoyudo, Poncokusumo, Sumbermanjing, Jabung, Dampit, Wonosari, Wagir, Dau and Gedangan sub-districts.