The aims of this research were to map and analyze the risk of land subsidence in the Seoul Metropolitan Area, South Korea using satellite interferometric synthetic aperture radar (InSAR) time-series data, and three ensemble machine-learning models, Bagging, LogitBoost, and Multiclass Classifier. Of the types of infrastructure present in the Seoul Metropolitan Area, subway lines may be vulnerable to land subsidence. In this study, we analyzed Persistent Scatterer InSAR time-series data using the Stanford Method for Persistent Scatterers (StaMPS) algorithm to generate a deformation time-series map. Subsidence occurred at four locations, with a deformation rate that ranged from 6–12 mm/year. Subsidence inventory maps were prepared using deformation time-series data from Sentinel-1. Additionally, 10 potential subsidence-related factors were selected and subjected to Geographic Information System analysis. The relationship between each factor and subsidence occurrence was analyzed by using the frequency ratio. Land subsidence susceptibility maps were generated using Bagging, Multiclass Classifier, and LogitBoost models, and map validation was carried out using the area under the curve (AUC) method. Of the three models, Bagging produced the largest AUC (0.883), with LogitBoost and Multiclass Classifier producing AUCs of 0.871 and 0.856, respectively.

The availability of groundwater is of concern. The demand for groundwater in Korea increased by more than 100% during the period 1994–2014. This problem will increase with population growth. Thus, a reliable groundwater analysis model for regional scale studies is needed. This study used the geographical information system (GIS) data and machine learning to map groundwater potential in Gangneung-si, South Korea. A spatial correlation performed using the frequency ratio was applied to determine the relationships between groundwater productivity (transmissivity data from 285 wells) and various factors. This study used four topography factors, four hydrological factors, and three geological factors, along with the normalized difference wetness index and land use and soil type. Support vector regression (SVR) and metaheuristic optimization algorithms—namely, grey wolf optimization (GWO), and particle swarm optimization (PSO), were used in the construction of the groundwater potential map. Model validation based on the area under the receiver operating curve (AUC) was used to determine model accuracy. The AUC values of groundwater potential maps made using the SVR, SVR_GWO, and SVR_PSO algorithms were 0.803, 0.878, and 0.814, respectively. Thus, the application of optimization algorithms increased model accuracy compared to the standard SVR algorithm. The findings of this study improve our understanding of groundwater potential in a given area and could be useful for policymakers aiming to manage water resources in the future.

Dampak dari pencemaran air dapat menyebabkan terjadinya ketidakseimbangan ekosistem dan dapat sebagai pembawa penyakit menular. Oleh karena itu perlu dilakukan penjernihan terhadap air sebelum digunakan untuk meningkatkan kualitas air.  Salah satu  yang dapat digunakan untuk penjernihan air adalah arang aktif cangkang kelapa sawit (Elaeis guineensis). Tujuan penelitian ini adalah: Untuk mengetahui efektifitas penambahan arang aktif cangkang kelapa sawit  dalam proses  filtrasi terhadap karakter fisik (kekeruhan, Ph, bau dan rasa) air sumur. Penelitian ini bersifat eksperimen dengan menggunakan Rancangan Acak Lengkap (RAL). Filtrasi air sumur dilakukan dengan 4 perlakuan  dan 3 kali ulangan yaitu: 1). tanpa melewati saringan pasir (kontrol), 2). melewati saringan pasir tanpa arang aktif cangkang kelapa sawit, 3). melewati saringan pasir dengan penambahan arang aktif cangkang kelapa sawit  dengan ketebalan 10 cm, dan 4). ketebalan 15 cm. One way ANOVA digunakan dalam analisis data. Analisa kekeruhan adalah 100,0; 50,5; 40,4; dan 47,5 berturut-turut untuk kontrol, tanpa arang aktif, dengan arang aktif 10 cm dan dengan arang aktif 15 cm. Hasil uji menunjukkan perbedaan yang bermakna antara kekeruhan air kontrol dengan kekeruhan dari perlakuan lainnya. pH air < 7 menjadi 8,99. Analisa bau dan rasa didapatkan  air yang melewati proses filtrasi dengan penambahan arang aktif menghilangkan bau dan rasa pada air tersebut. Penambahan arang aktif cangkang kelapa sawit dengan ketebalan 10 cm cukup efektif dalam proses filtrasi air sumur dan dapat memperbaiki kualitas fisik air.

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