Identification of Landslide Prone Areas Using Slope Morphology Method in South Leitimur District, Ambon City

South Leitimur District is one of the districts in Ambon City where landslides often occur, and this disaster causes many losses. One of the mitigation efforts is mapping areas with the potential for landslides to determine their distribution and risks. This study aims to apply the slope morphology method to identify landslide-prone areas in South Leitimur Regency. This study uses a Digital Elevation Model (DEM) extracted into the shape of slopes and slopes and processed using ArcGIS 10.8 software. This study uses the slope morphology method or SMORPH to identify and classify areas with potential landslides based on the matrix between the slope's shape and angle. The results of the study were classified into four classes of landslide potential, namely very low potential with an area of 2,489, 53 ha, low with an area of 3,278, 22 ha, medium with an area of 672, 32 ha, and high with an area of 685, 67 ha. Hutumury Village is a village that has the largest landslide potential area in each class of landslide potential in the South Leitimur District; this is because this village is a village that has the most significant area compared to other villages. The village that has a low landslide potential is Ema Village. The results of this study also illustrate that the higher the slope with convex or concave slopes, the higher the potential for landslides. The results of this study are expected to help the government of South Leitimur Regency in efforts to mitigate landslides in the future

Application of different watershed units to debris flow susceptibility mapping: A case study of Northeast China

The main purpose of this study was to compare two types of watershed units divided by the hydrological analysis method (HWUs) and mean curvature method (CWUs) for debris flow susceptibility mapping (DFSM) in Northeast China. Firstly, a debris flow inventory map consisting of 129 debris flows and 129 non-debris flows was randomly divided into a ratio of 70% and 30% for training and testing. Secondly, 13 influencing factors were selected and the correlations between these factors and the debris flows were determined by frequency ration analysis. Then, two types of watershed units (HWUs and CWUs) were divided and logistic regression (LR), multilayer perceptron (MLP), classification and regression tree (CART) and Bayesian network (BN) were selected as the evaluation models. Finally, the predictive capabilities of the models were verified using the predictive accuracy (ACC), the Kappa coefficient and the area under the receiver operating characteristic curve (AUC). The mean AUC, ACC and Kappa of four models (LR, MLP, CART and BN) in the training stage were 0.977, 0.931, and 0.861, respectively, for the HWUs, while 0.961, 0.905, and 0.810, respectively, for the CWUs; in the testing stage, were 0.904, 0.818, and 0.635, respectively, for the HWUs, while 0.883, 0.800, and 0.601, respectively, for the CWUs, which showed that HWU model has a higher debris flow prediction performance compared with the CWU model. The CWU-based model can reflect the spatial distribution probability of debris flows in the study area overall and can be used as an alternative model

ANALISIS GEOMORFOLOGI KEJADIAN LONGSOR DI KECAMATAN WOLOTOLO KABUPATEN ENDE

The aimed of this research to determine the analysis of landslide in Wolotolo village, Detusoko district, Ende regency. This research used a survey method to map geomorphological conditions in Wolotolo Village, Detusoko District, Ende Regency. The method used in this research was a survey method to map geomorphological conditions in Wolotolo Village, Detusoko District. The main stages of this research include the pre-field, field and post-field stages. The pre-field stage was in the form of secondary data collection. The field stage was taking soil samples, identifying landslides in the field. The post-field stage was in the form of making landslides and reporting the results of field identification. The results of this research indicate that most of the soil types in Wolotolo Village wereultisols and inceptisols. This can caused this area to became an area that was prone to landslides. This study also found several dominant factors that may caused landslides, namely: slope, land use, soil type and rainfall as trigger factors. Steep slopes tend to be prone to landslides, especially with unstable slope conditions. This causes many steep slopes to be steep. Based on the results of the analysis, the slope in Wolotolo Village was more dominated by a slope of >45% which is a very steep area. So it can be identified that the area has the potential for landslides. Land use on steep slopes also affects slope stability. The land use in the study area is dominated by forest, plantations and shrubs

GIS-based evaluation of diagnostic areas in landslide susceptibility analysis of Bahluieț River Basin (Moldavian Plateau, NE Romania). Are Neolithic sites in danger?

© 2018 Elsevier BV. This manuscript version is made available under the CC-BY-NC-ND 4.0 license: http://creativecommons.org/licenses/by-nc-nd/4.0/ This author accepted manuscript is made available following 24 month embargo from date of publication (April 2018) in accordance with the publisher’s archiving policy.The aim of this study is to compare the predictive strenghtness of different diagnostic areas in determining landslide susceptibility using frequency ratio (FR), statistical index (SI), and analytic hierarchy process (AHP) models in a catchment from the northeastern part of Romania. Scarps (point), landslide areas (polygon), and middle of the landslide (point) have been tested and checked in regards to their performance. The three statistical models have been employed to assess the landslide susceptibility using eleven conditioning factors (slope angle, elevation, curvature, lithology, precipitations, land use, topographic wetness index (TWI), landforms, aspect, plan curvature and distance to river). The three models were validated using the receiver operating characteristic (ROC) curves and the seed cell area index (SCAI) methods. The predictive capability of each model was established from the area under the curve (AUC), for FR, SI and AHP; the values are 0.75, 0.81 and 0.78 (using polygon as diagnostic area), respectively. Among the three methods used, SI had a better predictability. When it comes to the predictability values regarding the diagnostic areas, the landslide area (polygon) proves to have the highest values. This results from the entire surface of the landslide being taken into account when validating the data. Approximately 70% of the Neolithic sites are located in areas with high and very high susceptibility to landslides, meaning that they are in danger of being destroyed in the future. The final susceptibility maps are useful in hazard mitigation, risk reduction, a sustainable land use planning, evaluation of cultural heritage integrity, and to highlight the most endangered sites that are likely to be destroyed in the future

Aplikasi Citra Landsat untuk Pemetaan Daerah Rawan Longsor di Kabupaten Bandung

Tanah longsor adalah gerakan tanah yang disebabkan oleh faktor alami dan nonalami. Faktor alami dipengaruhi oleh struktur geologi daerah, jenis batuan, kemiringan lereng, dan intensitas curah hujan. Sedangkan faktor nonalami bersifat dinamis yaitu penggunaan lahan dan infrastruktur. Metode penginderaan jauh dengan citra satelit dapat digunakan untuk memetakan kawan rawan longsor. Citra satelit merupakan hasil perekaman satelit yang menggambarkan objek di permukaan bumi. Citra satelit terdiri dari beberapa band dengan panjang gelombang tertentu. Komposit band pada citra digunakan untuk mempertajam objek untuk mempermudah klasifikasi tutupan lahan. Pemetaan zonasi area rawan longsor di Kabupaten Bandung menggunakan metode pembobotan dan scoring. Pembobotan dan scoring dilakukan pada semua parameter yang menyebabkan longsor yaitu struktur geologi, jenis batuan, cutah hujan, kemiringan lereng, infrastruktur, dan tuutpan lahan hasil pengolahan citra satelit.  Hasil yang didapat menunjukan potensi tanah longsor di Kabupaten Bandung didominasi kategori sedang sampai tinggi. Pemetaan kawasan rawan longsor sangat diperlukan sebagai langkah mitigasi untuk mengurangi dampak yang diakibatkan oleh bencana tanah longsor

Pemetaan Rawan Longsor Daerah Palu Dengan Metode Weight Overlay

Topografi yang berbukit dengan tingkat kemiringan yang cukup tinggi menjadi salah satu faktor penting yang mempengaruhi terjadinya longsor. Hal ini didukung dengan kondisi alam Kota Palu yang dominan dengan daerah perbukitan yang cukup terjal juga sangat berpengaruh terhadap terjadinya bencana longsor. Penelitian ini dilakukan dengan tujuan untuk memanfaatkan SIG dalam pemetaan tingkat kerawanan terjadinya bencana longsor di Kota Palu, Sulawesi Tengah. Hasil penelitian menunjukkan curah hujan pada wilayah penelitian didominasi oleh intensitas yang sedang hingga tinggi berada pada kisaran di bawah 700 hingga di atas 2500. Jenis tanah yang mendominasi yaitu jenis batuan kapur dan metamorf Dengan didominasi oleh batuan kapur dan metamorf. Kemudian jenis batuan didominasi oleh batuan berkapur dan metamorf, batuan sedimen serta batuan vulkanik. Berdasarkan peta kemiringan lereng Kota Palu, dibagi 4 klasifikasi kemiringan lereng berdasarkan kemiringannya Sangat Rendah, Rendah, Sedang, dan Tinggi. Hasil dari pemanfaatan SIG ini kita dapat mengetahui bahwa kabupaten Palu memiliki potensi bencana longsor yang cukup tinggi karena Kondisi tanah di Kota Palu yang cenderung tidak memiliki sumber serapan yang baik, sehingga air yang masuk ke dalam tanah tidak dapat menahan dan mengakibatkan erosi pada lapisan tanah yang dilewatinya

IDENTIFIKASI WILAYAH RAWAN LONGSOR DI KECAMATAN CITEUREUP KABUPATEN BOGOR

Kejadian bencana longsor di Kecamatan Citeureup pada tahun 2017 – 2021 tercatat sebanyak 21 kejadian yang menyebabkan puluhan rumah rusak ringan hingga berat, serta infrastruktur jalan dan jembatan terputus. Penelitian ini bertujuan untuk mengidentifikasi wilayah rawan longsor di Kecamatan Citeureup. Metode yang digunakan yaitu metode deksriptif dengan pendekatan spasial. Pengolahan data menggunakan software ArcGIS 10.8 dengan teknik analisis yang digunakan adalah analisis spasial dengan melakukan tumpang susun antar lima parameter kerawanan longsor. Setelah itu, dilakukan validasi lapangan. Subjek dalam penelitian ini yaitu 12 unit lahan terpilih. Hasil penelitian menunjukan bahwa rawan longsor di daerah penelitian terbagi menjadi empat kategori, yaitu rendah, sedang, tinggi, dan sangat tinggi. Wilayah yang masuk pada kategori rawan longsor rendah yaitu Desa Citeureup, Leuwinutug, Puspanegara, Puspasari, Sanja, serta Kelurahan Karangasem Barat dan Karangasem Timur. Sebaran wilayah pada kategori rawan longsor sedang berada di sebagian kecil Desa Hambalang, Sanja, Kelurahan Karangasem Timur, dan Desa Leuwinutug. Sebaran wilayah pada kategori rawan longsor tinggi berada di sebagian besar Desa Hambalang, Tajur, dan Tangkil. Sebaran wilayah pada kategori rawan longsor sangat tinggi berada di sebagian besar Desa Hambalang, Tangkil, Sukahati, dan sebagian kecil di Kelurahan Karangasem Timur. Persebaran daerah rawan longsor didominasi oleh wilayah pada kategori rawan longsor tinggi. Sedangkan persebaran daerah rawan longsor paling sedikit terdapat pada wilayah kategori rawan longsor sedang. ***** There were 21 landslides in Citeureup Subdistrict in 2017-2021 which caused several houses severely broken to heavily damaged , and road and bridge infrastructure was cut off. This study aims to identify landslide-prone areas in Citeureup District. The method used was descriptive method with a spatial approach. Data was processed using GIS 10.8 software with the analytical technique used was a spatial analysis by overlaying five parameters of landslide susceptibility. After that, field validation was carried out. The subjects in this study were 12 selected land units. The results showed that the landslide-prone area in the study area was divided into four categories, namely low, medium, high, and very high. Areas that are included in the category of low landslide prone are Citeureup, Leuwinutug, Puspanegara, Puspasari, Sanja, and West Karangasem and East Karangasem Villages. The distribution of areas in the landslide-prone category is in a small part of Hambalang Village, Sanja, East Karangasem Village, and Leuwinutug Village. The distribution of areas in the high landslide-prone category is in most of Hambalang, Tajur, and Tangkil Villages. The distribution of areas in the landslide-prone category is very high in most of Hambalang, Tangkil, Sukahati, and a small part of it in East Karangasem Village. The distribution of landslide-prone areas is dominated by areas in the high landslide-prone category. Meanwhile, the distribution of landslide-prone areas is at least found in the medium landslide-prone category area

Prototype-Design of Soil Movement Detector Using IoT Hands-on Application

The landslide disaster that killed people occurred due to public ignorance of the type of soil prone to landslides. Several efforts have been made to create prototype tools for soil movement detection. However, researchers using the Internet of Things (IoT) technology are still limited. The IoT allows for the transmission of data over an internet connection, is always connected, offers remote control capabilities, and data sharing. All of this served as the prototype design of foundation for the soil movement detection. A light-based proximity sensor is used in system, and its output is represented as a movement of the soil on an inclined plane. F furthermore, the data used as input for the NodeMCU ESP8266 microcontroller is linked to the internet. The output is an HMI in the form of an LCD monitor that displays the soil movement measurement. The simulation of disturbances in an inclined plane is done differently depending on the frequency and duration. Moreover, monitoring is carried out by transferring processed data to the Blynk platform, which is subsequently shown in real time via the Blynk Android application. The test results of the tool used three distinct samples, as well as varied disturbance frequencies and durations. With the soil samples, the biggest movement data was 5cm achieved at a disturbance frequency of 5Hz and 40 seconds duration. The largest movement data for sand samples was 11cm at a disturbance frequency of 3Hz and 50 seconds duration, followed by largest movement data for sand soil mixture samples was 8cm at a disturbance frequency of 5Hz and 50 seconds duration. People should not reside on slopes, especially if the soil's primary component is sand.Bencana longsor yang memakan korban jiwa tersebut terjadi karena ketidaktahuan masyarakat akan jenis tanah yang rawan longsor. Beberapa upaya telah dilakukan untuk membuat prototipe alat pendeteksi gerakan tanah. Namun, peneliti yang menggunakan teknologi Internet of Things (IoT) masih terbatas. IoT memungkinkan transmisi data melalui koneksi internet, selalu terhubung, menawarkan kemampuan kendali jarak jauh, dan berbagi data. Semua ini berfungsi sebagai prototipe desain pondasi untuk deteksi gerakan tanah. Sensor jarak berbasis cahaya digunakan dalam sistem, dan outputnya direpresentasikan sebagai pergerakan tanah pada bidang miring. Selanjutnya data yang digunakan sebagai input mikrokontroler NodeMCU ESP8266 terhubung dengan internet. Outputnya berupa HMI berupa monitor LCD yang menampilkan hasil pengukuran pergerakan tanah. Simulasi gangguan pada bidang miring dilakukan secara berbeda tergantung pada frekuensi dan durasi. Selain itu, pemantauan dilakukan dengan mentransfer data yang diproses ke platform Blynk, yang selanjutnya ditampilkan secara real time melalui aplikasi Blynk Android. Hasil pengujian alat menggunakan tiga sampel yang berbeda, serta frekuensi dan durasi gangguan yang bervariasi. Dengan sampel tanah, data pergerakan terbesar adalah 5cm yang dicapai pada frekuensi gangguan 5Hz dan durasi 40 detik. Data pergerakan terbesar untuk sampel pasir adalah 11cm pada frekuensi gangguan 3Hz dan durasi 50 detik, diikuti oleh data pergerakan terbesar untuk sampel campuran tanah pasir adalah 8cm pada frekuensi gangguan 5Hz dan durasi 50 detik. Orang tidak boleh tinggal di lereng, terutama jika komponen utama tanahnya adalah pasir

Identifikasi Tingkat Kerawanan Tanah Longsor di Provinsi Sulawesi Tengah

The Landslide disaster is an event that is associated with various types of factors such as precipitation, geology, distance from the fault, vegetation, and topography. The Central Sulawesi region is an area that has many hills and mountains as well as sandy soil types with a total annual rainfall that varies due to geographical location and topographical conditions which make this region have a type of rain which is dominated by local dry types and very dry or wet to very wet, so it is interesting to be used as a research area. Based on the background of the problems that have been described, this study aims to determine areas that are prone to landslides in Central Sulawesi Province. The types of data used to determine the level of vulnerability to landslides are rainfall, rock type, slope, land cover and soil type. The mapping system used is geographic information system (GIS) software with a weighted overlay method. The results showed that the level of vulnerability to landslides in the Central Sulawesi region was dominated by the moderate level of vulnerability. Based on the research results, it can be concluded that landslides will occur if the values of all the factors (parameters) that cause them are met.Bencana tanah longsor merupakan suatu kejadian yang berhubungan dengan berbagai jenis faktor seperti presipitasi, geologi, jarak dari patahan, vegetasi, dan topografi. Wilayah Sulawesi Tengah merupakan wilayah yang memiliki banyak perbukitan dan pegunungan serta jenis tanah yang berpasir dengan total curah hujan tahunan yang bervariasi karena letak geografis dan kondisi topografinya yang membuat wilayah ini memiliki tipe hujan yang didominasi oleh tipe lokal kering serta sangat kering maupun basah hingga sangat basah, sehingga menarik untuk dijadikan sebagai wilayah penelitian. Berdasarkan latarbelakang masalah yang telah dijelaskan, penelitian ini bertujuan untuk mengetahui wilayah yang rawan terhadap bencana tanah longsor di Provinsi Sulawesi Tengah. Jenis data – data yang digunakan untuk menentukan tingkat kerawanan tanah longsor yakni curah hujan, tipe batuan, kelerengan, tutupan lahan dan jenis tanah. Adapun sistem pemetaan yang digunakan yaitu menggunakan software sistem informasi geografis (SIG) dengan metode overlay berbobot. Hasil penelitian menunjukkan bahwa tingkat kerawanan tanah longsor di wilayah Sulawesi Tengah didominasi oleh tingkat kerawanan sedang. Berdasarkan hasil penelitian dapat ditarik kesimpulan bahwa bencana tanah longsor akan terjadi apabila nilai seluruh faktor (parameter) penyebabnya terpenuhi

Penerapan Weighted Overlay Pada Pemetaan Tingkat Probabilitas Zona Rawan Longsor di Kabupaten Sumedang, Jawa Barat

Kabupaten Sumedang berada pada wilayah pegunungan dan perbukitan sehingga meningkatkan kemungkinkan untuk terjadinya bencana tanah longsor yang dapat menimbulkan kerugian baik secara materi ataupun nonmateri. Untuk mencegah dan meminimalisir dampak dari bencana tersebut diperlukan pengetahuan mendetail mengenai bencana tanah longsor itu sendiri. Berdasarkan hal tersebut maka dilakukan penelitian ini dengan tujuan untuk melakukan pemetaan dan memberikan informasi tentang wilayah-wilayah yang mempunyai kerawanan terjadinya bencana longsor di Kabupaten Sumedang yang kemudian diharapkan hasil dari penelitian ini dapat dijadikan acuan dalam melakukan upaya mitigasi serta diharapkan dapat meminimalkan dampak yang diakibatkan jika terjadinya bencana tanah longsor pada wilayah Kabupaten Sumedang. Penelitian ini memanfaatkan metode skoring, weighting dan overlay yang terdapat pada SIG dalam melakukan pemetaan daerah rawan longsor dengan mengacu terhadap nilai dan parameter yang dikeluarkan oleh Puslittanak 2004. Berdasarkan penelitian yang telah dilakukan maka diketahui bahwa Kabupaten Sumedang didominasi oleh jenis tanah alluvial, jenis batuan vulkanik, kemiringan lereng dengan kisaran 15-30%, dan memiliki curah hujan yang sangat tinggi. Hasil analisis yang dilakukan didapatkan bahwa Kabupaten Sumedang memiliki tingkat kerawanan longsor yang berada pada kategori sedang sampai dengan tinggi