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All Journal Bumi Lestari Ecotrophic, Journal of Environmental Science Journal of the Civil Engineering Forum
I Made Oka Guna Antara
Center For Environment Research (PPLH), Udayana University
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Earth & Planetary Sciences Energy Environmental Science

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Prediction Landslide Location Area Using ANN (Case study in Bangli Regency, Bali Indonesia) I Made Oka Guna Antara; Ricardo Salvador Ríos Márquez; Takahiro Osawa
Bumi Lestari Journal of Environment Vol 21 No 1 (2021)
Publisher : Environmental Research Center (PPLH) of Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/blje.2021.v21.i02.p05

Abstract

Landslides are significant geo-hazards heavily impacting many regions of the world regarding human lives and economic losses. The large magnitude of natural forces involved in landslides makes actions of mitigation or prevention unfeasible, with exceptions for minor occurrences or under special conditions. Many old methods have been applied in landslide management and/or prediction, such as overlays or weighting methods. The newest/advanced methods are still being developed and one of the newest methods is Artificial Neural Network (ANN). ANN are biologically inspired computer programs designed to simulate how the human brain processes information. Many types of ANN exist; the most famous one is Multilayer Perceptron (MLP) Neural Network Algorithm with FeedForward model. MLP consists of three parts: the input layers as neurons representing the value of data; the hidden layer, which demonstrates the network training process; and the output layer, which provides the prediction of the landslide areas. In this research, the input layer consists of landslide location characteristics, such as the rainfall intensity, land cover, slope, geological types, and rate displacement of landslides. As a case study, Bangli Regency was selected. In 2017 there was a landslide disaster in the Kintamani District, Bangli Regency, which resulted in dozens of people missing or dead, and several houses destroyed. In this study different numbers of neurons were used in the hidden layer (15, 50, 100, and 150 neurons). The best performance is obtained at 150 neurons, with 0.9677 (96,77%) for the test set.
AN APPLICATION OF SEGNET FOR DETECTING LANDSLIDE AREAS BY USING FULLY POLARIMETRIC SAR DATA I Made Oka Guna Antara; Norikazu Shimizu; Takahiro Osawa; I Wayan Nuarsa
ECOTROPHIC : Jurnal Ilmu Lingkungan (Journal of Environmental Science) Vol 13 No 2 (2019)
Publisher : Master Program of Environmental Science, Postgraduate Program of Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (793.053 KB) | DOI: 10.24843/EJES.2019.v13.i02.p09

Abstract

The study location of landslide is in Hokkaido, Japan which occurred due to the Iburi Earthquake 2018. In this study the landslide has been estimated by the fully Polarimetric SAR (Pol-SAR) technique based on ALOS-2 PALSAR-2 data using the Yamaguchi’s decomposition. The Yamaguchi's decomposition is proposed by Yoshio Yamaguchi et.al. The data has been analyzed using the deep learning process with SegNet architecture with color composite. In this research, the performance of SegNet is fast and efficient in memory usage. However, the result is not good, based on the Intersection over Union (IoU) evaluation obtained the lowest value is 0.0515 and the highest value is 0.1483. That is because of difficulty to make training datasets and of a small number of datasets. The greater difference between accuracy and loss graph along with higher epochs represents overfitting. The overfitting can be caused by the limited amount of training data and failure of the network to generalize the feature set over the training images.
Development of IoT-Based Real-Time Monitoring System and LFA to Improve the Efficiency and Performance of Wastewater Treatment Plant in Udayana University Hospital Pujianiki Ni Nyoman; I Nyoman Sudi Parwata; I Made Oka Guna Antara; Kurihara Kazumi; Akhmad Rivai
Journal of the Civil Engineering Forum Vol. 9 No. 2 (May 2023)
Publisher : Department of Civil and Environmental Engineering, Faculty of Engineering, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jcef.5122

Abstract

Indonesia is one of the countries infected by the Coronavirus Disease 2019 (COVID-19) pandemic, which is caused by acute respiratory syndrome virus 2 (SARS-CoV-2). At the end of March 2020, the provincial government of Bali appointed Udayana University Hospital to handle COVID-19 patients because the province has experienced an increase in the number of positive cases. In September 2020, COVID-19 cases in Bali increased by more than 100%, resulting in a higher volume and content of hazardous liquid waste. Furthermore, hazardous liquid waste is the residue of activities that contain substances that can pollute and damage the environment and health, necessitating more efforts in managing the processing of hazardous wastewater produced by the hospital. Based on the background above, this study developed and applied an Internet of Things (IoT) based monitoring system to the Wastewater Treatment Plant (WWTP) in Udayana University Hospital. In principle, the IoT system can be used as a real-time monitoring tool and minimizes direct contact activities of officers’ WWTP sites. Moreover, the Liquid Film Aerator (LFA) was applied to improve the efficiency of WWTP. The developed IoT system successfully monitors pH, DO, and real-time temperature, and the monitoring results were presented in a web-based user interface. The result shows better power usage efficiency than conventional aeration. Furthermore, conventional aeration with a root blower requires 619.8 watts to produce 1 mg/L of DO, while LFA only requires 273.2 watts. The developed systems can be applied to other hospitals or similar wastewater plants that handle COVID-19 cases.
Co-Authors Akhmad Rivai I Nyoman Sudi Parwata I Wayan Nuarsa Kurihara Kazumi Norikazu Shimizu Pujianiki Ni Nyoman Ricardo Salvador Ríos Márquez Takahiro Osawa Takahiro Osawa