MODEL SUPPORT VEKTOR MACHINE (SVM) BERDASARKAN PARAMETER WINDOWS UNTUK PREDIKSI KEKUATAN GEMPA BUMI

Earthquakes are a type of natural disaster that currently cannot be predicted. Predicting the value of earthquake magnitude for related parties such as government and National Disaster Management Authority is very important. Furthermore, the results of earthquake predictions by several parties are used as indicators in post-earthquake response in minimizing the risks that will occur. Several studies have applied machine learning methods to predict earthquakes such as deep neural networks and parallel Support Vector Regression. In this article, we propose a data mining method using the Support Vector Machine (SVM) algorithm accompanied by the optimization of the windowing parameter value in the model that is applied to predict the value of the earthquake magnitude. Based on its advantages, the SVM model was chosen because it has been applicable in time series data processing. In the experimental stage process, parameter settings are first carried out, namely setting the kernel type, sampling type, and number of windowing to optimize the level of accuracy of the resulting model. The results showed that the best model with the smallest Root Mean Square Error (RMSE) was 0.712

Neural Networks: Training and Application to Nonlinear System Identification and Control

This dissertation investigates training neural networks for system identification and classification. The research contains two main contributions as follow:1. Reducing number of hidden layer nodes using a feedforward componentThis research reduces the number of hidden layer nodes and training time of neural networks to make them more suited to online identification and control applications by adding a parallel feedforward component. Implementing the feedforward component with a wavelet neural network and an echo state network provides good models for nonlinear systems.The wavelet neural network with feedforward component along with model predictive controller can reliably identify and control a seismically isolated structure during earthquake. The network model provides the predictions for model predictive control. Simulations of a 5-story seismically isolated structure with conventional lead-rubber bearings showed significant reductions of all response amplitudes for both near-field (pulse) and far-field ground motions, including reduced deformations along with corresponding reduction in acceleration response. The controller effectively regulated the apparent stiffness at the isolation level. The approach is also applied to the online identification and control of an unmanned vehicle. Lyapunov theory is used to prove the stability of the wavelet neural network and the model predictive controller. 2. Training neural networks using trajectory based optimization approachesTraining neural networks is a nonlinear non-convex optimization problem to determine the weights of the neural network. Traditional training algorithms can be inefficient and can get trapped in local minima. Two global optimization approaches are adapted to train neural networks and avoid the local minima problem. Lyapunov theory is used to prove the stability of the proposed methodology and its convergence in the presence of measurement errors. The first approach transforms the constraint satisfaction problem into unconstrained optimization. The constraints define a quotient gradient system (QGS) whose stable equilibrium points are local minima of the unconstrained optimization. The QGS is integrated to determine local minima and the local minimum with the best generalization performance is chosen as the optimal solution. The second approach uses the QGS together with a projected gradient system (PGS). The PGS is a nonlinear dynamical system, defined based on the optimization problem that searches the components of the feasible region for solutions. Lyapunov theory is used to prove the stability of PGS and QGS and their stability under presence of measurement noise

Geohazards in the three Gorges Reservoir Area, China – Lessons learned from decades of research

Abstract The impoundment of the 660-km long reservoir behind the huge Three Gorges Dam, the world's largest hydropower station, increased regional seismicity and reactivated severe geohazards. Before the reservoir filling was initiated in 2003, the region had approximately two earthquakes per year with magnitudes between 3.0 and 4.9; after the full impoundment in 2008, approximately 14 earthquakes per year occurred with magnitudes between 3.0 and 5.4. In addition, hundreds of landslides were reactivated and are now in a state of intermittent creep. Many landslides exhibit step-like annual pattern of displacement in response to quasi-regular variations in seasonal rainfall and reservoir level. Additional problems include rock avalanches, impulse waves and debris flows. The seriousness of these events motivated numerous studies that resulted in 1) Better insight into the behavior and evolution mechanism of geohazards in the Three Gorges Reservoir Area (TGRA); 2) Implementation of monitoring and early-warning systems of geohazards; and 3) Design and construction of preventive countermeasures including lattice anchors, stabilizing piles, rock bolts, drainage canals and tunnels, and huge revetments. This paper reviews the hydro-geologic setting of TGRA geohazards, examines their occurrence and evolution in the past few decades, offers insight learned from extensive research on TGRA geohazards, and suggests topics for future research to address the remaining challenges

Earthquake Engineering

The book Earthquake Engineering - From Engineering Seismology to Optimal Seismic Design of Engineering Structures contains fifteen chapters written by researchers and experts in the fields of earthquake and structural engineering. This book provides the state-of-the-art on recent progress in the field of seimology, earthquake engineering and structural engineering. The book should be useful to graduate students, researchers and practicing structural engineers. It deals with seismicity, seismic hazard assessment and system oriented emergency response for abrupt earthquake disaster, the nature and the components of strong ground motions and several other interesting topics, such as dam-induced earthquakes, seismic stability of slopes and landslides. The book also tackles the dynamic response of underground pipes to blast loads, the optimal seismic design of RC multi-storey buildings, the finite-element analysis of cable-stayed bridges under strong ground motions and the acute psychiatric trauma intervention due to earthquakes

Limited Earthquake Interaction During a Geothermal Hydraulic Stimulation in Helsinki, Finland

We investigate induced seismicity associated with a hydraulic stimulation campaign performed in 2020 in the 5.8 km deep geothermal OTN-2 well near Helsinki, Finland as part of the St1 Deep Heat project. A total of 2,875 m3 of fresh water was injected during 16 days at well-head pressures <70 MPa and with flow rates between 400 and 1,000 L/min. The seismicity was monitored using a high-resolution seismic network composed of 10 borehole geophones surrounding the project site and a borehole array of 10 geophones located in adjacent OTN-3 well. A total of 6,121 induced earthquakes with local magnitudes urn:x-wiley:21699313:media:jgrb55848:jgrb55848-math-0001 were recorded during and after the stimulation campaign. The analyzed statistical parameters include magnitude-frequency b-value, interevent time and interevent time ratio, as well as magnitude correlations. We find that the b-value remained stationary for the entire injection period suggesting limited stress build-up or limited fracture network coalescence in the reservoir. The seismicity during the stimulation neither shows signatures of magnitude correlations, nor temporal clustering or anticlustering beyond those arising from varying injection rates. The interevent time statistics are characterized by a Poissonian time-varying distribution. The calculated parameters indicate no earthquake interaction. Focal mechanisms suggest that the injection activated a spatially distributed network of similarly oriented fractures. The seismicity displays stable behavior with no signatures pointing toward a runaway event. The cumulative seismic moment is proportional to the cumulative hydraulic energy and the maximum magnitude is controlled by injection rate. The performed study provides a base for implementation of time-dependent probabilistic seismic hazard assessment for the project site

Estimation of Flores Sea Aftershock Rupture Data Based on AI

The earthquake catalog notes that there have been earthquakes with Mw &gt; 7 that hit the Flores area, three of which occurred in the Flores Sea in 1992, 2015, and 2021. Revealed that the seismic activity of Eastern Indonesia is thought to be influenced by the isolated thrust fault segment of the island of Flores and the island of Wetar. The study of the rising fault segment on Flores Island and Wetar Island helps in further understanding the fault behavior, earthquake pattern, and seismic risk in the Flores Sea region. In earthquakes with giant magneto, an aftershock can occur due to the interaction of ground movements. This research analyzes and compares the data from the evaluation of the classification algorithm and the regression algorithm. The initial stages of this research include requesting IRIS DMC Web Service data. The data is then subjected to a cleaning process to obtain the expected feature extraction. The next stage is to perform the clustering process. This stage is carried out to label dependent data by adding new features as data clusters. The following procedure divides the validation value, which consists of training and test data. The estimation results show that the classification algorithm's evaluation value is better than that of the regression algorithm. The evaluation value of several algorithms indicates this, with an accuracy rate between 80% and 100%

斜面崩壊の分布図作成、分類、発生しやすさの評価 : 日本と中国を例に

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 小口 高, 東京大学教授 斎藤 馨, 東京大学教授 須貝 俊彦, 東京大学准教授 芦 寿一郎, 東京大学准教授 早川 裕一University of Tokyo(東京大学

Landslide mapping using multiscale LiDAR digital elevation models

Thesis (M.S.) University of Alaska Fairbanks, 2018This study presents a new methodology to identify landslide and landslide susceptible locations in interior Alaska using only geomorphic properties from light detection and ranging (LiDAR) derivatives (i.e., slope, profile curvature, roughness) and the normalized difference vegetation index (NDVI). The study specifically focused on the effect of different resolutions of LiDAR images in identifying landslide locations. I developed a semi-automated object-oriented image classification approach in ArcGIS 10.5, and prepared a landslide inventory from visual observation of hillshade images. The multistage workflow included combining derivatives from 1m, 2.5m, and 5m resolution LiDAR, image segmentation, image classification using a support vector machine classifier, and image generalization to clean false positives. I assessed the accuracy of the classifications by generating confusion matrix tables. Analysis of the results indicated that the scale of LiDAR images played an important role in the classification, and the use of NDVI generated better results in identifying landslide and landslide susceptible places. Overall, the LiDAR 5m resolution image with NDVI generated the best results with a kappa value of 0.55 and an overall accuracy of 83%. The LiDAR 1m resolution image with NDVI generated the highest producer accuracy of 73% in identifying landslide locations. I produced a combined overlay map by summing the individual classified maps, which was able to delineate landslide objects better than the individual maps. The combined classified map from 1m, 2.5m, and 5m resolution LiDAR with NDVI generated producer accuracies of 60%, 80%, 86%, and user accuracies of 39%, 51%, 98% for landslide, landslide susceptible, and stable locations, respectively, with an overall accuracy of 84% and a kappa value of 0.58. The proposed method can be improved by fine-tuning segmented image generation, incorporating other data sets, and developing a standard accuracy assessment technique for object-oriented image analysis

Remote Sensing of Natural Hazards

Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches