scholarly journals Coral Reef Change Detection in Remote Pacific Islands Using Support Vector Machine Classifiers

2019 ◽  
Vol 11 (13) ◽  
pp. 1525 ◽  
Author(s):  
Justin J. Gapper ◽  
Hesham El-Askary ◽  
Erik Linstead ◽  
Thomas Piechota
Keyword(s):  
Support Vector Machine ◽  
Coral Reef ◽  
Change Detection ◽  
Cross Validation ◽  
Pacific Islands ◽  
Coral Cover ◽  
Ground Truth ◽  
Support Vector ◽  
Live Coral ◽  
Ground Truth Data

Despite the abundance of research on coral reef change detection, few studies have been conducted to assess the spatial generalization principles of a live coral cover classifier trained using remote sensing data from multiple locations. The aim of this study is to develop a machine learning classifier for coral dominated benthic cover-type class (CDBCTC) based on ground truth observations and Landsat images, evaluate the performance of this classifier when tested against new data, then deploy the classifier to perform CDBCTC change analysis of multiple locations. The proposed framework includes image calibration, support vector machine (SVM) training and tuning, statistical assessment of model accuracy, and temporal pixel-based image differencing. Validation of the methodology was performed by cross-validation and train/test split using ground truth observations of benthic cover from four different reefs. These four locations (Palmyra Atoll, Kingman Reef, Baker Island Atoll, and Howland Island) as well as two additional locations (Kiritimati Island and Tabuaeran Island) were then evaluated for CDBCTC change detection. The in-situ training accuracy against ground truth observations for Palmyra Atoll, Kingman Reef, Baker Island Atoll, and Howland Island were 87.9%, 85.7%, 69.2%, and 82.1% respectively. The classifier attained generalized accuracy scores of 78.8%, 81.0%, 65.4%, and 67.9% for the respective locations when trained using ground truth observations from neighboring reefs and tested against the local ground truth observations of each reef. The classifier was trained using the consolidated ground truth data of all four sites and attained a cross-validated accuracy of 75.3%. The CDBCTC change detection analysis showed a decrease in CDBCTC of 32% at Palmyra Atoll, 25% at Kingman Reef, 40% at Baker Island Atoll, 25% at Howland Island, 35% at Tabuaeran Island, and 43% at Kiritimati Island. This research establishes a methodology for developing a robust classifier and the associated Controlled Parameter Cross-Validation (CPCV) process for evaluating how well the model will generalize to new data. It is an important step for improving the scientific understanding of temporal change within coral reefs around the globe.

Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

2018 ◽  
Vol 1 (1) ◽  
pp. 120-130 ◽  
Author(s):  
Chunxiang Qian ◽  
Wence Kang ◽  
Hao Ling ◽  
Hua Dong ◽  
Chengyao Liang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Environmental Factors ◽  
Cross Validation ◽  
Concrete Strength ◽  
Simulation Method ◽  
Support Vector ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Influence Degree ◽  
Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

Coral reef condition in several dive points around Bunaken Island, North Sulawesi

2014 ◽  
pp. 44
Author(s):  
Robert Towoliu
Keyword(s):  
Coral Reef ◽  
Coral Cover ◽  
Integrated Management ◽  
Good Condition ◽  
Live Coral ◽  
Core Zone ◽  
Integrated Management System ◽  
North Sulawesi ◽  
Live Coral Cover ◽  
Reef Condition

In order to know the coral reef conditions at several diving points around Bunaken Island, three dive locations (Ron’s point, Lekuan, and Tawara) were chosen as representative locations receiving pressures from snorkeling and SCUBA diving activities, while  core zone was representative of location for  no diving and fishing activities.  Results showed that location with diving activities had live coral cover  ranging from 16.89% to 45.78% at 3 and 10m depths, with condition range of bad to moderate, while the location for no diving and fishing activities (core zone) had live coral cover of 55.03% at 3m and 58.15% at 10m, respectively,  with good condition category.  The present study indicated that the diving activities have affected the coral reef condition, so that a sustainable integrated management system is needed to use the marine ecotourism potency without degrading the coral reef condition in Bunaken Island. Untuk mengetahui kondisi terumbu karang di beberapa lokasi penyelaman di Pulau Bunaken, tiga lokasi penyelaman(Ron’s point, Lekuan, dan Tawara) dipilih mewakili lokasi dengan tekanan aktivitas penyelaman snorkeling maupun SCUBA, sedangkan satu lokasi lainnya yaitu zona inti dipilih mewakili lokasi tanpa aktivitas penyelaman maupun aktivitas penangkapan ikan.  Hasil penelitian ini memperlihatkan bahwa lokasi dengan tekanan aktivitas penyelaman memiliki prosentase tutupan karang batu/hidup berkisar antara 16,89% - 45,78% pada kedalaman 3 dan 10m, dengan kategori kondisi terumbu karang buruk sampai cukup, sedangkan pada lokasi yang tidak memiliki aktivitas penyelaman memiliki prosentase tutupan karang batu/hidup sebesar 53,03% pada 3m dan 58,15% pada 10m dengan kategori kondisi terumbu karang adalah baik.  Hasil penelitian ini mengindikasikan bahwa aktivitas penyelaman snorkeling maupun SCUBA berdampak pada kondisi terumbu karang di Pulau Bunaken, sehingga sangat diperlukan system pengelolaan yang terpadu dan berkesinambungan dalam memanfaatkan secara maksimal potensi ekowisata bahari tanpa merusak ekosistem terumbu karang di Pulau Bunaken.

Supervised Change Detection in Satellite Imagery Using Super Pixels and Relevance Feedback

GEOMATICA ◽  
2014 ◽  
Vol 68 (1) ◽  
pp. 5-14 ◽  
Author(s):  
Surender Varma Gadhiraju ◽  
Hichem Sahbi ◽  
Biplab Banerjee ◽  
Krishna Mohan Buddhiraju
Keyword(s):  
Change Detection ◽  
Relevance Feedback ◽  
Ground Truth ◽  
Feedback Mechanism ◽  
User Preferences ◽  
Support Vector ◽  
Remotely Sensed Data ◽  
Detection Strategy ◽  
Final Output ◽  
Traditional Approaches

The data from remote sensing satellites provide opportunities to acquire information about land at varying resolutions and has been widely used for change detection studies. A large number of change detection methodologies and techniques utilizing remotely sensed data have been developed, and newer techniques are still emerging. In this paper, a novel supervised approach of change detection using Support Vector Machine (SVM) and super pixels is proposed. In the formulation of change detection, SVM is modeled as a binary classifier to get the final output as “Change” and “No-Change” information. A relevant feedback mechanism is also included in to the change detection strategy so that it adapts in accordance with user preferences. Both ground truth and relevance feedback are collected using the developed GUIs. Comparison of the proposed approach with three other techniques of change detection is done via the experiments conducted on three multitemporal datasets. It is observed that the supervised, super pixel based change detection strategy gives superior results compared to traditional approaches of change detection. It is also seen that the usage of relevance feedback fine-tunes the results of change detection and acts as a desirable post-change detection process.

scholarly journals Intelligent Prediction of Sieving Efficiency in Vibrating Screens

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Bin Zhang ◽  
Jinke Gong ◽  
Wenhua Yuan ◽  
Jun Fu ◽  
Yi Huang
Keyword(s):  
Support Vector Machine ◽  
Cross Validation ◽  
Prediction Performance ◽  
Least Square ◽  
Support Vector ◽  
Adaptive Genetic Algorithm ◽  
Screen Length ◽  
Vibrating Screen ◽  
Working Parameters ◽  
Better Than

In order to effectively predict the sieving efficiency of a vibrating screen, experiments to investigate the sieving efficiency were carried out. Relation between sieving efficiency and other working parameters in a vibrating screen such as mesh aperture size, screen length, inclination angle, vibration amplitude, and vibration frequency was analyzed. Based on the experiments, least square support vector machine (LS-SVM) was established to predict the sieving efficiency, and adaptive genetic algorithm and cross-validation algorithm were used to optimize the parameters in LS-SVM. By the examination of testing points, the prediction performance of least square support vector machine is better than that of the existing formula and neural network, and its average relative error is only 4.2%.

scholarly journals CONDITION OF CORAL AND REEF FISH IN THE LOCATION OF FISH CATCHING USING DYNAMITE FISHING IN KAPOPOSANG WATER PARK AND THE SURROUNDING SEA

2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Mahmudin Mahmudin ◽  
Chair Rani ◽  
Hamzah Hamzah
Keyword(s):  
Coral Reef ◽  
Reef Fish ◽  
Coral Cover ◽  
Low Cost ◽  
Fish Abundance ◽  
Live Coral ◽  
Coral Reef Ecosystem ◽  
Control Location ◽  
Target Fish ◽  
Reef Ecosystem

Dynamite fishing is one of the causes of damage to the coral reef ecosystem in Indonesia. Fishing activities using explosives (dynamite fishing) occur because of the desire of fishermen to get a lot of catch with low cost in a short time. Kapoposang Water Park (WP) is a region rich in marine biological resources. However, dynamite fishing activities which are still found within the area have caused the coral reef ecosystem to be severely damaged. The results showed a lower difference in the percentage of live coral cover at dynamite fishing locations (DF1, DF2) compared to control locations (K1, K2). In addition, the highest average values of coral fish abundance were found at locations K1, DF1, and DF2. Conversely, the results of the analysis found the lowest fish abundance at the K2 location. Different from the average number of reef fish species that were higher at the control location (K1, K2) compared to dynamite fishing locations (DF1, DF2). For the target fish biomass there is no real difference between the control location and dynamite fishing.

scholarly journals Pengenalan Wajah Manusia berbasis Algoritma Local Binary Pattern

2017 ◽  
Vol 17 (2) ◽  
pp. 29-38
Author(s):  
Ratih Purwati ◽  
Gunawan Ariyanto
Keyword(s):  
Computer Vision ◽  
Support Vector Machine ◽  
Face Recognition ◽  
Local Binary Pattern ◽  
Cross Validation ◽  
Support Vector ◽  
Fold Cross Validation

Face Recognition merupakan teknologi komputer untuk mengidentifikasi wajah manusia melalui gambar digital yang tersimpan di database. Wajah manusia dapat berubah bentuk sesuai dengan ekspresi yang dimilikinya. Wajah manusia dapat berubah bentuk sesuai dengan eskpresi yang dimilikinya. Ekspresi wajah manusia memiliki kemiripan satu sama lain sehingga untuk mengenali suatu ekspresi adalah kepunyaan siapa akan sedikit sulit. Pengenalan wajah terus menjadi topik aktif di zaman sekarang pada penelitian bidang computer vision. Penggunaan wajah manusia sering kita jumpai pada fitur-fitur aplikasi media sosial seperti Snapchat, Snapgram dari Instagram dan banyak aplikasi sosial media lainnya yang menggunakan teknologi tersebut. Pada penelitian ini dilakukan analisa pengenalan ekpresi wajah manusia dengan pendekatan fitur alogaritma Local Binary Pattern dan mencari pengembangan alogaritma dasar Local Binary Pattern yang paling optimal dengan cara menggabungkan metode Hisogram Equalization, Support Vector Machine, dan K-fold cross validation sehingga dapat meningkatkan pengenalan gambar wajah manusia pada hasil yang terbaik. Penelitian ini menginput beberapa database wajah manusia seperti JAFFE yang merupakan gambar wajah manusia wanita jepang yang berjumlah 10 orang dengan 7 ekspresi emosional seperti marah, sedih, bahagia, jijik, kaget, takut dan netral ke dalam sistem. YALE yaitu merupakan gambar wajah manusia orang Amerika. Serta menggunakan dataset CALTECH yang merupakan gambar manusia yang terdiri dari 450 gambar dengan ukuran 896 x 592 piksel dan disimpan dalam format JPEG. Kemudian data tersebut di sesuaikan dengan bentuk tekstur wajah masing-masing. Dari hasil penggabungan ketiga metode diatas dan percobaan-percobaan yang sudah dilakukan, didapatkan hasil yang paling optimal dalam pengenalan wajah manusia yaitu menggunakan dataset JAFFE dengan resolusi 92 x 112 piksel dan dengan tingkat penggunaan processor yang tinggi dapat mempengaruhi waktu kecepatan komputasi dalam proses menjalankan sistem sehingga menghasilkan prediksi yang lebih tepat.

scholarly journals Status of Coral Reefs in the Water of Spermonde, Makassar, South Sulawesi

2021 ◽  
Vol 324 ◽  
pp. 03007
Author(s):  
Ni Wayan Purnama Sari ◽  
Rikoh Manogar Siringoringo ◽  
Muhammad Abrar ◽  
Risandi Dwirama Putra ◽  
Raden Sutiadi ◽  
...  
Keyword(s):  
Coral Reef ◽  
Coral Reefs ◽  
Coral Cover ◽  
Public Awareness ◽  
Live Coral ◽  
Health Index ◽  
Point Count ◽  
Live Coral Cover ◽  
Reef Health ◽  
Index Value

Observations of the condition of coral reefs have been carried out in Spermonde waters from 2015 to 2018. The method used in this observation uses Underwater Photo Transect (UPT), and the data obtained is analyzed using CPCe (Coral Point Count with Excel Extensions) software. The results show that the percentage of coral cover has increased from year to year. The percentage of live coral cover in 2015 was 19.64%, 23.60 in 2016, 23.72% in 2017, and 27.83% in 2018. The increase in live coral cover from year to year is thought to occur due to the availability of nutrients. or increasing public awareness, considering this location is one of the most famous tourist attractions in Makassar. Coral reef health index values can be used to classify coral reef health. Through the analysis of the coral reef health index, an index value of 4 was obtained, which means that the condition of the coral reefs is in the “moderate” category.

scholarly journals KEPADATAN MEGABENTOS PADA EKOSISTEM TERUMBU KARANG DI PERAIRAN DESA PERLANG BANGKA TENGAH, BANGKA BELITUNG

2019 ◽  
Vol 13 (2) ◽  
pp. 173-177
Author(s):  
Arham Hafidh Akbar ◽  
Sudirman Adibrata ◽  
Wahyu Adi
Keyword(s):  
Coral Reef ◽  
Data Collection ◽  
Coral Cover ◽  
Live Coral ◽  
Coral Reef Ecosystems ◽  
Tridacna Maxima ◽  
Belt Transect ◽  
Tridacna Gigas ◽  
Drupella Cornus ◽  
Diadema Setosum

This study aims to analyze the density of megabenthos in coral reef ecosystems in the waters of Perlang Village. This research was conducted in November 2019 in the waters of Perlang Village with the megabentos data collection method using the Bentos Belt Transect (BBT) method based on COREMAP CTI LIPI (2017) with 5 data collection stations. The results found 603 individuals consisting of 9 species from 4 megabenthos families in coral reef ecosystems. Species found at the study site are Diadema setosum, Diadema antillarium (Familli Deadematidae), Drupella cornus, Drupella rugosa (Family Murcidae), Trochus sp, Trochus conus, Tectus pyramis (Family Trochidae), Tridacna gigas, and Tridacna maxima (Family Tridacnidae) . The highest attendance percentage of all stations was obtained by Diadema setosum of 47.93% (289 people). Percentage of live coral cover from 5 observation stations ranged from 57.44% - 91.78%. Observation pensions that received the highest percentage of cover values ​​were at pension 2 with 91.78% in the very good category.

scholarly journals Research on Parallel Support Vector Machine Based on Spark Big Data Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yao Huimin
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Big Data ◽  
Support Vector Machines ◽  
Cross Validation ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Lambda Architecture ◽  
Vector Machines ◽  
Data Platform

With the development of cloud computing and distributed cluster technology, the concept of big data has been expanded and extended in terms of capacity and value, and machine learning technology has also received unprecedented attention in recent years. Traditional machine learning algorithms cannot solve the problem of effective parallelization, so a parallelization support vector machine based on Spark big data platform is proposed. Firstly, the big data platform is designed with Lambda architecture, which is divided into three layers: Batch Layer, Serving Layer, and Speed Layer. Secondly, in order to improve the training efficiency of support vector machines on large-scale data, when merging two support vector machines, the “special points” other than support vectors are considered, that is, the points where the nonsupport vectors in one subset violate the training results of the other subset, and a cross-validation merging algorithm is proposed. Then, a parallelized support vector machine based on cross-validation is proposed, and the parallelization process of the support vector machine is realized on the Spark platform. Finally, experiments on different datasets verify the effectiveness and stability of the proposed method. Experimental results show that the proposed parallelized support vector machine has outstanding performance in speed-up ratio, training time, and prediction accuracy.

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