Erico N. de Souza

Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning

A key challenge in contemporary ecology and conservation is the accurate tracking of the spatial distribution of various human impacts, such as fishing. While coastal fisheries in national waters are closely monitored in some countries, existing maps of fishing effort elsewhere are fraught with uncertainty, especially in remote areas and the High Seas. Better understanding of the behavior of the global fishing fleets is required in order to prioritize and enforce fisheries management and conservation measures worldwide. Satellite-based Automatic Information Systems (S-AIS) are now commonly installed on most ocean-going vessels and have been proposed as a novel tool to explore the movements of fishing fleets in near real time. Here we present approaches to identify fishing activity from S-AIS data for three dominant fishing gear types: trawl, longline and purse seine. Using a large dataset containing worldwide fishing vessel tracks from 2011–2015, we developed three methods to detect and map fishing activities: for trawlers we produced a Hidden Markov Model (HMM) using vessel speed as observation variable. For longliners we have designed a Data Mining (DM) approach using an algorithm inspired from studies on animal movement. For purse seiners a multi-layered filtering strategy based on vessel speed and operation time was implemented. Validation against expert-labeled datasets showed average detection accuracies of 83% for trawler and longliner, and 97% for purse seiner. Our study represents the first comprehensive approach to detect and identify potential fishing behavior for three major gear types operating on a global scale. We hope that this work will enable new efforts to assess the spatial and temporal distribution of global fishing effort and make global fisheries activities transparent to ocean scientists, managers and the public.

Knowledge-based clustering of ship trajectories using density-based approach

Maritime traffic monitoring is an important aspect of safety and security, particularly in close to port operations. While there is a large amount of data with variable quality, decision makers need reliable information about possible situations or threats. To address this requirement, we propose extraction of normal ship trajectory patterns that builds clusters using, besides ship tracing data, the publicly available International Maritime Organization (IMO) rules. The main result of clustering is a set of generated lanes that can be mapped to those defined in the IMO directives. Since the model also takes non-spatial attributes (speed and direction) into account, the results allow decision makers to detect abnormal patterns - vessels that do not obey the normal lanes or sail with higher or lower speeds.

Network traffic classification using AdaBoost Dynamic

Accurate traffic classification and identification is of paramount importance for proper network management and control in both edge and backbone networks. The use of Machine Learning (ML) algorithms has been gaining popularity due to its widespread availability and to its somewhat straightforward application to Internet traffic. This work focus on a specific case of using ML algorithms for network traffic classification. We introduce AdaBoost Dynamic with Logistic Function (AB-DL), an extension of AdaBoost. M1, that combines various classifiers to improve the final hypothesis. We carefully choose parameters from the flow records traces to improve the accuracy of the algorithms. Tests were executed with a publicly available data set from Ground Truth, and the other simulation was executed in a data set generated from University, that is not public. Results show that AB-DL achieve accuracy of 93% and 98.1%, respectively from each data set.

Identifying fishing activities from AIS data with Conditional Random Fields

Fishing activity detection is important for fishery management to maintain abundant oceans. This paper presents a novel approach to identifying fishing activities from Automatic Identification System (AIS) data using Conditional Random Fields (CRFs). CRFs are popular for solving structured prediction problems such as sequence labeling in natural language processing. To model the conditional probability distributions that can identify fishing activities of the vessel points, we treat attributes of vessel points as observed variables and the fishing and non-fishing labels as hidden variables. We present three experiments and two comparisons to demonstrate the stability and effectiveness of the resulting models.

A Density-Penalized Distance Measure for Clustering

Measure of similarity between objects plays an important role in clustering. Most of the clustering methods use Euclidean metric as a measure of distance. However, due to the limitations of the partitioning clustering methods, another family of clustering algorithms called density-based methods has been developed. This paper introduces a new distance measure that equips the distance function with a density-aware component. This distance measure, called Density-Penalized Distance (DPD), is a regularized Euclidean distance that adds a penalty term to Euclidean distance based on the difference between the densities around the two points. The intuition behind the idea is that if the densities around two points differ from each other, they are less likely to belong to same cluster. A new point density estimation method, an analysis on the computational complexity of the algorithm in addition to theoretical analysis of the distance function properties are also provided in this work. Experiments were conducted in five different clustering algorithms and the results of DPD are compared with that obtained by using three other standard distance measures. Nine different UCI datasets were used for evaluation. The results show that the performance of DPD is significantly better or at least comparable to the classical distance measures.

Fishing Activity Detection from AIS Data Using Autoencoders

Marine life has significant impact on our planet, providing food, oxygen and biodiversity. But, 90 percent of the large fish are gone primarily because of overfishing, according to the 2010 Census of Marine Life. Thus it is desirable to detect fishing activities in the ocean. Satellite AIS Automatic Identification System is a vessel identification system that monitors the position of ships worldwide for collision avoidance, allowing us to track vessels on the ocean. AIS equipment is required to be fitted aboard international voyaging ships that are 300 tons or above, and all passenger ships.

Improvements to Boosting with Data Streams

Data Streams (DS) pose a challenge for any machine learning algorithm, because of high volume of data - on the order of millions of instances for a typical data set. Various algorithms were proposed, in particular, OzaBoost - a parallel adaptation of AdaBoost - creates various “weak” learners in parallel and updates each of them with new instances during training. At any moment, OzaBoost can stop and output the final model. OzaBoost suffers with memory consumption, which avoids its use for certain types of problems. This work introduces OzaBoost Dynamic, which changes the weight calculation and the number of boosted “weak” learners used by OzaBoost to improve its performance in terms of memory consumption. This work presents the empirical results showing the performance of all algorithms using data sets with 50 and 60 million instances.

Improving point-based AIS trajectory classification with partition-wise gated recurrent units

We present Partition-wise Gated Recurrent Units (pGRUs) for point-based trajectory classification to detect real-world trawler fishing activities in the ocean. We propose partition-wise activation functions integrated with Gated Recurrent Units that partitions each feature and uses independent parameters to model distinct regions of the feature space. This approach enables us to leverage the benefits of deep learning on a low-dimensional and heterogeneous feature space by mapping the low-dimensional features into another space that can be better separated with piece-wise learnable parameters. We show that the proposed partition-wise activation functions can approximate a wide array of functions, including conventional activations such as sigmoid and hyperbolic tangent. Our experimental results demonstrate that pGRU learns the probability distribution from data and achieves substantial improvements over the state-of-the-art systems on the task of trawler fishing activity detection.

Traffic classification with on-line ensemble method

Traffic classification helps network managers to control services and activities done by users. Traditionally, Machine Learning (ML) is a tool to help managers to detect applications most used, and offer different types of services to their clients. Most of ML algorithms are designed to deal with limited amount of data, and in network context this is a problem, because of large data volume, speed and diversity. More recent work try to solve this issue by using ML algorithms developed to work with data streams, but they tend to implement only Very Fast Decision Trees (VFDT). This work goes in a different direction by proposing to use Ensemble Learners (EL), which, theoretically, offer more capability to deal with non-linear problems. The paper proposes to use a new EL called OzaBoost Dynamic (OzaDyn), and compares its performance with other ensemble methods designed to deal with data streams. Results indicate that the accuracy performance of OzaDyn is equal to other ensemble methods, while it helps reduce the memory consumption and time to evaluate the models.

Correction: Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning.

[This corrects the article DOI: 10.1371/journal.pone.0158248.].