A Data Mining Based Approach for Determining the Potential Fishing Zones

Abstract

Abstract-The aim of this paper is to analyze the determination of the potential fishing zones based on data mining approach. The algorithm utilized in this study is AGRID+, a grid density based clustering for high dimensional data. The case study area is in eastern Indian Ocean located at 16.56 -2 S and 100.49 -140 E. The algorithm is implemented in 7 phases, partitioning, computing distance threshold, calculating densities, compensating densities, calculating density threshold, clustering and removing noise. The clustering result is evaluated by the Silhouette index. The results of the study show that the best cluster formed at daily aggregate temporal with number of cell (m) = 14 and the number of cluster formed was 50 clusters. The constant execution time is in line with the increasing the value of m. From three different temporal aggregate, the daily aggregate is running relatively constant for various m value. To determine the potential fishing zones for different temporal aggregate can be achieved by applying the thresholding technique to the cluster result. Utilizing the data mining approach yielded a prominent 22 daily clusters identified as potential fishing zone.

Oceanographic features selection to predict the tuna potential fishing zones using SFFS method

“…The ocean current data contains of two different surface current wind direction and its resultants (r), which are based on meridional (u) and zonal (v) wind direction. PFZ ground truth obtained by cluster the fish catch data using A Spatio Temporal Grid Density Based (ST- Figure 1: Framework of Feature Selection on Oceanographic Data AGRID) clustering algorithm [3], [4]. The oceanographic feature and ground truth are combined based on its spatial location (longitude and latitude) and time using multiple data record linkage method [12].…”

“…To be able to maintain the tuna fishing activities, many studies are proposed to assist the fishermen to find the tuna potential fishing zones (PFZ). Starting from the fishing management study [2], the prediction of tuna potential fishing zone (PFZ) [3], [4] and also the study of oceanographic parameter that affect the prediction of tuna PFZ [5].…”

Implementation of Data Mining Analysis to Determine the Tuna Fishing Zone Using DBSCAN Algorithm

“…The next study related to the determination of tuna fishing areas is D. Fitrianah, H. Fahmi, A. N. Hidayanto, and A. M. Arymurthy [17] who also discussed the determination of tuna fishing areas, but in the journal discusses about Potential Fishing Zone's and in this study discusses the Spatial Areas of Tuna Fishing. It almost looks similar, but in this study discusses Spatial, while in the journal, the study discusses Temporal although there are several similar stages but the results of this study are very different of each other.…”

Synchronization of Catch Fish Data in Fisheries e-Logbook with a Vessel Monitoring System

“…In article [10], elogbook was used in medical for recoding patient record. Related to Unix Timestamp in article [11] comparing it with UTC and date time, and for fisheries area, articles in [12], [13] are research related to it.…”