Intelligent aquaculture system for pisciculture simulation using deep learning algorithm

Sherwin B. Sapin, Bryan A. Alibudbud, Paulo B. Molleno, Maureen B. Veluz, Jonardo R. Asor


The project aims to develop an intelligent system for simulating pisciculture in Taal Lake in the Philippines through geographical information system and deep learning algorithm. Records of 2018-2020 from the database of Bureau of fisheries and aquatic resources IV-A-protected area management board (BFAR IVA-PAMB) was collected for model development. Deep learning algorithm model was developed and integrated to the system for time series analysis and simulation. Different technologies including tensorflow.js were used to successfully developed the intelligent system. It is found on this paper that recurrent neural network (RNN) is a good deep learning algorithm for predicting pisciculture in Taal lake. Further, it is also shown in the initial visualization of the system that barangay Sampaloc in Taal has highest rate of fish production in Taal while Tilapia nilotica sp. is the major product of the latter.


Aquaculture; Deep learning algorithm; Geographic information system; Lake simulation; Recurrent neural network; Taal lake;

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