Impact on fishing patterns and life style changes of kanyakumari fishermen due to fading potential fishing zones

Ravindran, N.; Samraj, S. A.; Kavitha, C.

doi:10.1109/icghpc.2013.6533917

Cited by 3 publications

(5 citation statements)

References 5 publications

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“…In 2013, Ravindran et al [3] utilize the works as Jagannathan's did (distance-depth aspects), to see whether there is any impact on fishing patterns and lifestyle changes. Their works included fuzzy c-means clustering, and the data is clustered into two: summer and winter.…”

Section: A Related Work Regarding To Potential Fishing Zonementioning

confidence: 99%

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Feature Exploration for Prediction of Potential Tuna Fishing Zones

Fitrianah¹

2015

IJIEE

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Prediction for potential fishing zone is one of the important activities concerning for the tuna fishing exploration, conservation and management. Accurate prediction will give more efficient in fishing activities. One of the way to predict is the classification techniques. Currently, as the state of the art, most of the methods utilize the chlorophyll and SST features. However, there are still other parameters that can be utilized. In this paper, the other parameters are then observed: ocean currents and salinity feature. First the results shows that, taking a part of ocean currents together with the chlorophyll and SST feature combination gives the improvement on the prediction. On other hand, this ocean currents feature is then substituted with the salinity, and the result shows that the combination between salinity, chlorophyll, and SST also increases the result. Finally, the ocean current and salinity parameters are combined together with chlorophyll and SST parameters and the result was surprising. It is found that the last feature combination which includes Chlorophyll, SST, Ocean current and salinity gives the highest result in classification (in Naï ve Bayes reaches 69.03%, Decision Tree reaches 82.32% and SVM reaches 68.30% of accuracy) compared to the "baseline" feature combination including only Chlorophyll and SST (in Naï ve Bayes reaches 57.44%, Decision Tree reaches 58.91% and SVM reaches 56.74% of accuracy). Therefore it is suggested that the proposed feature can be harnessed for the better prediction of potential fishing zone.

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Section: A Related Work Regarding To Potential Fishing Zonementioning

confidence: 99%

“…However there is still a problem in tuna fish catching, regarding to the area that a fisherman should visit. The way to determine which trip that the fisherman should choose, however needs the good prediction of potential fishing zone [2], [3]. The prediction itself can be performed in many ways.…”

Section: Introductionmentioning

confidence: 99%

Feature Exploration for Prediction of Potential Tuna Fishing Zones

Fitrianah¹

2015

IJIEE

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“…The literature survey ensures that many fish catch prediction methods, such as purse-seine, beach seine and long line, are suitable only when the fish caught is in tons. Hence, in this work, fishing zone prediction and fish classification are proposed through machine learning algorithms using an automated 'FishID-AUV' [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Integrating Edge-Intelligence in AUV for Real-Time Fish Hotspot Identification and Fish Species Classification

Sowmmiya,

Roselyn,

Sundaravadivel

2024

Information

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Enhancing the livelihood environment for fishermen’s communities with the rapid technological growth is essential in the marine sector. Among the various issues in the fishing industry, fishing zone identification and fish catch detection play a significant role in the fishing community. In this work, the automated prediction of potential fishing zones and classification of fish species in an aquatic environment through machine learning algorithms is developed and implemented. A prototype of the boat structure is designed and developed with lightweight wooden material encompassing all necessary sensors and cameras. The functions of the unmanned boat (FishID-AUV) are based on the user’s control through a user-friendly mobile/web application (APP). The different features impacting the identification of hotspots are considered, and feature selection is performed using various classifier-based learning algorithms, namely, Naive Bayes, Nearest neighbors, Random Forest and Support Vector Machine (SVM). The performance of classifications are compared. From the real-time results, it is clear that the Naive Bayes classification model is found to provide better accuracy, which is employed in the application platform for predicting the potential fishing zone. After identifying the first catch, the species are classified using an AlexNet-based deep Convolutional Neural Network. Also, the user can fetch real-time information such as the status of fishing through live video streaming to determine the quality and quantity of fish along with information like pH, temperature and humidity. The proposed work is implemented in a real-time boat structure prototype and is validated with data from sensors and satellites.

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“…e yearly marine sheries creation in India is about 2.94 million tons against the harvestable capability of 3.93 million tons [1]. However, there is still a problem in nding the shing areas that shermen should visit [2,3]. Prediction of shing zone has been done utilizing the sea parameters derived either from satellite images or ground truth primary data [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…The yearly marine fisheries creation in India is about 2.94 million tons against the harvestable capability of 3.93 million tons [ 1 ]. However, there is still a problem in finding the fishing areas that fishermen should visit [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

HE-DFNETS: A Novel Hybrid Deep Learning Architecture for the Prediction of Potential Fishing Zone Areas in Indian Ocean Using Remote Sensing Images

Sivasankari

Anandan

Chamato

2022

Computational Intelligence and Neuroscience

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The Indian subcontinent is known for its larger coastline spanning, over 8100 km and is considered the habitat for many millions of people. The livelihood of their habitat is purely dependent upon the fishing activities. Often, the search for fish requires more time for catching and more resources, thus increasing the operational cost leading to low profitability. With the advent of artificial intelligence algorithms, designing intelligent algorithms for an effective prediction of fishing areas has reached new heights in terms of high accuracy (Acy) and less time. But still, predicting the location of potential fishing zones (PFZs) is always a daunting task. To reduce these issues, this work presented the novel hybrid prediction architecture of PFZs using remote sensing images. The proposed architecture integrates the deep convolutional layers and flitter bat optimized long short-term memory (FB-LSTM)-based recurrent neural networks (RNN). These convolutional layers are utilized to remove the various color features such as chlorophyll, sea surface temperature (SST), and GPS location from the satellite images, and FB-LTSM is utilized to predict the potential locations for fishing. The extensive experimentations are carried out utilizing the satellite data from Indian National Centre for Ocean Information Services (INCOIS) and implemented using TensorFlow 1.18 with Keras API. The performance metrics such as prediction Acy, precision (Pscn), recall (Rcl) or sensitivity (Sty), specificity (Sfy), and F1-score and compared with other existing intelligent learning models. From our observations, the proposed architecture (99% prediction Acy) has outperformed the other existing algorithms and finds its best place in designing an intelligent system for better predicting of PFZs.

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Impact on fishing patterns and life style changes of kanyakumari fishermen due to fading potential fishing zones

Cited by 3 publications

References 5 publications

Feature Exploration for Prediction of Potential Tuna Fishing Zones

Feature Exploration for Prediction of Potential Tuna Fishing Zones

Integrating Edge-Intelligence in AUV for Real-Time Fish Hotspot Identification and Fish Species Classification

HE-DFNETS: A Novel Hybrid Deep Learning Architecture for the Prediction of Potential Fishing Zone Areas in Indian Ocean Using Remote Sensing Images

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