Shrimp and Fish Catch Landing Trends in Songkhla Lagoon, Thailand During 2003-2016

Hue, Ha Thi

doi:10.15666/aeer/1603_30613078

Cited by 18 publications

(11 citation statements)

References 22 publications

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“…Modeling to predict the number of new cases in the next days is one way that reveals the trend of disease. Artificial Neural Networks (ANN) and Auto-Regressive Integrated Moving Average (ARIMA) are the two most popular class of models for trend modeling and predicting time-series data ( 17 ) Although ARIMA has been in use for forecasting infectious disease since past years ( 18 ), ANN has been recently known as powerful nonlinear regression techniques( 19 ); and due to its ability for time series forecasting, it has been widely applied ( 20 ). ANN is a member of machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…There are many studies confirmed the superior performance of machine learnig algorithms in comparison to more customary models ( 21 ). However, none of ARIMA and ANN has been definitively proven to be more precise than the other for different medical fields; and therefore studies continue to compare them ( 17 ). This comparison is also continued in this study to determine the most accurate model for forecasting the spreading trend of Coronavirus.…”

Section: Introductionmentioning

confidence: 99%

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Exponentially Increasing Trend of Infected Patients with COVID-19 in Iran: A Comparison of Neural Network and ARIMA Forecasting Models

Moftakhar

Seif

Safe

2020

ijph

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Background: The outbreak of COVID-19 is rapidly spreading around the world and became a pandemic disease. For help to better planning of interventions, this study was conducted to forecast the number of daily new infected cases with COVID-19 for next thirty days in Iran. Methods: The information of observed Iranian new cases from 19th Feb to 30th Mar 2020 was used to predict the number of patients until 29th Apr. Artificial Neural Networks (ANN) and Auto-Regressive Integrated Moving Average (ARIMA) models were applied for prediction. The data was prepared from daily reports of Iran Ministry of Health and open datasets provided by the JOHN Hopkins. To compare models, dataset was separated into train and test sets. Mean Squared Error (MSE) and Mean Absolute Error (MAE) was the comparison criteria. Results: Both algorithms forecasted an exponential increase in number of newly infected patients. If the spreading pattern continues the same as before, the number of daily new cases would be 7872 and 9558 by 29th Apr, respectively by ANN and ARIMA. While Model comparison confirmed that ARIMA prediction was more accurate than ANN. Conclusion: COVID-19 is contagious disease, and has infected many people in Iran. Our results are an alarm for health policy planners and decision-makers, to make timely decisions, control the disease and provide the equipment needed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exponentially Increasing Trend of Infected Patients with COVID-19 in Iran: A Comparison of Neural Network and ARIMA Forecasting Models

Moftakhar

Seif

Safe

2020

ijph

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“…About 13.75% fishing zone has been encroached enclosures '(Gherries), reduction in flushing flow frequencies by diverting inland flow and changing the hydrologic regimes (Gobkund cut), over exploited aqua catch with inappropriate fishing gears, more motorized boats for ecotourism; restricting fish migration; and finally ambient water quality deterioration need to be attended. Knowledge of ambient living requirements like seasonality, salinity gradient, water exchange, phytoplankton expanse and anthropogenic activities is needed along with forecasting play important role in fisheries and fish management as they institute the various steps to build strategic decision, (Stergiou, et al [52] and Hue, et al [53]). A major portion of the lagoon is encroached by human habitation, tourism, prawn farms (≈ more than 15%).…”

Section: Discussionmentioning

confidence: 99%

Prediction by Soft Computing, Planning, and Strategy Building of Aquatic Catch: Chilika Lagoon, Odisha, India

Mishra

Ojha

2021

ARRB

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Introduction: The Chilika lagoon in south Odisha, India was ecologically degraded from 1985 onwards by reduction of its aquatic (fish + prawn + shrimp) catches along with reduction in salinity, hydraulic regime, water exchange, aquatic weeds invasion, and sediment influx. The aquatic catch was 8669MT in year 1985-1986 gradually reduced to 1274MT during 1995-1996 from Odisha Fisheries Dept. records which resulted in poor economic condition of ≈0,2million fishermen and they migrated to adopt other livelihood. One direct tidal inlet dredged (Sipakuda) and Naraj barrage in the apex of South Mahanadi Delta were the major hydraulic interventions made to regain hydraulic regime. After the hydraulic interventions, the eco system restored, and the aquatic catch surged but it was insufficient to livelihood sustenance for the fishermen community of the Chilika, so that they are forced for alternate occupation and migration. Methodologies: Fish catch data collected for 30 years and soft computing models linear regression, Multi Linear Perception (ANN), SMOorg (SVM) and the Random Forest algorithms (Weka Software) are used to predict the fish catch data of the lagoon for coming decade from 2020 to 2030. The effects of major hydraulic interventions are analyzed and the soft computing method of the fish and shrimp catch prediction of the Chilika has been attempted for the first time except some statistical approaches. Results: The Random Forest is found to be the preferred algorithm followed by the MLP model. The amount of catch remained around 12-13TMT if the variables and the present status of the lagoon is maintained. The combined effect of the Sipakuda Tidal inlet and the effective operation of the Naraj barrage have maintained the sustainable aqua catch. The present study shall be an immense help for the lake users and policy makers to augment aquatic catch, and alternate livelihood fishers community of the Chilika lagoon.

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“…for disease management studies 33,34 . Previous study revealed that ARIMA is one of the most suitable models as it has higher fitting and forecasting accuracy 35,36 . In recent years, it is also an useful model for predicting the incidence of infectious disease 20 The prediction indicates an upward trend for daily total confirmed cases along with total deaths.…”

Section: Discussionmentioning

confidence: 99%

“…The estimation of infectious disease like COVID-19 management by developing hypothesis for interpreting the observed situation can be done via time series analysis 32 .Time series health researchers widely use ARIMA model because of the importance of ‘time’ for disease management studies 33,34 . Previous study revealed that ARIMA is one of the most suitable models as it has higher fitting and forecasting accuracy 35,36 . In recent years, it is also an useful model for predicting the incidence of infectious disease 20 .…”

Section: Discussionmentioning

confidence: 99%

Forecasting the spread of COVID-19 pandemic in Bangladesh using ARIMA model

Kundu¹,

Ferdous²,

Islam³

et al. 2020

Preprint

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Background: COVID-19 is one of the most serious global public health threats creating an alarming situation. Therefore, there is an urgent need for investigating and predicting COVID-19 incidence to control its spread more effectively. This study aim to forecast the expected number of daily total confirmed cases, total confirmed new cases, total deaths and total new deaths of COVID-19 in Bangladesh for next 30 days. Methods: The number of daily total confirmed cases, total confirmed new cases, total deaths and total new deaths of COVID-19 from 8 March 2020 to 16 October, 2020 was collected to fit an Autoregressive Integrated Moving Average (ARIMA) model to forecast the spread of COVID-19 in Bangladesh from 17th October 2020 to 15th November 2020. All statistical analyses were conducted using R-3.6.3 software with a significant level of p< 0.05. Results: The ARIMA (0,2,1) and ARIMA (0,1,1) model was adopted for forecasting the number of daily total confirmed cases, total deaths and total confirmed new cases, new deaths of COVID-19, respectively. The results showed that an upward trend for the total confirmed cases and total deaths, while total confirmed new cases and total new death, will become stable in the next 30 days if prevention measures are strictly followed to limit the spread of COVID-19. Conclusions: The forecasting results of COVID-19 will not be dreadful for upcoming month in Bangladesh. However, the government and health authorities should take new approaches and keep strong monitoring of the existing strategies to control the further spread of this pandemic.

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Shrimp and Fish Catch Landing Trends in Songkhla Lagoon, Thailand During 2003-2016

Cited by 18 publications

References 22 publications

Exponentially Increasing Trend of Infected Patients with COVID-19 in Iran: A Comparison of Neural Network and ARIMA Forecasting Models

Exponentially Increasing Trend of Infected Patients with COVID-19 in Iran: A Comparison of Neural Network and ARIMA Forecasting Models

Prediction by Soft Computing, Planning, and Strategy Building of Aquatic Catch: Chilika Lagoon, Odisha, India

Forecasting the spread of COVID-19 pandemic in Bangladesh using ARIMA model

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