Multiple Water-Level Seawater Temperature Prediction Method for Marine Aquaculture

2018 ◽  
pp. 366-371
Author(s):  
Takanobu Otsuka ◽  
Yuji Kitazawa ◽  
Takayuki Ito
Keyword(s):  
Water Level ◽  
Seawater Temperature ◽  
Prediction Method ◽  
Temperature Prediction ◽  
Marine Aquaculture

scholarly journals Seawater Temperature Prediction Method for Sustainable Marine Aquaculture

2017 ◽  
Author(s):  
Takanobu Otsuka ◽  
Yuji Kitazawa ◽  
Takayuki Ito
Keyword(s):  
Seawater Temperature ◽  
Prediction Method ◽  
Value Added ◽  
Temperature Data ◽  
Weather Data ◽  
Marine Resources ◽  
Temperature Prediction ◽  
Marine Aquaculture ◽  
Value Added Products

Aquaculture is growing ever more important due to the decrease in natural marine resources and increase inworldwide demand. To avoid losses due to aging and abnormalweather, it is important to predict seawater temperature in order to maintain a more stable supply, particularly for high value added products, such as pearls and scallops. The increase in species extinction is a prominent societal issue. Furthermore, in order to maintain a stable quality of farmed fishery, water temperature should be measured daily and farming methods altered according to seasonal stresses. In this paper, we propose an algorithm to estimate seawater temperature in marine aquaculture by combining seawater temperature data and actual weather data.

scholarly journals How to Predict Seawater Temperature for Sustainable Marine Aquaculture (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13887-13888
Author(s):  
Masahito Okuno ◽  
Takanobu Otsuka
Keyword(s):  
Neural Networks ◽  
Seawater Temperature ◽  
Prediction Method ◽  
Accurate Prediction ◽  
Average Error ◽  
Marine Aquaculture ◽  
Global Demand ◽  
Environment Control ◽  
Marine Products ◽  
Using Data

The increasing global demand for marine products has turned attention to marine aquaculture. In marine aquaculture, appropriate environment control is important for a stable supply. The influence of seawater temperature on this environment is significant and accurate prediction is therefore required. In this paper, we propose and describe the implementation of a seawater prediction method using data acquired from real aquaculture areas and neural networks. Our evaluation experiment showed that hourly next-day prediction has an average error of about 0.2 to 0.4 ◦C and daily prediction of up to one week has an average error of about 0.2 to 0.5 ◦C. This is enough to meet actual worker need, which is within 1 ◦C error, thus confirming that our seawater prediction method is suitable for actual sites.

scholarly journals A Top-oil Temperature Prediction Method for the Split Transformer Based on PSOGSA

2018 ◽  
Vol 366 ◽  
pp. 012060
Author(s):  
Zi Xin ◽  
Bengang Wei ◽  
Yongliang Liang ◽  
Yanshun Xu ◽  
Ruochen Guo ◽  
...  
Keyword(s):  
Prediction Method ◽  
Temperature Prediction

Rapid temperature prediction method for electronic equipment cabin

2018 ◽  
Vol 138 ◽  
pp. 83-93 ◽  
Author(s):  
Hongquan Qu ◽  
Shuo Fu ◽  
Liping Pang ◽  
Chen Ding ◽  
Helin Zhang
Keyword(s):  
Prediction Method ◽  
Electronic Equipment ◽  
Temperature Prediction ◽  
Rapid Temperature

Soil Temperature Prediction on Farmland Water Level and Environmental Factors Paddy Based on BP-ANN Model

2012 ◽  
Vol 518-523 ◽  
pp. 4247-4252
Author(s):  
Meng Hua Xiao ◽  
Shuan Gen Yu ◽  
Chun Xiao Fu ◽  
Li Li Kong ◽  
Jun Wang ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Soil Temperature ◽  
Water Level ◽  
Predictive Value ◽  
Total Solar Radiation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Level Control ◽  
Temperature Prediction ◽  
Drought Treatment

The main environmental factors of the influence of soil temperature are air temperature, total solar radiation, effective photosynthetic radiation, wind speed and relative humidity, while the farmland water and submerging duration are the main factors affecting the change of soil temperature, and it exist complex non linear relationship between them. The aim of this study was constructed the soil temperature prediction model on farmland water level and environmental factors and obtained paddy soil temperature predictive value by using the data of existing water level and environmental factors. For the flooding treatment, the minimum and maximum soil temperature appeared at 7:00 and 18:00, for the drought treatment the minimum and maximum soil temperature respectively appeared at 6:00 and 14:00. This study trained artificial neural network based on back propagation algorithm (BP-ANN) through the existing data to predict the four characteristics the soil temperature of different water level control so as to obtain the soil temperature amplitude. Results showed that there was certain deviation between the predictive value and the actual value at the four characteristic moments, relative error were 1.19%, 1.34%, 2.09% and 1.07%, the predicted outcome was satisfactory. It is significant for guiding the rice irrigation and the production of practice facilitated.

Intensity Based Simulation of the Temperature Prediction in the Focal Region of Liver Using MRI-Guided High Intensity Focused Ultrasound (HIFU)

2016 ◽  
Vol 13 (10) ◽  
pp. 6728-6732
Author(s):  
P Revathy ◽  
V Sadasivam ◽  
T. Ajith Bosco Raj
Keyword(s):  
Focused Ultrasound ◽  
Treatment Time ◽  
Prediction Method ◽  
Treatment Temperature ◽  
High Intensity Focused Ultrasound ◽  
Bioheat Transfer ◽  
Transfer Model ◽  
Focal Region ◽  
Temperature Elevation ◽  
Temperature Prediction

In this research paper a new temperature prediction method is proposed to predict the temperature in liver during thermal ablation which also takes in to account the blood flow cooling. The proposed method suggest a modification of Pennes bioheat transfer equation (PBHTE) inorder to more accurately predict the treatment temperature. The temperature elevation by the proposed heat transfer model is compared with the PBHTE model and the other two heat continuum models by Wulff and Klinger. Appropriate temperature prediction is useful in treatment planning. This may reduce the recurrence level of cancer. Further the reduction in treatment time increases patient safety.

Development of Prediction Method for Engine Compartment Water Level by Using Coupled Multibody and Fluid Dynamics

2017 ◽  
Vol 10 (2) ◽  
pp. 514-524 ◽  
Author(s):  
Yoshiteru Tanaka ◽  
Jun Yamamura ◽  
Atsushi Murakawa ◽  
Hiroshi Tanaka ◽  
Tsuyoshi Yasuki
Keyword(s):  
Fluid Dynamics ◽  
Water Level ◽  
Prediction Method ◽  
Engine Compartment
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