scholarly journals Prediction Method for Large Diatom Appearance with Meteorological Data and MODIS Derived Turbidity and Chlorophyll-A in Ariake Bay Area in Japan

2017 ◽  
Vol 8 (3) ◽  
Author(s):  
Kohei Arai
Keyword(s):  
Chlorophyll A ◽  
Meteorological Data ◽  
Prediction Method ◽  
Ariake Bay ◽  
Bay Area ◽  
Large Diatom

scholarly journals Discovery of an early Middle Pleistocene marker tephra around the Ariake Bay area, Southwest Japan

2012 ◽  
Vol 118 (11) ◽  
pp. 709-722 ◽  
Author(s):  
Shoichi Shimoyama ◽  
Hirohisa Matsuura ◽  
Kiyohide Mizuno ◽  
Masakazu Kubota ◽  
Takenori Hino ◽  
...  
Keyword(s):  
Middle Pleistocene ◽  
Ariake Bay ◽  
Southwest Japan ◽  
Bay Area

scholarly journals Improved MODIS-Aqua Chlorophyll-a Retrievals in the Turbid Semi-Enclosed Ariake Bay, Japan

2018 ◽  
Vol 10 (9) ◽  
pp. 1335 ◽  
Author(s):  
Meng Meng Yang ◽  
Joji Ishizaka ◽  
Joaquim I. Goes ◽  
Helga do R. Gomes ◽  
Elígio de Raús Maúre ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Red Tide ◽  
Atmospheric Correction ◽  
Ariake Bay ◽  
Band Ratio ◽  
Simple Method ◽  
Chl A ◽  
Switching Algorithm ◽  
Economic Significance

The accurate retrieval of chlorophyll-a concentration (Chl-a) from ocean color satellite data is extremely challenging in turbid, optically complex coastal waters. Ariake Bay in Japan is a turbid semi-enclosed bay of great socio-economic significance, but it suffers from serious water quality problems, particularly due to red tide events. Chl-a derived from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor on satellite Aqua in Ariake Bay was investigated, and it was determined that the causes of the errors were from inaccurate atmospheric correction and inappropriate in-water algorithms. To improve the accuracy of MODIS remote sensing reflectance (Rrs) in the blue and green bands, a simple method was adopted using in situ Rrs data. This method assumes that the error in MODIS Rrs(547) is small, and MODIS Rrs(412) can be estimated from MODIS Rrs(547) using a linear relation between in situ Rrs(412) and Rrs(547). We also showed that the standard MODIS Chl-a algorithm, OC3M, underestimated Chl-a, which was mostly due to water column turbidity. A new empirical switching algorithm was generated based on the relationship between in situ Chl-a and the blue-to-green band ratio, max(Rrs(443), Rrs(448)/Rrs(547), which was the same as the OC3M algorithm. The criterion of Rrs(667) of 0.005 sr−1 was used to evaluate the extent of turbidity for the switching algorithm. The results showed that the switching algorithm performed better than OC3M, and the root mean square error (RMSE) of estimated Chl-a decreased from 0.414 to 0.326. The RMSE for MODIS Chl-a using the recalculated Rrs and the switching algorithm was 0.287, which was a significant improvement from the RMSE of 0.610, which was obtained using standard MODIS Chl-a. Finally, the accuracy of our method was tested with an independent dataset collected by the local Fisheries Research Institute, and the results revealed that the switching algorithm with the recalculated Rrs reduced the RMSE of MODIS Chl-a from 0.412 of the standard to 0.335.

scholarly journals Quick Clay Development and Cation Composition of Pore Water in Marine Sediments from the Ariake Bay Area, Japan

2014 ◽  
Vol 05 (06) ◽  
pp. 595-606 ◽  
Author(s):  
Phanny He ◽  
Masami Ohtsubo ◽  
Hiroshi Abe ◽  
Takahiro Higashi ◽  
Motohei Kanayama
Keyword(s):  
Pore Water ◽  
Marine Sediments ◽  
Ariake Bay ◽  
Cation Composition ◽  
Bay Area ◽  
Quick Clay

A study on variations in excess attenuation due to ground surface and meteorological conditions based on a long-term outdoor sound propagation experiment

2021 ◽  
Vol 263 (2) ◽  
pp. 4368-4375
Author(s):  
Takatoshi Yokota ◽  
Koichi Makino ◽  
Genki Iizumi ◽  
Takuya Tsutsumi
Keyword(s):  
Sound Propagation ◽  
Meteorological Data ◽  
Surface Condition ◽  
Ground Surface ◽  
Prediction Method ◽  
Meteorological Condition ◽  
Pulse Method ◽  
Meteorological Conditions ◽  
Ground Condition ◽  
Excess Attenuation

From the winter of 2018, outdoor sound propagation experiments (maximum horizontal range: 300 m) have been repeatedly conducted three times a day on weekdays at a glider airfield in Hokkaido, Japan. The ground condition of the experimental field is grass-covered in summer and snow-covered in winter. In each experiment, impulse responses have been measured by time-stretched pulse method and excess attenuation has been obtained at receiving points. Meteorological data at the field has been also measured. Based on the data of excess attenuation collected under various meteorological conditions over a long period, variation in sound propagation characteristics due to the differences in ground surface condition and meteorological condition has been investigated. The numerical analysis based on the GFPE method has been also carried out with changing the parameter of meteorological condition and ground surface condition. By comparing the results with the experimental data, the prediction method of the variations in excess attenuation has been also investigated.

Research on Wind Power by a Combination Prediction Method Based on Meteorological Data

2013 ◽  
Author(s):  
Li Ma ◽  
◽  
Bo Li ◽  
Zhen Bin Yang ◽  
Jie Du ◽  
...  
Keyword(s):  
Wind Power ◽  
Meteorological Data ◽  
Prediction Method

Properties of a low-swelling smectitic marine clay of interest in soil engineering

1985 ◽  
Vol 22 (2) ◽  
pp. 241-245 ◽  
Author(s):  
Masami Ohtsubo ◽  
Kazuhiko Egashira ◽  
Masateru Takayama
Keyword(s):  
Paddy Soil ◽  
Swelling Pressure ◽  
Ariake Bay ◽  
Marine Clay ◽  
Swelling Properties ◽  
Air Drying ◽  
Swelling Clay ◽  
Bay Area ◽  
Nacl Concentration ◽  
Marine Clays

Smectite is generally a high-swelling clay. However, the smectite found in marine quick clays in the Ariake Bay area of Japan is a low-swelling clay like illite and kaolinite. The low swelling properties of an Ariake marine clay are investigated here in terms of consolidation, swelling, and shrinkage characteristics. The void ratios in compression curves of soils containing sodium are lower at 0.01 N than at 1.0 N NaCl concentration, and the slopes of swelling curves are independent of salt concentration in the pore water and cation valency. These tendencies are contrary to those observed for montmorillonite and a paddy soil containing high-swelling smectite. Measurements of swelling pressure suggest that the smectite in the Ariake marine clay exhibits little intracrystalline swelling even after saturation with Na. The volume shrinkage of the Ariake marine clay by air-drying is smaller than that of the paddy soil. Key words: compressibility, marine clays, smectite, swelling.

scholarly journals RESEARCH ON PM2.5 CONCENTRATION COMBINATION FORECASTING MODEL BASED ON COR-SVM

2019 ◽  
Vol XLII-3/W9 ◽  
pp. 23-30
Author(s):  
X. Y. Feng ◽  
P. Tian ◽  
Y. J. Shi ◽  
M. Zhang
Keyword(s):  
Support Vector Machine ◽  
Linear Regression ◽  
Correlation Coefficient ◽  
Large Scale ◽  
Meteorological Data ◽  
Prediction Method ◽  
Small Sample ◽  
Multivariate Linear Regression ◽  
Support Vector ◽  
Pm2.5 Concentration

Abstract. PM2.5 is a pollutant that can enter the lungs, threatening human health and affecting people’s living and traveling. In this paper, we use multivariate linear regression, support vector machine and their combined prediction method to predict the concentration of PM2.5. It is significant for the convenience of healthy life. This paper is based on a series of meteorological data such as O3 concentration, CO concentration, SO2 concentration, PM2.5 concentration and PM10 concentration from 2014 to 2018 in Beijing. By calculating the correlation coefficient between the concentration of PM2.5 and the concentration of the other four components, the multivariate linear regression equation was fitted by using the correlation coefficient with high correlation as the factor of multiple linear regression. Then we use support vector machine regression prediction method to predict the concentration of PM2.5. The combined prediction method is obtained by weighing the two prediction results. It is found that the prediction method of support vector machine is better in dealing with large-scale and small sample data prediction, and the multi-linear fitting method is better in processing short-term prediction. The combined prediction results based on correlation coefficients combine the advantages of the two prediction methods, and the prediction results are more reasonable.

scholarly journals Ultra-Short-Term Wind Power Prediction Based on Bidirectional Gated Recurrent Unit and Transfer Learning

2021 ◽  
Vol 9 ◽  
Author(s):  
Wenjin Chen ◽  
Weiwen Qi ◽  
Yu Li ◽  
Jun Zhang ◽  
Feng Zhu ◽  
...  
Keyword(s):  
Wind Power ◽  
Transfer Learning ◽  
Wind Farm ◽  
Meteorological Data ◽  
Prediction Method ◽  
Wind Farms ◽  
Power Prediction ◽  
Short Term ◽  
Training Time ◽  
Gated Recurrent Unit

Wind power forecasting (WPF) is imperative to the control and dispatch of the power grid. Firstly, an ultra-short-term prediction method based on multilayer bidirectional gated recurrent unit (Bi-GRU) and fully connected (FC) layer is proposed. The layers of Bi-GRU extract the temporal feature information of wind power and meteorological data, and the FC layer predicts wind power by changing dimensions to match the output vector. Furthermore, a transfer learning (TL) strategy is utilized to establish the prediction model of a target wind farm with fewer data and less training time based on the source wind farm. The proposed method is validated on two wind farms located in China and the results prove its superior prediction performance compared with other approaches.

scholarly journals Research on Short-Term Wind Power Forecasting by Data Mining on Historical Wind Resource

2020 ◽  
Vol 10 (4) ◽  
pp. 1295 ◽  
Author(s):  
Bin Tang ◽  
Yan Chen ◽  
Qin Chen ◽  
Mengxing Su
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Wind Power ◽  
Meteorological Data ◽  
Prediction Method ◽  
Short Term ◽  
Elman Neural Network ◽  
Wind Power Forecasting ◽  
Interval Prediction ◽  
Power Forecasting

In order to enhance the accuracy of short-term wind power forecasting (WPF), a short-term wind power forecasting method based on historical wind resources by data mining has been designed. Firstly, the spoiled data resulting from wind turbine and meteorological monitoring equipment is eliminated, and the missing data is added by the Lomnaofski optimization model, which is based on the temporal-spatial correlation of meteorological data. Secondly, the wind characteristics are analyzed by the continuous time similarity clustering (CTSC) method, which is used to select similar samples. To improve the accuracy of deterministic prediction and prediction error, the radial basis function neural network (RBF) deterministic forecasting model was built, which can approximate nonlinear solutions. In addition, the wind power interval prediction method, combining fuzzy information granulation and an Elman neural network (FIG-Elman), is proposed to acquire forecasting intervals. The deterministic prediction of the RBF-CTSC model has high accuracy, which can accurately describe the randomness, fluctuation and nonlinear characteristics of wind speed. Additionally, the mean absolute error (MAE) and root mean square error (RMSE) are reduced by the new model. The interval prediction of FIG-Elman results show that the interval width decreased by 18.85%, and the coverage probability of interval increased by 10.94%.

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