scholarly journals Occurrence Date Range

2020 ◽  
Author(s):  
Keyword(s):  
Occurrence Date

scholarly journals ANALYSIS OF THE RED TIDE OCCURRENCE PATTERN WHEN HIGH WATER TEMPERATURE

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 267-269
Author(s):  
D. H. Hwang ◽  
S. H. Bak ◽  
U. Enkhjargal ◽  
M. J. Jeong ◽  
H. J. Yoon ◽  
...  
Keyword(s):  
Water Temperature ◽  
Red Tide ◽  
High Water ◽  
Temperature Pattern ◽  
Self Organizing Map ◽  
High Water Temperature ◽  
Fisheries Science ◽  
Jinhae Bay ◽  
Occurrence Pattern ◽  
Occurrence Date

Abstract. In 1982, the red tide caused by Cochlodimium polykrikodies occurred at first in Jinhae Bay in Korea. Since then, it causes serious fisheries damage every year. In 2018, red tide occurred when high water temperature. We analyzed red tide occurrence pattern when high water temperature. Red tide occurrence date, occurrence area caused by the Cochlodinium polykrikoides were used provided by National Institute of Fisheries Science. SST data were used the GHRSST Level 4 OSTIA data provided by NOAA. The red tide occurred July 14 to September 2 in 2013, July 29 to October 4 in 2014, August 5 to September 23 in 2015, and July 23 to August 8 in 2018 in Korea. The nearest SST data from the red tide occurrence area were extracted. When red tide occurred, SST was 22~28 °C in 2013, 23~25 °C in 2014, 21~27 °C in 2015, 26~28 °C in 2018. SST in 2013 was increasing trend and 2015 was downward trend. SST in 2018 occurred at high water temperatures above 25 °C. The spatial pattern by using the self-organizing map (3x3 map), node 4 was the highest frequency (16.9%). It is considered that it appears at the beginning and end of red tide occurrence. Node 1 was 16.3% frequency, it showed at the end of 2014 and 2015. SST of node 1 and 4 maintained 22~23 °C on the southern sea of Korea. Node 9 was 13.9%, which is the late 2013 and 2018. Node 9 showed high temperature pattern of more than 26 °C in the southern sea of Korea.

scholarly journals Adapting the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms to map rainfall-triggered landslides in the West-Cameroon highlands (Central-Africa)

2021 ◽  
Author(s):  
ALFRED HOMERE NGANDAM MFONDOUM ◽  
Pauline Wokwenmendam Nguet ◽  
Jean Valery Mefire Mfondoum ◽  
Mesmin Tchindjang ◽  
Sofia Hakdaoui ◽  
...  
Keyword(s):  
Real Time ◽  
Cox Model ◽  
Central Africa ◽  
Daily Rainfall ◽  
Landsat 8 ◽  
Best Value ◽  
Landslide Event ◽  
Recent Landslide ◽  
Dichotomous Variables ◽  
Occurrence Date

Abstract Background – NASA’s developers recently proposed the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms. This method uses the Landsat 8 satellite images and daily rainfall recordings for a real-time mapping of this geohazard. This study adapts the processing to face the issues of data quality and unavailability/gaps for the mapping of the recent landslide events in west-Cameroon’s highlands. Methods – The SLIP algorithm is adapted, by integrating the inverse NDVI to assess the soil bareness, the Modified Normalized Multi-Band Drought Index (MNMDI) combined with the hydrothermal index to assess soil moisture, and the slope inclination to map the recent landslide. Further, the DRIP algorithm uses the mean daily rainfall to assess the thresholds corresponding to the recent landslide events. Their probability density function (PDF) curves are superimposed and their intersections are used to propose sets of dichotomous variables before (1948-2018) and after the 28 October 2019 landslide event. In addition, a survival analysis is performed to correlate the occurrence date of the landslide with the rainfall since the first known event in Cameroon, through the Cox model. Results – From the SLIP model, the Landslide Hazard Zonation (LHZ) map gives an overall accuracy of 96%. Further, the DRIP model states that 6/9 ranges of probability are rainfall-triggered landslides at 99.99%, between June and October, while 3/9 ranges show only 4.88% of risk for the same interval. Finally, the survival probability for a known site is up to 0.68 for the best value and between 0.38 and 0.1 for the lowest value through time. Conclusions – The proposed approach is an alternative based on data (un)availability, completed by the site’s lifetime.

scholarly journals Inferring Temporal Knowledge for Near-Periodic Recurrent Events

2018 ◽  
Author(s):  
Dinesh Raghu ◽  
Surag Nair ◽  
Mausam .
Keyword(s):  
Recurrent Events ◽  
Inference Engine ◽  
Knowledge Bases ◽  
Recurrent Event ◽  
The Novel ◽  
Knowledge Sources ◽  
Film Festivals ◽  
Joint Inference ◽  
System Output ◽  
Occurrence Date

We define the novel problem of extracting and predicting occurrence dates for a class of recurrent events -- events that are held periodically as per a near-regular schedule (e.g., conferences, film festivals, sport championships). Knowledge-bases such as Freebase contain a large number of such recurring events, but they also miss substantial information regarding specific event instances and their occurrence dates. We develop a temporal extraction and inference engine to fill in the missing dates as well as to predict their future occurrences. Our engine performs joint inference over several knowledge sources -- (1) information about an event instance and its date extracted from text by our temporal extractor, (2) information about the typical schedule (e.g., ``every second week of June") for a recurrent event extracted by our schedule extractor, and (3) known dates for other instances of the same event. The output of our system is a representation for the event schedule and an occurrence date for each event instance. We find that our system beats humans in predicting future occurrences of recurrent events by significant margins. We release our code and system output for further research.

scholarly journals Comparing Apple and Pear Phenology and Model Performance: What Seven Decades of Observations Reveal

Agronomy ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 73 ◽  
Author(s):  
Bianca Drepper ◽  
Anne Gobin ◽  
Serge Remy ◽  
Jos Van Orshoven
Keyword(s):  
Model Performance ◽  
Prediction Errors ◽  
Average Delay ◽  
North Eastern ◽  
Temperature Ranges ◽  
Level Model ◽  
Beginning Of Flowering ◽  
Chilling Period ◽  
Occurrence Date ◽  
The Relationship

Based on observations for the beginning of the flowering stage of Malus domestica (apple) and Pyrus communis (pear) for the 1950–2018 period, phenological trends in north-eastern Belgium were investigated in function of temperatures during dormancy. Moreover, two different phenological models were adapted and evaluated. Median flowering dates of apple were on average 9.5 days earlier following warm dormancy periods, and 11.5 days for pear, but the relationship between bloom date and temperature was found not to be linear, suggesting delayed fulfilment of dormancy requirements due to increased temperatures during the chilling period. After warm chilling periods, an average delay of 5.0 and 10.6 days in the occurrence date of dormancy break was predicted by the phenological models while the PLSR reveals mixed signals regarding the beginning of flowering. Our results suggest overlapping chilling and forcing processes in a transition phase. Regarding the beginning of flowering, a dynamic chill model coupled to a growing degree days estimation yielded significantly lower prediction errors (on average 5.0 days) than a continuous chill-forcing model (6.0 days), at 99% confidence level. Model performance was sensitive to the applied parametrization method and limitations for the application of both models outside the past temperature ranges became apparent.

scholarly journals Adapting the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms to map rainfall-triggered landslides in the West-Cameroons’ highlands (Central-Africa)

2021 ◽  
Author(s):  
Alfred Homère Ngandam Mfondoum ◽  
Pauline Wokwenmendam Nguet ◽  
Jean Valery Mefire Mfondoum ◽  
Mesmin Tchindjang ◽  
Sofia Hakdaoui ◽  
...  
Keyword(s):  
Real Time ◽  
Cox Model ◽  
Central Africa ◽  
Daily Rainfall ◽  
Landsat 8 ◽  
Best Value ◽  
Landslide Event ◽  
Recent Landslide ◽  
Dichotomous Variables ◽  
Occurrence Date

Abstract Background – NASA’s developers recently proposed the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms. This method uses the Landsat 8 satellite images and daily rainfall recordings for a real-time mapping of this geohazard. This study adapts the processing to face the issues of data quality and unavailability/gaps for the mapping of the recent landslide events in west-Cameroon’s highlands. Methods – The SLIP algorithm is adapted, by integrating the inverse NDVI to assess the soil bareness, the Modified Normalized Multi-Band Drought Index (MNMDI) combined with the hydrothermal index to assess soil moisture, and the slope inclination to map the recent landslide. Further, the DRIP algorithm uses the mean daily rainfall to assess the thresholds corresponding to the recent landslide events. Their probability density function (PDF) curves are superimposed and their intersections are used to propose sets of dichotomous variables before (1948-2018) and after the 28 October 2019 landslide event. In addition, a survival analysis is performed to correlate the occurrence date of the landslide with the rainfall since the first known event in Cameroon, through the Cox model.Results – From the SLIP model, the Landslide Hazard Zonation (LHZ) map gives an overall accuracy of 96 % . Further, the DRIP model states that 6/9 ranges of probability are rainfall-triggered landslides at 99.99% , between June and October, while 3/9 ranges show only 4.88% of risk for the same interval. Finally, the survival probability for a known site is up to 0.68 for the best value and between 0.38 and 0.1 for the lowest value through time. Conclusions – The proposed approach is an alternative based on data (un)availability, completed by the site’s lifetime.

scholarly journals Adapting the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms to map rainfall-triggered landslides in the West-Cameroons’ highlands (Central-Africa)

2020 ◽  
Author(s):  
ALFRED HOMERE NGANDAM MFONDOUM ◽  
Pauline Wokwenmendam Nguet ◽  
Jean Valery Mefire Mfondoum ◽  
Mesmin Tchindjang ◽  
Sofia Hakdaoui ◽  
...  
Keyword(s):  
Real Time ◽  
Cox Model ◽  
Central Africa ◽  
Daily Rainfall ◽  
Landsat 8 ◽  
Best Value ◽  
Landslide Event ◽  
Recent Landslide ◽  
Dichotomous Variables ◽  
Occurrence Date

Abstract Background – NASA’s developers recently proposed the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms. This method uses the Landsat 8 satellite images and daily rainfall recordings for a real-time mapping of this geohazard. This study adapts the processing to face the issues of data quality and unavailability/gaps for the mapping of the recent landslide events in west-Cameroon’s highlands. Methods – The SLIP algorithm is adapted, by integrating the inverse NDVI to assess the soil bareness, the Modified Normalized Multi-Band Drought Index (MNMDI) combined with the hydrothermal index to assess soil moisture, and the slope inclination to map the recent landslide. Further, the DRIP algorithm uses the mean daily rainfall to assess the thresholds corresponding to the recent landslide events. Their probability density function (PDF) curves are superimposed and the intersections are used to propose sets of dichotomous variables before (1948-2018) and after the 28 October 2019 landslide event. In addition, a survival analysis is performed to correlating the occurrence date of the landslide with the rainfall since the first known event in Cameroon, through the Cox model. Results – The outcome of the SLIP adapted model is the Landslide Hazard Zonation (LHZ) map, with an overall accuracy of 96%. Further, the outcome of the DRIP adapted model states that the probability of rainfall-triggered landslides is 99.99%, for 6/9 ranges of probability between June and October. Finally, the survival probability for a known site is up to 0.68 for the best value and between 0.38 and 0.1 for the lowest value through time. Conclusions – The proposed approach is an alternative based on data (un)availability, completed by the site’s lifetime.

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