scholarly journals Constraining the age of Lateglacial and early Holocene pollen zones and tephra horizons in southern Sweden with Bayesian probability methods

2006 ◽  
Vol 21 (4) ◽  
pp. 321-334 ◽  
Author(s):  
B. Wohlfarth ◽  
M. Blaauw ◽  
S. M. Davies ◽  
M. Andersson ◽  
S. Wastegård ◽  
...  
Keyword(s):  
Early Holocene ◽  
Bayesian Probability ◽  
Southern Sweden ◽  
Probability Methods

scholarly journals Early Holocene bark-stripping damages as an indicator of large herbivores: Evidence from a submerged Mesolithic landscape in the Haväng area, southern Baltic basin

The Holocene ◽  
2021 ◽  
pp. 095968362110332
Author(s):  
Johannes Edvardsson ◽  
Ola Magnell ◽  
Anton Hansson ◽  
Hans Linderson ◽  
Arne Sjöström ◽  
...  
Keyword(s):  
Early Holocene ◽  
Southern Sweden ◽  
Bark Stripping ◽  
Baltic Basin ◽  
Large Herbivores ◽  
Wild Game ◽  
Different Types ◽  
Valuable Complement ◽  
Clearcut Logging ◽  
Source Of Information

A unique assemblage consisting of 113 pine samples collected from a submerged Mesolithic landscape in the Haväng area, southern Sweden, was examined to assess the presence of large herbivores, as well as changes in wild-game population density and composition. Bark-stripping damages on prehistoric trees is an extremely underutilized source of information about past game-population dynamics, yet our analyzes of wood samples – dated to around 10 500 cal. yr. BP – shows that such material can be successfully used to study the presence and activities of large herbivores, most likely ungulates. To evaluate our results, comparisons have been made with subfossil peatland trees that grew around 6000 years ago, as well as trees from two present day clearcut logging sites in southern Sweden. Furthermore, studies in a wild-game reserve were performed to recognize and understand different types of damages on trees caused by ungulates. Bark-stripping indicate the presence of ungulates, and the rate of damage is commonly associated with the density of the wild game. Bark-stripping was most frequently observed in the submerged wood material from the early Holocene, where damages were detected in 15% of the trees. In comparisons, 11% of the mid-Holocene trees show bark-stripping damages, whereas marks could be detected in the range between 0% and 6% of the trees from the two present-day clearcut logging sites. Our results show that tree-ring analyzes of prehistoric wood can generate information about wild-game dynamics of the past, and thereby being a valuable complement to more commonly used paleoecological and zooarcheological records.

scholarly journals Retrieval of Water Quality Parameters from Hyperspectral Images Using Hybrid Bayesian Probabilistic Neural Network

2020 ◽  
Vol 12 (10) ◽  
pp. 1567
Author(s):  
Yishan Zhang ◽  
Lun Wu ◽  
Huazhong Ren ◽  
Licui Deng ◽  
Pengcheng Zhang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Chlorophyll A ◽  
Probabilistic Neural Network ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Bayesian Probability ◽  
Proposed Model ◽  
Probability Methods

The protection of water resources is of paramount importance to human beings’ practical lives. Monitoring and improving water quality nowadays has become an important topic. In this study, a novel Bayesian probabilistic neural network (BPNN) improved from ordinary Bayesian probability methods has been developed to quantitatively predict water quality parameters including phosphorus, nitrogen, chemical oxygen demand (COD), biochemical oxygen demand (BOD), and chlorophyll a. The proposed method, based on conventional Bayesian probability methods, involves feature engineering and deep neural networks. Additionally, it extracts significant information for each endmember from combinations of spectra by feature extraction, with spectral unmixing based on mathematical and statistical analysis, and calculates each of the water quality parameters. The experimental results show the great performance of the proposed model with all coefficient of determination R 2 over 0.9 greater than the values (0.6–0.8) from conventional methods, which are greater than ordinary Bayesian probability analysis. The mean percent of absolute error (MPAE) is taken into account as an important statistical criterion to evaluate model performance, and our results show that MPAE ranges from 4% (nitrogen) to 10% (COD). The root mean squared errors (RMSEs) of phosphorus, nitrogen, COD, BOD, and chlorophyll-a (Chla) are 0.03 mg/L, 0.28 mg/L, 3.28 mg/L, 0.49 mg/L, and 0.75 μg/L, respectively. In comparison with other deep learning methods, this study takes a relatively small amount of data as training data to train the proposed model and the proposed model is then tested on the same amount of testing data, achieving a greater performance. Thus, the proposed method is time-saving and more effective. This study proposes a more compatible and effective method to assist with decomposing combinations of hyperspectral signatures in order to calculate the content level of each water quality parameter. Moreover, the proposed method is practically applied to hyperspectral image data on board an unmanned aerial vehicle in order to monitor the water quality on a large scale and trace the location of pollution sources in the Maozhou River, Guangdong Province of China, obtaining well-explained and significant results.

A new early Holocene shoreline displacement record for Blekinge, southern Sweden, and implications for underwater archaeology

Boreas ◽  
2018 ◽  
Vol 48 (1) ◽  
pp. 57-71 ◽  
Author(s):  
Anton Hansson ◽  
Dan Hammarlund ◽  
Giacomo Landeschi ◽  
Arne Sjöström ◽  
Björn Nilsson
Keyword(s):  
Early Holocene ◽  
Underwater Archaeology ◽  
Southern Sweden ◽  
Shoreline Displacement

‘Mass’ deaths of mooseAlces alcesin southern Sweden: population level characterisation

2002 ◽  
Vol 8 (1) ◽  
pp. 219-228 ◽  
Author(s):  
Emil Broman ◽  
Kjell Wallin ◽  
Margareta Steén ◽  
Göran Cederlund
Keyword(s):  
Population Level ◽  
Southern Sweden
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