scholarly journals Geoekološko vrjednovanje reljefa otoka Pašmana

Geoadria ◽  
2017 ◽  
Vol 15 (2) ◽  
pp. 241
Author(s):  
Marica Mamut

The geoecological evaluation of the relief of Pašman Island from the standpoint of its touristic evaluation was conducted on the previously conducted geomorphologic analysis of the island relief. The evaluation was conducted for the needs of specific types of touristic activities (swimming, sunbathing, walking, "škraping") in the sense of its physical favourability, aesthetic value and accessibility.The method of relative relief evaluation was applied, whereby relief was evaluated within four morphographic categories: slopes, peaks, valley bottoms and beds and the coast. According to this method, as the principal restricting factor of tourist evaluation of certain parts of Pašman Island is the problem of inaccessibility, especially on the steeper south-west façade of the island (remoteness, inexistence or lack of arranged paths and wharfs). In accordance therewith, several proposals as possible solutions to the present problem were given (arrangement of paths, informing tourists on possible individual or group walks to certain destinations, organising trips with professional guides).

2019 ◽  
Vol 25 ◽  
pp. 317
Author(s):  
Ayotunde Ale ◽  
Opeyemi Aloro ◽  
Ayanbola Adepoju
Keyword(s):  

2019 ◽  
Vol 25 ◽  
pp. 121-122
Author(s):  
Olufunmilayo Adeleye ◽  
Ejiofor Ugwu ◽  
Anthonia Ogbera ◽  
Akinola Dada ◽  
Ibrahim Gezawa ◽  
...  

1991 ◽  
Vol 102 (5-6) ◽  
pp. 437-443
Author(s):  
B. M. Sharma
Keyword(s):  

2020 ◽  
Vol 12 (1) ◽  
pp. 60-69 ◽  
Author(s):  
Pijush Basak

The South West Monsoon rainfall data of the meteorological subdivision number 6 of India enclosing Gangetic West Bengal is shown to be decomposable into eight empirical time series, namely Intrinsic Mode Functions. This leads one to identify the first empirical mode as a nonlinear part and the remaining modes as the linear part of the data. The nonlinear part is modeled with the technique Neural Network based Generalized Regression Neural Network model technique whereas the linear part is sensibly modeled through simple regression method. The different Intrinsic modes as verified are well connected with relevant atmospheric features, namely, El Nino, Quasi-biennial Oscillation, Sunspot cycle and others. It is observed that the proposed model explains around 75% of inter annual variability (IAV) of the rainfall series of Gangetic West Bengal. The model is efficient in statistical forecasting of South West Monsoon rainfall in the region as verified from independent part of the real data. The statistical forecasts of SWM rainfall for GWB for the years 2012 and 2013 are108.71 cm and 126.21 cm respectively, where as corresponding to the actual rainfall of 93.19 cm 115.20 cm respectively which are within one standard deviation of mean rainfall.


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