scholarly journals Soil-Landscape Modelling – Reference Soil Group Probability Prediction in Southern Ecuador

10.5772/29468 ◽  
2011 ◽  
Author(s):  
Mareike Lie ◽  
Bruno Glaser ◽  
Bernd Huwe
Keyword(s):  
Soil Group ◽  
Reference Soil ◽  
Southern Ecuador ◽  
Soil Landscape ◽  
Probability Prediction

A soil-landscape model for close-jointed mudstone, Gisborne-East Cape, North-Island, New Zealand

Soil Research ◽  
1995 ◽  
Vol 33 (3) ◽  
pp. 381 ◽  
Author(s):  
M Mcleod ◽  
WC Rijkse ◽  
JR Dymond
Keyword(s):  
New Zealand ◽  
Digital Elevation Model ◽  
Aerial Photographs ◽  
Computer Algorithms ◽  
Landscape Model ◽  
Soil Class ◽  
Soil Group ◽  
Digital Elevation ◽  
Soil Landscape ◽  
Elevation Model

A soil-landscape model, comprising 12 land components at a scale of 1 : 5000, has been developed in Neogene close-jointed mudstone in the Gisborne-East Cape region of the North Island, New Zealand. In a validation, soil order was predicted correctly in 81% of observations, soil group in 80%, soil subgroup in 63% and soilform in 60% of observations. A simplified model based on 11 land components for use at a scale of 1 : 50 000 has also been validated. Here soil order was predicted correctly in 71% of observations, soil group in 73% and soil subgroup in 49% of observations. For application with a digital elevation model (1 : 50 000), the number of land components was amalgamated to five. Here the soil order and soil group were predicted correctly in 63% of observations and soil subgroup in 40% of observations during validation. In all trials, the percentage of correct observations increased if a second choice or subdominant soil class was allowed. It took 2 person-weeks to produce a soil map from the 1 :50 000 form of the model over 400 km2 of steep and hilly country by photo interpretation of stereo aerial photographs, compared with 1 day of applying computer algorithms on the digital elevation model (DEM). The soil-landscape model succinctly relates soil class to land component and it enables improved targeting of farm and planning inputs by empowering existing research into soil fertilizer requirements and soil physical properties.

Classification of technogenic soils according to WRB system in the light of Polish experiences

2013 ◽  
Vol 64 (4) ◽  
pp. 145-150 ◽  
Author(s):  
Przemysław Charzyński ◽  
Renata Bednarek ◽  
Andrzej Greinert ◽  
Piotr Hulisz ◽  
Łukasz Uzarowicz
Keyword(s):  
Soil System ◽  
Soil Group ◽  
Reference Soil ◽  
Technogenic Soils ◽  
Soil Groups

Abstract Technosols are relatively young soil group in WRB soil system, and there is still a lot of to do to better understand processes taking place in these soils and to classify them in a proper way. The objectives of this paper were to (1) evaluate Technosol and 'technogenic' qualifiers for other Reference Soil Groups, and (2) propose new solutions which would improve the classification of technogenic soils in WRB. New qualifiers . Edific, Nekric, Misceric, Artefactic, Radioactivic and new specifier . Technic . are proposed to be added to keys to Technosols. Moreover, Salic and Sodic qualifiers should be also available for Technosols. Furthermore, the supplementation of definitions of thionic horizon and sulphidic material with reference to Technosols is also suggested

Regionalization of wind-speed data to analyse tree-line wind conditions in the eastern Andes of southern Ecuador

Erdkunde ◽  
2015 ◽  
Vol 69 (1) ◽  
pp. 3-19 ◽  
Author(s):  
Julia Wagemann ◽  
Boris Thies ◽  
Rütger Rollenbeck ◽  
Thorsten Peters ◽  
Jörg Bendix
Keyword(s):  
Wind Speed ◽  
Tree Line ◽  
Wind Speed Data ◽  
Southern Ecuador

Day-ahead Load Probabilistic Forecasting Based on Space-time Correction

2020 ◽  
Vol 13 ◽  
Author(s):  
Fei Jin ◽  
Xiaoliang Liu ◽  
Fangfang Xing ◽  
Guoqiang Wen ◽  
Shuangkun Wang ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Northern China ◽  
Space Time ◽  
Load Prediction ◽  
Probabilistic Forecasting ◽  
Residual Distribution ◽  
Time And Space ◽  
Probability Prediction ◽  
Time Correction ◽  
Interval Coverage

Background : The day-ahead load forecasting is an essential guideline for power generating, and it is of considerable significance in power dispatch. Objective: Most of the existing load probability prediction methods use historical data to predict a single area, and rarely use the correlation of load time and space to improve the accuracy of load prediction. Methods: This paper presents a method for day-ahead load probability prediction based on space-time correction. Firstly, the kernel density estimation (KDE) is employed to model the prediction error of the long short-term memory (LSTM) model, and the residual distribution is obtained. Then the correlation value is used to modify the time and space dimensions of the test set's partial period prediction values. Results: The experiment selected three years of load data in 10 areas of a city in northern China. The MAPE of the two modified models on their respective test sets can be reduced by an average of 10.2% and 6.1% compared to previous results. The interval coverage of the probability prediction can be increased by an average of 4.2% and 1.8% than before. Conclusion: The test results show that the proposed correction schemes are feasible.

Traditional approaches and new ideas of Maria Glazovskaya in thematic mapping

2019 ◽  
Vol 947 (5) ◽  
pp. 54-62
Author(s):  
M.D. Bogdanova ◽  
M.I. Gerasimova ◽  
V.A. Snytko
Keyword(s):  
Chemical Elements ◽  
Methodological Issues ◽  
Prognostic Information ◽  
Natural Systems ◽  
Natural Factors ◽  
Soil Landscape ◽  
Thematic Mapping ◽  
Technogenic Loads ◽  
New Ideas ◽  
Traditional Approaches

Professor Maria Glazovskaya (1912–2016) – an outstanding geographer, geochemist and soil scientist, made a prominent contribution to the formation and development of several aspects of thematic mapping both in conceptual and methodological issues. These aspects, namely, soil, landscape- and soil-geochemical, as well as ecological mapping, were derived from the knowledge on soils combined with the concepts of geochemical migrations facilities for certain chemical elements in soils and landscapes. Methodology of compilation of such maps presumes purposeful interpretation of diverse soil and landscape features, their expert evaluation and forecast of response reactions of soils and landscapes to certain technogenic loads. Maria Glazovskaya proposed innovative approaches to thematic mapping enabling her to compile original maps. She introduced the principle of “prognostic information capacity of natural factors”, which means that properties of landscape components contain information appropriate for evaluating the resilience of natural systems. The ideas and methods proposed by Maria Glazovskaya are now implemented in basic and applied thematic mapping.

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