Variación espacio-temporal del oxígeno disuelto en la microcuenca del Río Cutuchi, utilizando técnicas de estimación geoestadística, cantón Latacunga, provincia de Cotopaxi.

The present research evaluated the variation space - temporary of the Oxygen Dissolved concentrations (OD) in Cutuchi River Microbasin (CRM), which is part of the Pastaza River, the CRM is born in the moors of Cotopaxi volcano thaws, it has an area of 2677 km2 and 60 km length. The distribution of t...

সম্পূর্ণ বিবরণ

সংরক্ষণ করুন:
গ্রন্থ-পঞ্জীর বিবরন
প্রধান লেখক: Estrada Sanabria, Johanna Paola (author)
বিন্যাস: bachelorThesis
ভাষা:spa
প্রকাশিত: 2018
বিষয়গুলি:
অনলাইন ব্যবহার করুন:http://repositorio.utc.edu.ec/handle/27000/8444
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বিবরন
সংক্ষিপ্ত:The present research evaluated the variation space - temporary of the Oxygen Dissolved concentrations (OD) in Cutuchi River Microbasin (CRM), which is part of the Pastaza River, the CRM is born in the moors of Cotopaxi volcano thaws, it has an area of 2677 km2 and 60 km length. The distribution of the OD in the CRM is determined by the gaseous exchange through the water surface, this is an indicator of how contaminated is the water and is indispensable for all the aquatic life forms. For the investigation, two databases were used, one from Technical University of Cotopaxi (UTC) and the other from SEK University, which were monitored since September 2010 to July 2011 by UTC, and by SEK since October to April 2017. Initially, it was realized an exploratory analysis of data (EAD), to evaluate the distribution and behavior of later they were processed by the package of the Surfer interpolator and finally to realize the spatial distribution maps of the OD in the CRM. As a result, data was obtained in the EAD that has enough outliers, which could not be completed with the mean since there is too high variability in its distribution in space and in its uniformity due to the data asymmetry, this variation is presented because the OD is very sensitive to contaminating factors near the CRM that alter the obtained samples and for the time when they were taken. Therefore, it was decided to separate the databases on dry and rainy season; achieving the present data less variability and contributing better in the interpolation analysis. As a result, it was obtained that the three best interpolators, for the two databases, although the values are so high and are not acceptable were: Inverse Distance Weighted, Polynomial Regression and Kriging, which were evaluated with the mean square error (MSE). It was concluded that these methods can be used to obtain OD data in the CRM, reducing costs of time and money that the gathering of information in the field would require.