Remote sensing of the oceans from space with special reference to marine resources

A number of ocean science fields have profitted, either directly or indirectly from satellite remote sensing, including physical, biological and geological oceanography. User oriented applications include fishing, shipping, offshore drilling and mining, coastal engineering and coastal hydrology. Following a brief account of the technology involved, areas in oceanography benefitting from satellite information are detailed. Examples are given of satellite data applications to marine resources

On the role of pre and post-processing in environmental data mining

The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed

Assessment of hydrological and seasonal controls over the nitrate flushing from a forested watershed using a data mining technique

A data mining, regression tree algorithm M5 was used to review the role of mutual hydrological and seasonal settings which control the streamwater nitrate flushing during hydrological events within a forested watershed in the southwestern part of Slovenia, characterized by distinctive flushing, almost torrential hydrological regime. The basis for the research was an extensive dataset of continuous, high frequency measurements of seasonal meteorological conditions, watershed hydrological responses and streamwater nitrate concentrations. The dataset contained 16 recorded hydrographs occurring in different seasonal and hydrological conditions. Based on predefined regression tree pruning criteria, a comprehensible regression tree model was obtained in the sense of the domain knowledge, which was able to adequately describe most of the streamwater nitrate concentration variations (RMSE=1.02mg/l-N; r=0.91). The attributes which were found to be the most descriptive in the sense of streamwater nitrate concentrations were the antecedent precipitation index (API) and air temperatures in the preceding periods. The model was most successful in describing streamwater concentrations in the range 1-4 mg/l-N, covering large proportion of the dataset. The model performance was little worse in the periods of high streamwater nitrate concentration peaks during the summer hydrographs (up to 7 mg/l-N) but poor during the autumn hydrograph (up to 14 mg/l-N) related to highly variable hydrological conditions, which would require a less robust regression tree model based on the extended dataset

Estudi comparatiu de la publicació científica de la UPC i l’Escola de Camins vs.altres universitats d’àmbit internacional (2009-2018)

L'informe se centra en la publicació científica especialitzada en l'àmbit temàtic propi de l'Escola de Camins: l'enginyeria civil. Es comparen indicadors bibliomètrics de la UPC i l'Escola de Camins amb els d'altres universitats internacionals amb activitat de recerca notable en l'àmbit de l'enginyeria civilPostprint (published version

Data mining as a tool for environmental scientists

Over recent years a huge library of data mining algorithms has been developed to tackle a variety of problems in fields such as medical imaging and network traffic analysis. Many of these techniques are far more flexible than more classical modelling approaches and could be usefully applied to data-rich environmental problems. Certain techniques such as Artificial Neural Networks, Clustering, Case-Based Reasoning and more recently Bayesian Decision Networks have found application in environmental modelling while other methods, for example classification and association rule extraction, have not yet been taken up on any wide scale. We propose that these and other data mining techniques could be usefully applied to difficult problems in the field. This paper introduces several data mining concepts and briefly discusses their application to environmental modelling, where data may be sparse, incomplete, or heterogenous

Hydrogeological challenges in a low carbon economy

Hydrogeology has traditionally been regarded as the province of the water industry, but it is increasingly finding novel applications in the energy sector. Hydrogeology has a longstanding role in geothermal energy exploration and management. Although aquifer management methods can be directly applied to most high-enthalpy geothermal reservoirs, hydrogeochemical inference techniques differ somewhat owing to peculiarities of high-temperature processes. Hydrogeological involvement in the development of ground-coupled heating and cooling systems using heat pumps has led to the emergence of the sub-discipline now known as thermogeology. The patterns of groundwater flow and heat transport are closely analogous and can thus be analysed using very similar techniques. Without resort to heat pumps, groundwater is increasingly being pumped to provide cooling for large buildings; the renewability of such systems relies on accurate prediction and management of thermal breakthrough from reinjection to production boreholes. Hydrogeological analysis can contribute to quantification of accidental carbon emissions arising from disturbance of groundwater-fed peatland ecosystems during wind farm construction. Beyond renewables, key applications of hydrogeology are to be found in the nuclear sector, and in the sunrise industries of unconventional gas and carbon capture and storage, with high temperatures attained during underground coal gasification requiring geothermal technology transfer

Quarterly literature review of the remote sensing of natural resources

The Technology Application Center reviewed abstracted literature sources, and selected document data and data gathering techniques which were performed or obtained remotely from space, aircraft or groundbased stations. All of the documentation was related to remote sensing sensors or the remote sensing of the natural resources. Sensors were primarily those operating within the 10 to the minus 8 power to 1 meter wavelength band. Included are NASA Tech Briefs, ARAC Industrial Applications Reports, U.S. Navy Technical Reports, U.S. Patent reports, and other technical articles and reports

Literature review of the remote sensing of natural resources

Abstracts of 596 documents related to remote sensors or the remote sensing of natural resources by satellite, aircraft, or ground-based stations are presented. Topics covered include general theory, geology and hydrology, agriculture and forestry, marine sciences, urban land use, and instrumentation. Recent documents not yet cited in any of the seven information sources used for the compilation are summarized. An author/key word index is provided