Develop a digital platform for assimilating data of operational performance of existing STPs CETPs ETPs through creation of data exchange interface and flashing messages against defaults Problem Statements

ABSTARCT :

Predicting future changes in ecosystem services is not only highly desirable but is also becoming feasible as several forces (e.g., available big data, developed data assimilation (DA) techniques, and advanced cyber-infrastructure) are converging to transform ecological research into quantitative forecasting. To realize ecological forecasting, we have developed an Ecological Platform for Assimilating Data (EcoPAD, v1.0) into models. EcoPAD (v1.0) is a web-based software system that automates data transfer and processing from sensor networks to ecological forecasting through data management, model simulation, data assimilation, forecasting, and visualization. It facilitates interactive data–model integration from which the model is recursively improved through updated data while data are systematically refined under the guidance of model. EcoPAD (v1.0) relies on data from observations, process-oriented models, DA techniques, and the web-based workflow. We applied EcoPAD (v1.0) to the Spruce and Peatland Responses Under Climatic and Environmental change (SPRUCE) experiment in northern Minnesota. The EcoPAD-SPRUCE realizes fully automated data transfer, feeds meteorological data to drive model simulations, assimilates both manually measured and automated sensor data into the Terrestrial ECOsystem (TECO) model, and recursively forecasts the responses of various biophysical and biogeochemical processes to five temperature and two CO2 treatments in near-real time (weekly).

EXISTING SYSTEM :

? More importance than the ease of re-using existing apps, source code, platforms and standards, is the fact that this can significantly lower the investment cost in terms of money, effort and expertise. ? However, it can be a challenge to find existing apps with development documentation that is thorough, up-to-date, and also includes feedback. ? When such structured vocabularies exist, either as dictionaries, glossaries, gazetteers, or ontologies, they can serve as knowledge entities that can drive further NLP systems.

DISADVANTAGE :

? Data assimilation is growing in importance as process-based ecological models, despite largely simplifying the real systems, need to be complex enough to address sophisticated ecological issues. ? These ecological issues are composed of an enormous number of biotic and abiotic factors interacting with each other. ? Such shifts quantify the potential acclimation of methane production to warming, and future climate warming is likely to have a smaller impact on emissions than most current predictions that do not take account of acclimation.

PROPOSED SYSTEM :

• The proposed framework is part of a platform developed by the hackAIR project2 that gathers and fuses environmental data and specifically particulate matter (PM) measurements from official open sources and from user generated content. • The proposed model involves the use of a negative log-log ordinal classifier to fit the ordinal output well, and the use of a new activation function for photo air pollution level estimation. • We believe that these two repositories cover adequately the needs of the proposed framework and there is no need for a specialized webcam discovery framework.

ADVANTAGE :

? It is usually unidirectional as data are normally used to train models, while the guidance of the model for efficient data collection is limited. ? Similarly to these systems, EcoPAD (v1.0) takes advantage of modern information technology, especially the metadata catalog, to manage diverse data streams. ? Users will also be able to access data directly through programming environments like R, Python, and MATLAB. Simplicity, ease of use, and interoperability are among the main advantages of this API, which enables web-based modeling.