This paper introduces the dynamic mode decomposition (DMD) for the prediction of the air quality and the precipitation in the presence of high rain as an application of environment prediction tasks by forecasting the appropriate index parameters for each field. The forecasting procedure is based on the use of two real data bases containing the pollutant concentration, and the precipitation for the flooding forecasting. Moreover, the temporal evolution of the DMD modes, can be used to reconstruct the desired components and perform forecasting at the same time using the eigenvalues and eigenvectors. This task of the DMD is already known by the literature, the new in this paper is the application of the DMD for forecasting tasks on the environment issues which present appreciated results that are discussed and well analysed in this paper using different performance indexes to prove its efficiency.

Comfort conditions in buildings are no more a luxury than a necessity. Architects and designers are focussing their work on providing the best conditions of comfort in a building depending on the activity. The university gives students and teachers a space where they can share knowledge and experience. Besides, it should offer the best conditions for this process to work properly. That is why a lot of effort is put into bringing university occupants a proper space for working and learning. Despite all this effort, an evaluation of the comfort conditions in classrooms is necessary to ensure that indoor conditions can be adapted to occupant activity. One way to do this is to perform in situ measurements during teaching hours and compare the results to local standards. Besides the measures, we need occupants review regarding the Indoor Environment Quality (IEQ) trough survey. These data give us late feedback regarding the IEQ. therefore, with the different set of data acquired we intend to predict comfort conditions using different machine learning algorithms. A comparison between these algorithms will help us choose an adequate algorithm to estimate perceived comfort

The “delay scheduling” procedure is a recent concept for the design of control system. “Delay scheduling” strategy manipulates existing delays in the feedback as a control parameter which is increasing in order to recover stability. Indeed, the system should have many stable regions in the time delays domain. To do this, a recent procedure, Cluster Treatment of Characteristic Roots (CTCR) is deployed, allowing the exact determination of the complete picture of stable regions in time delays space. Starting from “delay scheduling” and CTCR methods, the objective of the proposed approach is to track desired trajectories even in presence of time delay and static disturbances at the output of system.