Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain)

[EN] The Mar Menor is a hypersaline coastal lagoon with high environmental value and a characteristic example of a highly anthropized hydro-ecosystem located in the southeast of Spain. An unprecedented eutrophication crisis in 2016 and 2019 with abrupt changes in the quality of its waters caused a great social alarm. Understanding and modeling the level of a eutrophication indicator, such as chlorophyll-a (Chl-a), benefits the management of this complex system. In this study, we investigate the potential machine learning (ML) methods to predict the level of Chl-a. Particularly, Multilayer Neural Networks (MLNNs) and Support Vector Regressions (SVRs) are evaluated using as a target dataset information of up to nine different water quality parameters. The most relevant input combinations were extracted using wrapper feature selection methods which simplified the structure of the model, resulting in a more accurate and efficient procedure. Although the performance in the validation phase showed that SVR models obtained better results than MLNNs, experimental results indicated that both ML algorithms provide satisfactory results in the prediction of Chl-a concentration, reaching up to 0.7 R-CV(2) (cross-validated coefficient of determination) for the best-fit models.This research was partially funded by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities under grants RTI2018-096384-B-I00 and RTC-2017-6389-5.Jimeno-Sáez, P.; Senent-Aparicio, J.; Cecilia-Canales, JM.; Pérez-Sánchez, J. (2020). Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain). International Journal of Environmental research and Public Health (Online). 17(4):1-14. https://doi.org/10.3390/ijerph17041189S114174Pérez-Ruzafa, A., Pérez-Ruzafa, I. M., Newton, A., & Marcos, C. (2019). Coastal Lagoons: Environmental Variability, Ecosystem Complexity, and Goods and Services Uniformity. Coasts and Estuaries, 253-276. doi:10.1016/b978-0-12-814003-1.00015-0Kennish, M. J. (2015). Coastal Lagoons. Encyclopedia of Earth Sciences Series, 140-143. doi:10.1007/978-94-017-8801-4_47García-Ayllón, S. (2019). New Strategies to Improve Co-Management in Enclosed Coastal Seas and Wetlands Subjected to Complex Environments: Socio-Economic Analysis Applied to an International Recovery Success Case Study after an Environmental Crisis. Sustainability, 11(4), 1039. doi:10.3390/su11041039Le Moal, M., Gascuel-Odoux, C., Ménesguen, A., Souchon, Y., Étrillard, C., Levain, A., … Pinay, G. (2019). Eutrophication: A new wine in an old bottle? Science of The Total Environment, 651, 1-11. doi:10.1016/j.scitotenv.2018.09.139Alcolea, A., Contreras, S., Hunink, J. E., García-Aróstegui, J. L., & Jiménez-Martínez, J. (2019). Hydrogeological modelling for the watershed management of the Mar Menor coastal lagoon (Spain). Science of The Total Environment, 663, 901-914. doi:10.1016/j.scitotenv.2019.01.375Nixon, S. W. (1995). Coastal marine eutrophication: A definition, social causes, and future concerns. Ophelia, 41(1), 199-219. doi:10.1080/00785236.1995.10422044Huang, J., Gao, J., & Zhang, Y. (2015). Combination of artificial neural network and clustering techniques for predicting phytoplankton biomass of Lake Poyang, China. Limnology, 16(3), 179-191. doi:10.1007/s10201-015-0454-7Canfield, D. E. (1983). PREDICTION OF CHLOROPHYLL A CONCENTRATIONS IN FLORIDA LAKES: THE IMPORTANCE OF PHOSPHORUS AND NITROGEN. Journal of the American Water Resources Association, 19(2), 255-262. doi:10.1111/j.1752-1688.1983.tb05323.xPhillips, G., Pietiläinen, O.-P., Carvalho, L., Solimini, A., Lyche Solheim, A., & Cardoso, A. C. (2008). Chlorophyll–nutrient relationships of different lake types using a large European dataset. Aquatic Ecology, 42(2), 213-226. doi:10.1007/s10452-008-9180-0EL PAÍS https://elpais.com/elpais/2019/10/22/inenglish/1571743580_215496.htmlGarcía-Ayllón, S. (2017). Integrated management in coastal lagoons of highly complexity environments: Resilience comparative analysis for three case-studies. Ocean & Coastal Management, 143, 16-25. doi:10.1016/j.ocecoaman.2016.10.007Garcia-Ayllon, S. (2018). The Integrated Territorial Investment (ITI) of the Mar Menor as a model for the future in the comprehensive management of enclosed coastal seas. Ocean & Coastal Management, 166, 82-97. doi:10.1016/j.ocecoaman.2018.05.004Pérez-Ruzafa, A., Campillo, S., Fernández-Palacios, J. M., García-Lacunza, A., García-Oliva, M., Ibañez, H., … Marcos, C. (2019). Long-Term Dynamic in Nutrients, Chlorophyll a, and Water Quality Parameters in a Coastal Lagoon During a Process of Eutrophication for Decades, a Sudden Break and a Relatively Rapid Recovery. Frontiers in Marine Science, 6. doi:10.3389/fmars.2019.00026Iglesias, C., Martínez Torres, J., García Nieto, P. J., Alonso Fernández, J. R., Díaz Muñiz, C., Piñeiro, J. I., & Taboada, J. (2013). Turbidity Prediction in a River Basin by Using Artificial Neural Networks: A Case Study in Northern Spain. Water Resources Management, 28(2), 319-331. doi:10.1007/s11269-013-0487-9Najah, A., El-Shafie, A., Karim, O. A., & El-Shafie, A. H. (2012). Application of artificial neural networks for water quality prediction. Neural Computing and Applications, 22(S1), 187-201. doi:10.1007/s00521-012-0940-3Li, X., Cheng, Z., Yu, Q., Bai, Y., & Li, C. (2017). Water-Quality Prediction Using Multimodal Support Vector Regression: Case Study of Jialing River, China. Journal of Environmental Engineering, 143(10), 04017070. doi:10.1061/(asce)ee.1943-7870.0001272Su, J., Wang, X., Zhao, S., Chen, B., Li, C., & Yang, Z. (2015). A Structurally Simplified Hybrid Model of Genetic Algorithm and Support Vector Machine for Prediction of Chlorophyll a in Reservoirs. Water, 7(12), 1610-1627. doi:10.3390/w7041610Abba, S. I., Hadi, S. J., & Abdullahi, J. (2017). River water modelling prediction using multi-linear regression, artificial neural network, and adaptive neuro-fuzzy inference system techniques. Procedia Computer Science, 120, 75-82. doi:10.1016/j.procs.2017.11.212Juntunen, P., Liukkonen, M., Pelo, M., Lehtola, M. J., & Hiltunen, Y. (2012). Modelling of Water Quality: An Application to a Water Treatment Process. Applied Computational Intelligence and Soft Computing, 2012, 1-9. doi:10.1155/2012/846321Li, X., Sha, J., & Wang, Z. (2016). A comparative study of multiple linear regression, artificial neural network and support vector machine for the prediction of dissolved oxygen. Hydrology Research, 48(5), 1214-1225. doi:10.2166/nh.2016.149Charulatha, G., Srinivasalu, S., Uma Maheswari, O., Venugopal, T., & Giridharan, L. (2017). Evaluation of ground water quality contaminants using linear regression and artificial neural network models. Arabian Journal of Geosciences, 10(6). doi:10.1007/s12517-017-2867-6Keller, S., Maier, P., Riese, F., Norra, S., Holbach, A., Börsig, N., … Hinz, S. (2018). Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae and Turbidity. International Journal of Environmental Research and Public Health, 15(9), 1881. doi:10.3390/ijerph15091881Li, X., Sha, J., & Wang, Z.-L. (2017). Chlorophyll-A Prediction of Lakes with Different Water Quality Patterns in China Based on Hybrid Neural Networks. Water, 9(7), 524. doi:10.3390/w9070524Yi, H.-S., Park, S., An, K.-G., & Kwak, K.-C. (2018). Algal Bloom Prediction Using Extreme Learning Machine Models at Artificial Weirs in the Nakdong River, Korea. International Journal of Environmental Research and Public Health, 15(10), 2078. doi:10.3390/ijerph15102078Nazeer, M., Bilal, M., Alsahli, M., Shahzad, M., & Waqas, A. (2017). Evaluation of Empirical and Machine Learning Algorithms for Estimation of Coastal Water Quality Parameters. ISPRS International Journal of Geo-Information, 6(11), 360. doi:10.3390/ijgi6110360Erena, Domínguez, Aguado, Soria, & García-Galiano. (2019). Monitoring Coastal Lagoon Water Quality Through Remote Sensing: The Mar Menor as a Case Study. Water, 11(7), 1468. doi:10.3390/w11071468García-Oliva, M., Marcos, C., Umgiesser, G., McKiver, W., Ghezzo, M., De Pascalis, F., & Pérez-Ruzafa, A. (2019). Modelling the impact of dredging inlets on the salinity and temperature regimes in coastal lagoons. Ocean & Coastal Management, 180, 104913. doi:10.1016/j.ocecoaman.2019.104913López-Ballesteros, A., Senent-Aparicio, J., Srinivasan, R., & Pérez-Sánchez, J. (2019). Assessing the Impact of Best Management Practices in a Highly Anthropogenic and Ungauged Watershed Using the SWAT Model: A Case Study in the El Beal Watershed (Southeast Spain). Agronomy, 9(10), 576. doi:10.3390/agronomy9100576Senent-Aparicio, J., Pérez-Sánchez, J., García-Aróstegui, J., Bielsa-Artero, A., & Domingo-Pinillos, J. (2015). Evaluating Groundwater Management Sustainability under Limited Data Availability in Semiarid Zones. Water, 7(12), 4305-4322. doi:10.3390/w7084305Navarro, M. C., Pérez-Sirvent, C., Martínez-Sánchez, M. J., Vidal, J., Tovar, P. J., & Bech, J. (2008). Abandoned mine sites as a source of contamination by heavy metals: A case study in a semi-arid zone. Journal of Geochemical Exploration, 96(2-3), 183-193. doi:10.1016/j.gexplo.2007.04.011Conesa, H. M., & Jiménez-Cárceles, F. J. (2007). The Mar Menor lagoon (SE Spain): A singular natural ecosystem threatened by human activities. Marine Pollution Bulletin, 54(7), 839-849. doi:10.1016/j.marpolbul.2007.05.007Domingo-Pinillos, J., Senent-Aparicio, J., García-Aróstegui, J., & Baudron, P. (2018). Long Term Hydrodynamic Effects in a Semi-Arid Mediterranean Multilayer Aquifer: Campo de Cartagena in South-Eastern Spain. Water, 10(10), 1320. doi:10.3390/w10101320Stefanova, A., Hesse, C., & Krysanova, V. (2015). Combined Impacts of Medium Term Socio-Economic Changes and Climate Change on Water Resources in a Managed Mediterranean Catchment. Water, 7(12), 1538-1567. doi:10.3390/w7041538Velasco, J., Lloret, J., Millan, A., Marin, A., Barahona, J., Abellan, P., & Sanchez-Fernandez, D. (2006). Nutrient And Particulate Inputs Into The Mar Menor Lagoon (Se Spain) From An Intensive Agricultural Watershed. Water, Air, and Soil Pollution, 176(1-4), 37-56. doi:10.1007/s11270-006-2859-8García-Oliva, M., Pérez-Ruzafa, Á., Umgiesser, G., McKiver, W., Ghezzo, M., De Pascalis, F., & Marcos, C. (2018). Assessing the Hydrodynamic Response of the Mar Menor Lagoon to Dredging Inlets Interventions through Numerical Modelling. Water, 10(7), 959. doi:10.3390/w10070959Wei, B., Sugiura, N., & Maekawa, T. (2001). Use of artificial neural network in the prediction of algal blooms. Water Research, 35(8), 2022-2028. doi:10.1016/s0043-1354(00)00464-4(2000). Artificial Neural Networks in Hydrology. I: Preliminary Concepts. Journal of Hydrologic Engineering, 5(2), 115-123. doi:10.1061/(asce)1084-0699(2000)5:2(115)Jimeno-Sáez, P., Senent-Aparicio, J., Pérez-Sánchez, J., & Pulido-Velazquez, D. (2018). A Comparison of SWAT and ANN Models for Daily Runoff Simulation in Different Climatic Zones of Peninsular Spain. Water, 10(2), 192. doi:10.3390/w10020192Nguyen, V. D., Tan, R. R., Brondial, Y., & Fuchino, T. (2007). Prediction of vapor–liquid equilibrium data for ternary systems using artificial neural networks. Fluid Phase Equilibria, 254(1-2), 188-197. doi:10.1016/j.fluid.2007.03.014Bekkari, N., & Zeddouri, A. (2019). Using artificial neural network for predicting and controlling the effluent chemical oxygen demand in wastewater treatment plant. Management of Environmental Quality: An International Journal, 30(3), 593-608. doi:10.1108/meq-04-2018-0084Zhang, Y., Gao, X., Smith, K., Inial, G., Liu, S., Conil, L. B., & Pan, B. (2019). Integrating water quality and operation into prediction of water production in drinking water treatment plants by genetic algorithm enhanced artificial neural network. Water Research, 164, 114888. doi:10.1016/j.watres.2019.114888Naghibi, S. A., Ahmadi, K., & Daneshi, A. (2017). Application of Support Vector Machine, Random Forest, and Genetic Algorithm Optimized Random Forest Models in Groundwater Potential Mapping. Water Resources Management, 31(9), 2761-2775. doi:10.1007/s11269-017-1660-3Kuhn, M. (2008). Building Predictive Models inRUsing thecaretPackage. Journal of Statistical Software, 28(5). doi:10.18637/jss.v028.i05Caret: Classification and Regression Training, R Package Version 6.0-84 https://CRAN.R-project.org/package=caretMaier, H. R., Jain, A., Dandy, G. C., & Sudheer, K. P. (2010). Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions. Environmental Modelling & Software, 25(8), 891-909. doi:10.1016/j.envsoft.2010.02.003Kumar, S., & Bucher, P. (2016). Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features. BMC Bioinformatics, 17(S1). doi:10.1186/s12859-015-0846-zMjalli, F. S., Al-Asheh, S., & Alfadala, H. E. (2007). Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance. Journal of Environmental Management, 83(3), 329-338. doi:10.1016/j.jenvman.2006.03.004Palani, S., Liong, S.-Y., & Tkalich, P. (2008). An ANN application for water quality forecasting. Marine Pollution Bulletin, 56(9), 1586-1597. doi:10.1016/j.marpolbul.2008.05.021Kuo, J.-T., Hsieh, M.-H., Lung, W.-S., & She, N. (2007). Using artificial neural network for reservoir eutrophication prediction. Ecological Modelling, 200(1-2), 171-177. doi:10.1016/j.ecolmodel.2006.06.018Jimeno-Sáez, P., Senent-Aparicio, J., Pérez-Sánchez, J., Pulido-Velazquez, D., & Cecilia, J. (2017). Estimation of Instantaneous Peak Flow Using Machine-Learning Models and Empirical Formula in Peninsular Spain. Water, 9(5), 347. doi:10.3390/w905034

Advanced Occupancy Measurement Using Sensor Fusion

With roughly about half of the energy used in buildings attributed to Heating, Ventilation, and Air conditioning (HVAC) systems, there is clearly great potential for energy saving through improved building operations. Accurate knowledge of localised and real-time occupancy numbers can have compelling control applications for HVAC systems. However, existing technologies applied for building occupancy measurements are limited, such that a precise and reliable occupant count is difficult to obtain. For example, passive infrared (PIR) sensors commonly used for occupancy sensing in lighting control applications cannot differentiate between occupants grouped together, video sensing is often limited by privacy concerns, atmospheric gas sensors (such as CO2 sensors) may be affected by the presence of electromagnetic (EMI) interference, and may not show clear links between occupancy and sensor values. Past studies have indicated the need for a heterogeneous multi-sensory fusion approach for occupancy detection to address the short-comings of existing occupancy detection systems. The aim of this research is to develop an advanced instrumentation strategy to monitor occupancy levels in non-domestic buildings, whilst facilitating the lowering of energy use and also maintaining an acceptable indoor climate. Accordingly, a novel multi-sensor based approach for occupancy detection in open-plan office spaces is proposed. The approach combined information from various low-cost and non-intrusive indoor environmental sensors, with the aim to merge advantages of various sensors, whilst minimising their weaknesses. The proposed approach offered the potential for explicit information indicating occupancy levels to be captured. The proposed occupancy monitoring strategy has two main components; hardware system implementation and data processing. The hardware system implementation included a custom made sound sensor and refinement of CO2 sensors for EMI mitigation. Two test beds were designed and implemented for supporting the research studies, including proof-of-concept, and experimental studies. Data processing was carried out in several stages with the ultimate goal being to detect occupancy levels. Firstly, interested features were extracted from all sensory data collected, and then a symmetrical uncertainty analysis was applied to determine the predictive strength of individual sensor features. Thirdly, a candidate features subset was determined using a genetic based search. Finally, a back-propagation neural network model was adopted to fuse candidate multi-sensory features for estimation of occupancy levels. Several test cases were implemented to demonstrate and evaluate the effectiveness and feasibility of the proposed occupancy detection approach. Results have shown the potential of the proposed heterogeneous multi-sensor fusion based approach as an advanced strategy for the development of reliable occupancy detection systems in open-plan office buildings, which can be capable of facilitating improved control of building services. In summary, the proposed approach has the potential to: (1) Detect occupancy levels with an accuracy reaching 84.59% during occupied instances (2) capable of maintaining average occupancy detection accuracy of 61.01%, in the event of sensor failure or drop-off (such as CO2 sensors drop-off), (3) capable of utilising just sound and motion sensors for occupancy levels monitoring in a naturally ventilated space, (4) capable of facilitating potential daily energy savings reaching 53%, if implemented for occupancy-driven ventilation control

Realt-Time Building Occupancy Sensing for Supporting Demand Driven HVAC Operations

Accurate knowledge of localised and real-time occupancy numbers can have compelling control applications for Heating, Ventilation and Air-conditioning (HVAC) systems. However, a precise and reliable measurement of occupancy still remains difficult. Existing technologies are plagued with a number of issues ranging from unreliable data, maintaining privacy and sensor drift. More effective control of HVAC systems may be possible using a smart sensing network for occupancy detection. A low-cost and non-intrusive sensor network is deployed in an open-plan office, combining information such as sound level and motion, to estimate occupancy numbers, while an infrared camera is implemented to establish ground truth occupancy levels. Symmetrical uncertainty analysis is used for feature selection, and selected multi-sensory features are fused using a neuralnetwork model, with occupancy estimation accuracy reaching up to 84.59%. The proposed system offers promising opportunities for reliable occupancy sensing, capable of supporting demand driven HVAC operations

Autonomic State Management for Optimistic Simulation Platforms

We present the design and implementation of an autonomic state manager (ASM) tailored for integration within optimistic parallel discrete event simulation (PDES) environments based on the C programming language and the executable and linkable format (ELF), and developed for execution on x8664 architectures. With ASM, the state of any logical process (LP), namely the individual (concurrent) simulation unit being part of the simulation model, is allowed to be scattered on dynamically allocated memory chunks managed via standard API (e.g., malloc/free). Also, the application programmer is not required to provide any serialization/deserialization module in order to take a checkpoint of the LP state, or to restore it in case a causality error occurs during the optimistic run, or to provide indications on which portions of the state are updated by event processing, so to allow incremental checkpointing. All these tasks are handled by ASM in a fully transparent manner via (A) runtime identification (with chunk-level granularity) of the memory map associated with the LP state, and (B) runtime tracking of the memory updates occurring within chunks belonging to the dynamic memory map. The co-existence of the incremental and non-incremental log/restore modes is achieved via dual versions of the same application code, transparently generated by ASM via compile/link time facilities. Also, the dynamic selection of the best suited log/restore mode is actuated by ASM on the basis of an innovative modeling/optimization approach which takes into account stability of each operating mode with respect to variations of the model/environmental execution parameters

Wireless Sensor Networking in Challenging Environments

Recent years have witnessed growing interest in deploying wireless sensing applications in real-world environments. For example, home automation systems provide fine-grained metering and control of home appliances in residential settings. Similarly, assisted living applications employ wireless sensors to provide continuous health and wellness monitoring in homes. However, real deployments of Wireless Sensor Networks (WSNs) pose significant challenges due to their low-power radios and uncontrolled ambient environments. Our empirical study in over 15 real-world apartments shows that low-power WSNs based on the IEEE 802.15.4 standard are highly susceptible to external interference beyond user control, such as Wi-Fi access points, Bluetooth peripherals, cordless phones, and numerous other devices prevalent in residential environments that share the unlicensed 2.4 GHz ISM band with IEEE 802.15.4 radios. To address these real-world challenges, we developed two practical wireless network protocols including the Adaptive and Robust Channel Hopping (ARCH) protocol and the Adaptive Energy Detection Protocol (AEDP). ARCH enhances network reliability through opportunistically changing radio\u27s frequency to avoid interference and environmental noise and AEDP reduces false wakeups in noisy wireless environments by dynamically adjusting the wakeup threshold of low-power radios. Another major trend in WSNs is the convergence with smart phones. To deal with the dynamic wireless conditions and varying application requirements of mobile users, we developed the Self-Adapting MAC Layer (SAML) to support adaptive communication between smart phones and wireless sensors. SAML dynamically selects and switches Medium Access Control protocols to accommodate changes in ambient conditions and application requirements. Compared with the residential and personal wireless systems, industrial applications pose unique challenges due to their critical demands on reliability and real-time performance. We developed an experimental testbed by realizing key network mechanisms of industrial Wireless Sensor and Actuator Networks (WSANs) and conducted an empirical study that revealed the limitations and potential enhancements of those mechanisms. Our study shows that graph routing is more resilient to interference and its backup routes may be heavily used in noisy environments, which demonstrate the necessity of path diversity for reliable WSANs. Our study also suggests that combining channel diversity with retransmission may effectively reduce the burstiness of transmission failures and judicious allocation of multiple transmissions in a shared slot can effectively improve network capacity without significantly impacting reliability

Automating Large-Scale Simulation Calibration to Real-World Sensor Data

Many key decisions and design policies are made using sophisticated computer simulations. However, these sophisticated computer simulations have several major problems. The two main issues are 1) gaps between the simulation model and the actual structure, and 2) limitations of the modeling engine\u27s capabilities. This dissertation\u27s goal is to address these simulation deficiencies by presenting a general automated process for tuning simulation inputs such that simulation output matches real world measured data. The automated process involves the following key components -- 1) Identify a model that accurately estimates the real world simulation calibration target from measured sensor data; 2) Identify the key real world measurements that best estimate the simulation calibration target; 3) Construct a mapping from the most useful real world measurements to actual simulation outputs; 4) Build fast and effective simulation approximation models that predict simulation output using simulation input; 5) Build a relational model that captures inter variable dependencies between simulation inputs and outputs; and finally 6) Use the relational model to estimate the simulation input variables from the mapped sensor data, and use either the simulation model or approximate simulation model to fine tune input simulation parameter estimates towards the calibration system. The work in this dissertation individually validates and completes five out of the six calibration components with respect to the residential energy domain. Step 1 is satisfied by identifying the best model for predicting next hour residential electrical consumption, the calibration target. Step 2 is completed by identifying the most important sensors for predicting residential electrical consumption, the real world measurements. While step 3 is completed by domain experts, step 4 is addressed by using techniques from the Big Data machine learning domain to build approximations for the EnergyPlus (E+) simulator. Step 5\u27s solution leverages the same Big Data machine learning techniques to build a relational model that describes how the simulator\u27s variables are probabilistically related. Finally, step 6 is partially demonstrated by using the relational model to estimate simulation parameters for E+ simulations with known ground truth simulation inputs

Microservices and Machine Learning Algorithms for Adaptive Green Buildings

In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings