Project:
PLANNING AND MANAGEMENT OF WATER RESOURCES BASED ON IoT DATA ANALYSIS (WATERoT).
Participants:
CENTIC, HIDROCONTA, UCAM, UPV, VIELCA INGENIEROS.
Summary:
The aim of this project is to facilitate the path towards a better use, valorization and management of water sources by society through the development of an IoT infrastructure hardware and software that allows to offer intelligent services based on the analysis of large volumes of data related to the management of water resources.
Call:
Ministerio Economía y Empresa Retos Colaboración.
Duration:
4 years 2018-2021.
Funded by:
MINECO, Ministerio de Economía, Industria y Competitividad (MINECO), Agencia Estatal de Investigación (AEI) y al Fondo Europeo de Desarrollo Regional (FEDER) Num Expediente: RTC 2017-6389-5.
Vielca Ingenieros, S.A. (Coordinator of the project)
Principal Researcher: D. Pablo Blanco López
Universidad Católica San Antonio de Murcia (UCAM)
Principal Researcher: Dr. Javier Senent Aparicio
Centro Tecnológico de las Tecnologías de la Información y las Comunicaciones de la Región de Murcia
Principal Researcher: Dr. D Joaquín Lasheras Velasco
Universitat Politécnica de Valencia (UPV)
Principal Researcher: Dr. D. Carlos Periñán Pascual
HIDROCONTA, S.A.
Principal Researcher: Francisco Pagán Ros
Thanks to the research framework of this project, a number of doctoral theses have begun to be developed, some of them with the mention of industrial doctorate. These theses aim to train members of the companies associated with the project in aspects of research that can strengthen the lines of innovation of their respective companies. In addition, those hired on the academic side are also receiving doctoral training closely linked to the company.
The following are the doctoral theses defended:
1.- Nicolás José Fernández Martínez. 21/10/2020. A Linguistically-aware Computational Approach To Microtext Location Detection. Directores: Dr. D. Carlos Periñán Pascual (UPV) y Dr. D. Ángel Felices Lago (Universidad de Granada). Github: https://github.com/njfm0001/LORE/
2.- Juan José Franco Peñaranda. Infraestructuras IoT eficientes para localización indoor. Industrial doctorate between UCAM and CENTIC. Directores: Dr. D. Joaquin Lasheras Velasco (CENTIC) y Dr. D. José M. Cecilia Canales (UCAM-UPV).
3.- Fredy Núñez Torres. 26/11/2021. Diseño y desarrollo de un modelo de desambiguación léxica automática para el procesamiento del lenguaje natural. Directores: Dr. D. Carlos Periñán-Pascual (UPV) y Dr. D. Carlos González Vergara (Pontificia Universidad Católica de Chile). Github: https://github.com/fredyrodrigors/tesis-phd
4.- Pablo Blanco Gómez. Evaluación de la utilidad de datos de satélite para modelización hidrológica de cuencas no aforadas en América Central. Industrial doctorate between UCAM and VIELCA Ingenieros. Directores: Dr. D. Javier Senent Aparicio (UCAM) y Dra. Dña. Patricia Jimeno Sáez (UCAM).
5.- Daniel Hernández Vicente. Análisis, diseño y evaluación de algoritmos nóveles de machine learning en entornos IoT. Subscribed to the doctoral programme in computer science at the UPV. Directores: Dr. D. Andrés Muñoz Ortega (UCAM) y Dr. D. José M. Cecilia Canales (UCAM-UPV).
6.- Miguel Ángel Guillén Navarro. Diseño e Implementación de un Sistema IoT para la Predicción de Heladas en Cultivos mediante técnicas de Análisis Inteligente de Datos. Subscribed to the doctoral program of information technologies and environmental engineering of UCAM. Directores: Dr. Dña. Raquel Martínez España (UCAM) y Dr. Dña. Belén López Ayuso (UCAM).
7.- Adrián López Ballesteros (Becario FPU). Desarrollo de un modelo integrado de ayuda a la decisión para la gestión de los recursos hídricos en zonas de agricultura intensiva. Subscribed to the doctoral program of information technologies and environmental engineering of UCAM. Director: Dr. D. Javier Senent Aparicio (UCAM).
8.- Sitian Liu. Impact of climate variability and human activities on water resources. Subscribed to the doctoral program of information technologies and environmental engineering of UCAM. Directores: Dr. D. Javier Senent Aparicio (UCAM) y Dr. D. Francisco Alcalá (IGME).
9.- Daria Mitroshenko. Design of an intelligent system to analyse sustainability in hospitality: A study in the Mediterranean south coast. Dr. D. Andrés Muñoz (UCAM) y Dra. Dña. Ginesa Martínez (UCAM).
It also lists the theses that have begun their research in line with the project and that are in the process of being defended:
1.- Doctoral thesis of Alicia Sepúlveda Muñoz. Desarrollo de aplicaciones nóveles basadas en sensores sociales. (in progress) Directores: Dr. D. Carlos Periñán Pascual (UPV) y Dr. D. José M. Cecilia Canales (UCAM-UPV)
2.- Doctoral thesis of Yolanda Blázquez López. Tratamiento de los Operadores Contextuales de Cambio de Polaridad en Español y en Inglés para la Minería de Opiniones. (ongoing; estimated defence date: July 2022) Directores: Dr. D. Carlos Periñán Pascual (UPV) y Dr. D. Ricardo Mairal Usón (UNED).
The main objective of this project is to develop a hardware and software infrastructure to enable the analysis of large amounts of data in real time in IoT environments in order to provide novel solutions for the management of water resources in the short, medium and long term. The specific points of interest and how they will be achieved are dealt with below in the form of objectives (O):
Objectives of the project (O):
O1.- Development of predictive machine learning models for the intelligent management of water resources. Physical sensors usually send information in the form of time series. This information is generated 24 hours a day, 365 days a year, generating a huge amount of data. Of particular interest for this project is the monitoring of variables that may influence the management of water resources. Among these variables we can highlight meteorological phenomena such as solar radiation, ambient temperature or wind speed, to mention a few examples or water consumption in homes or agricultural land. These types of variables are fundamental to the planning and management of water resources, and their analysis can predict adverse effects for their optimization. This objective proposes the development of predictive machine learning models to offer intelligent services that allow optimizing the management of water resources. Both the prediction of frosts and the prediction of water evaporation in irrigation ponds will be analysed as case studies. The need to have the value of these variables available in advance is essential in order to reduce the socio-economic impact associated with them. In addition, this project will work on modelling for the prediction of water quality in coastal lagoons. In particular, the Mar Menor will be analysed as a case study given the high socio-economic and environmental impact of its recent eutrophication. Finally, water consumption variables will be analyzed to establish consumption patterns by users in order to optimize access to water by users.
O2.- Detection of problems arising from the misuse or ineffective management of water resources through social sensors. Physical (or electronic) sensors provide quantitative information that can be enriched by information obtained through social sensors. The processing of large data streams via social networks such as Twitter or Facebook will make it possible to discover problems in our environment, thus providing relevant information for a specific IoT scenario. Through the development of a knowledge-based computer system, this project aims to convert the comments available on different social networks into valuable information for detecting problems arising from misuse or ineffective water management. This will be possible because, ultimately, the purpose of these social sensors is to immediately alert the responsible bodies to an environmental problem inferred from the complaints that citizens submit on social networks.
O3.- Fusion of data from different sources. Combining physical and social sensors in IoT scenarios can offer several advantages. Social sensors can: (1) add semantics to quantitative information from physical sensors, (2) provide information where physical sensors are unable to measure, or (3) validate physical sensor information. In fact, machine learning algorithms and visualization strategies are mandatory to combine both sources of information in order to provide real solutions in the field of water resources management.
O4. – Acceleration of machine-learning algorithms: The algorithms developed to meet the objectives O1, O2 and O3 will work with large data sets. In addition, these must be executed at a reduced interval to achieve real-time interactions. Therefore, a high-performance computing design is required to accelerate these algorithms. This project will analyze different processors available in an IoT infrastructure, from low-power processors based on heterogeneous architectures (Big.Little cores, HSA, TPUs) that are embedded in SBCs (Single Board Computers) such as Raspberry Pi or Arduino, to high-performance processors such as Nvidia GPUs, Intel Xeon Phi architectures located on high-performance servers.
O5.- Development of a functional prototype. This project will develop a functional prototype of the hardware-software infrastructure for the analysis of semantically enriched water resource management data. This will involve the deployment of a real IoT infrastructure in different terrains where water resource management is critical. In addition, a dashboard will be developed through a web application where data from physical and social sensors will be received and through which the most relevant indicators for the management of water resources will be visually analysed. Undoubtedly, this prototype will result in a real and concrete product that will offer an innovative service for the management of water resources.
O6.- Obtaining distributed hydrological models with a physical basis. The prediction of fundamental variables for the proper planning and management of water resources, such as the estimation of flows in unfinished basins or the prediction of sediment transport, will be evaluated by means of classical hydrological modelling in order to compare the results obtained with those from the application of machine learning techniques.
List of articles in international journals (JCR): The results obtained have been disseminated in journals and international congresses of recognized prestige. The main publications resulting from the results of the project are as follows:
1.- Gams, M., Gu, I. Y. H., Härmä, A., Muñoz, A., & Tam, V. (2019). Artificial intelligence and ambient intelligence. Journal of Ambient Intelligence and Smart Environments, 11(1), 71-86. Link
2.- GUILLEN-NAVARRO, M. A., MARTINEZ-ESPANA, R., Belen AYUSO & MORENO, L. (2019, August). An LSTM Deep Learning Scheme for Prediction of Low Temperatures in Agriculture. In Intelligent Environments 2019: Workshop Proceedings of the 15th International Conference on Intelligent Environments (Vol. 26, p. 130). IOS Press. Link
3.- Guillén‐Navarro, M. A., Martínez‐España, R., López, B., & Cecilia, J. M. (2019). A high‐performance IoT solution to reduce frost damages in stone fruits. Concurrency and Computation: Practice and Experience, e5299. COMPUTER SCIENCE, THEORY & METHODS [55/108] Q3. Link
4.- Cecilia, José M., and José M. García. “Re-engineering the ant colony optimization for CMP architectures.” The Journal of Supercomputing (2019): 1-22. COMPUTER SCIENCE, THEORY & METHODS [31/108] Q2. Link
5.- Terroso-Saenz, Fernando, Andres Muñoz, and José M. Cecilia. “QUADRIVEN: A framework for qualitative taxi demand prediction based on time-variant online social network data analysis.” Sensors 19.22 (2019): 4882. INSTRUMENTS & INSTRUMENTATION [15/64] Q1. Link
6.- Cecilia, J. M. (2020). Guest editors’ note: Special issue on novel high-performance computing algorithms and platforms in bioinformatics. The International Journal of High Performance Computing Applications, 34(1), 3–4 Enlace
7.- Guillén-Navarro, M. A., Martínez-España, R., Llanes, A., Bueno-Crespo, A., & Cecilia, J. M. (2020). A deep learning model to predict lower temperatures in agriculture. Journal of Ambient Intelligence and Smart Environments, (Preprint), 1-14. COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE [90/136] Q3. Link
9.- Cebrian, J. M., Imbernón, B., Soto, J., García, J. M., & Cecilia, J. M. (2020). High-throughput fuzzy clustering on heterogeneous architectures. Future Generation Computer Systems, 106, 401-411. COMPUTER SCIENCE, THEORY & METHODS [8/108] Q1. Link
10.- Terroso-Saenz, Fernando, and Andres Muñoz. “Land use discovery based on Volunteer Geographic Information classification.” Expert Systems with Applications 140 (2020): 112892. COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE [21/136] Q1. Link
11.- Fernández-Martínez, N.J. & Periñán-Pascual, C. LORE: a model for the detection of fine-grained locative references in tweets. Onomazein 52, 195-225. LINGUISTICS [169/183] Q4 Enlace
12.- Navarro, J. M., Martínez-España, R., Bueno-Crespo, A., Martínez, R., & Cecilia, J. M. (2020). Sound Levels Forecasting in an Acoustic Sensor Network Using a Deep Neural Network. Sensors, 20(3), 903 . INSTRUMENTS & INSTRUMENTATION [15/64] Q1. Link
13.- Jimeno-Sáez, P., Senent-Aparicio, J., Cecilia, J. M., & 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, 17(4), 1189. ENVIRONMENTAL SCIENCES [105/265] Q2. Link
14.- Guillén, M. A., Llanes, A., Imbernón, B., Martínez‑España, R., Bueno‑Crespo, A., Cano, J. C., & Cecilia, J. M. (2020). Performance evaluation of edge‑computing platforms for the prediction of low temperatures in agriculture using deep learning. JOURNAL OF SUPERCOMPUTING. COMPUTER SCIENCE, THEORY & METHODS [31/108] Q2. Link
15.- Muñoz, A., Park, J., Mouazen, A. M., de Oliveira, J. B., & Moshou, D. (2020). Smart environments and ambient intelligence in agricultural and environmental technology. Journal of Ambient Intelligence and Smart Environments, 12(5), 1-2. Link
16.- Jimeno-Sáez, P., Blanco-Gómez, P., Péres-Sánchez, J., Cecilia, J.M. & Senent- Aparicio, J. (2021). Impact Assessment of Gridded Precipitation Products on Streamflow Simulations over a Poorly Gauged Basin in El Salvador. Water 13 (18), 2497. WATER RESOURCES, [31/94] Q2. Link
17.- Cadenas, J. M., Garrido, M. C., Martínez-España, R., & Guillén-Navarro, M. A. (2020). Making decisions for frost prediction in agricultural crops in a soft computing framework. Computers and Electronics in Agriculture, 175, 105587. Link
18.- Cadenas, J. M., Garrido, M. C., & Martinez-España, R. (2020). Development of an application to make knowledge available to the farmer: Detection of the most suitable crops for a more sustainable agriculture. Journal of Ambient Intelligence and Smart Environments, 12(5), 419-432. Link
19.- Cecilia, J. M., Cano, J. C., Morales-García, J., Llanes, A., & Imbernón, B. (2020). Evaluation of Clustering Algorithms on GPU-Based Edge Computing Platforms. Sensors, 20(21), 6335. Link
20.- Andreo-Martínez, P., Ortiz-Martínez, V.M., Muñoz, A., Menchón-Sánchez, P., Quesada-Medina, J. (2021). A web application to estimate the carbon footprint of constructed wetlands, Environmental Modelling & Software, 135, 104898 Enlace
21.- Terroso-Sáenz, F., Muñoz, A., Arcas, F. (2021). Land-use dynamic discovery based on heterogeneous mobility sources, International Journal of Intelligent Systems, In press Enlace
22.- Guillén-Navarro, M. A., Martínez-España, R., Bueno-Crespo, A., Morales-García, J., Ayuso, B., & Cecilia, J. M. (2020). A decision support system for water optimization in anti-frost techniques by sprinklers. Sensors, 20(24), 7129. Link
23.- Cebrian, J. M., Imbernón, B., Soto, J., & Cecilia, J. M. (2021). Evaluation of Clustering Algorithms on HPC Platforms. Mathematics, 9(17), 2156. MATHEMATICS[24/330] Q1. Link
24.- FERNÁNDEZ MARTÍNEZ, N. J., & PERIÑÁN PASCUAL, C. (2020). Knowledge-based rules for the extraction of complex, fine-grained locative references from tweets. RaeL: Revista Electronica de Linguistica Aplicada, 19.
25.- Terroso-Sáenz, F., Muñoz, A., Fernández-Pedauye, J., & Cecilia, J. M. (2021). Human Mobility Prediction with Region-based Flows and Water Consumption. IEEE Access, 9, 88651 – 88663, doi: 10.1109/ACCESS.2021.3090582.
26.- Cecilia, J. M., Cano, J., Calafate, C. T., Manzoni, P., Perinan-Pascual, C., Arcas-Tunez, F., & Munoz-Ortega, A. (2021). WATERSensing: A smart warning system for natural disasters in Spain. IEEE Consumer Electronics Magazine. doi: 10.1109/MCE.2021.3063703.
27.- Senent-Aparicio, J., Jimeno-Sáez, P., López-Ballesteros, A., Giménez, J. G., Pérez-Sánchez, J., Cecilia, J. M., & Srinivasan, R. (2021). Impacts of swat weather generator statistics from high-resolution datasets on monthly streamflow simulation over Peninsular Spain. Journal of Hydrology: Regional Studies, 35, 100826. Link
28.- Senent-Aparicio, J., George, C., & Srinivasan, R. (2021). Introducing a new post-processing tool for the SWAT+ model to evaluate environmental flows. Environmental Modelling & Software, 136, 104944. Link
29.- Guillén, M. A., Llanes, A., Imbernón, B., Martínez-España, R., Bueno-Crespo, A., Cano, J. C., & Cecilia, J. M. (2021). Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning. The Journal of Supercomputing, 77(1), 818-840.
30.- Nakamura, K., Hernández, D., Cecilia, J. M., Manzoni, P., Zennaro, M., Cano, J. C., & Calafate, C. T. (2021). LADEA: A Software Infrastructure for Audio Delivery and Analytics. Mobile Networks and Applications, 1-7.
31.- Garrido, M. C., Cadenas, J. M., Bueno-Crespo, A., Martínez-España, R., Giménez, J. G., & Cecilia, J. M. (2022). Evaporation Forecasting through Interpretable Data Analysis Techniques. Electronics, 11(4), 536. ENGINEERING, ELECTRICAL & ELECTRONIC [145/273] Q3. Link
32.- Jimeno-Sáez, P., Martínez-España, R., Casalí, J., Pérez-Sánchez, J., Senent-Aparicio, J. (2022). A comparison of performance of SWAT and machine learning models for predicting sediment load in a forested Basin, Northern Spain. Catena, 212, 105953. WATER RESOURCES [12/98] Q1. Link
Internationalization activities
Thanks to this project, it has been possible to contact other international researchers in order to obtain international external funding. Specifically, the following international funding has been requested for this annuality:
1.- PRIMA S1 2019 FARMING SYSTEMS IA. The OPTIGREEN project was applied to this call, whose main objective was to develop intelligent techniques for the control and monitoring of sensorized greenhouses through the use of IoT technologies. 9 partners from Spain, France, Italy, Greece, Portugal and Morocco participated. NUTRICONTROL was the coordinator of the proposal. The UCAM team was led by Dr. Andrés Muñoz Ortega. The proposal was not selected for the final phase.
2.- Eurolab-4-HPC. The European project Eurolab-4-HPC 2019 has been requested for the market exploration of the technologies developed in this project.
3.- MSCA-ITN-2020: The UPV applied to a Marie-Curie action with the RIOT project: Enabling IoT in rural scenarios with a total budget of €3.6 million. An holistic approach, with a score of 72.2 out of 100.
4.- CHIST-ERA 2019 (Novel computational approaches for Environmental Sustainability). Members of the UPV and UCAM led by Dr. Cecilia applied to this call with the FloodingOT project with a total budget of €1,089,515.81.
5.- Research and Innovation action Topic FETPROACT-EIC-08-2020. The UPV, UCAM and VIELCA have applied within an international consortium to this call with the SMARTLAGOON project, with a total budget of around €4M.
It is also worth mentioning the organisation of two scientific events related to the project. On the one hand, Workshop “Intelligent Systems for Agriculture Production and Environment Protection (ISAPEP)” was organized in June 2019 in Rabat, Morocco within the International Conference on Intelligent Environments (IE) and is led by Dr. Andrés Muñoz Ortega. This was the 3rd edition of the workshop that attracts researchers involved in the application of intelligent systems to agricultural and environmental problems. In addition, the 8th International Conference on Meaning and Knowledge Representation (MKR) was held from 3 to 5 July 2019. As in previous editions, this conference focuses on studies related to the meaning and representation of knowledge in the multidisciplinary context of natural language understanding, not only from the perspective of linguistics and cognitive science, but also from the perspective of knowledge engineering and artificial intelligence, among other fields. Both events allowed for new contacts with other researchers.
Other financing proposals under preparation:
In addition to those described above and as a result of the research results that are emerging from the project, the consortium members are applying to other calls for projects which are listed below by categories:
COVIDSensing
The research group, formed by members of the Universitat Politècnica de València (UPV) and the Universidad Católica San Antonio de Murcia (UCAM), has developed a prototype tool of social sensors for the management of problems related to water as a result of activity 1. This tool, called WATERSensing, is based on the analysis of large amounts of information from different sources (e.g. social networks and physical sensors, among others) in real time. Thanks to the good preliminary results of WATERSensing and motivated by the health emergency caused by the COVID-19 disease, the research team formed by UPV, UCAM and VIELCA decided to direct all its resources to adapting WATERSensing to the management of the crisis caused by the pandemic, resulting in the prototype application COVIDSensing (COVIDSensing.com) and for which several grants have been requested, as listed below:
– Fondos Supera COVID-19 Santader-CRUE. “Sistema de monitorización de las percepciones y preocupaciones de la gente respecto a la pandemia COVID-19”. Members: UCAM, UPV.
– La Caixa Foundation: “A social sensing strategy to build a systemic understanding of the socio-sanitary interrelations derived from the COVID-19 pandemic in Spain” Members: UCAM, UPV.
– Data-IA-COVID-19, Fundación BBVA: Diseño de un gemelo digital para la gestión eficiente de la pandemia COVID-19. Members: UCAM, UPV.
– Convocatoria FONDO-COVID19, Instituto de Salud Carlos III. COVIDSensing: Sistema de alertas basado en sensores sociales para el control epidemiológico. Memberss: UPV.
– Convocatoria COVID-19 A PROYECTOS DE I+D E INVERSIÓN 2020, CDTI. In writing. Members: UPV y VIELCA.
IoT + AI
– Retos de la sociedad. Ministry of Science and Innovation (Knowledge Generation) Social sensors in smart cities: merging multimodal information into social networks for problem detection in multiple domains. Members: UCAM, UPV.
– Becas Leonardo a Investigadores y Creadores Culturales 2020.: Infraestructuras IoT de altas prestaciones para el análisis de datos en tiempo real. Members: UPV
Dissemination and dissemination of results
– Ministerio de ciencia e innovación (FECYT). HYDROTWEET Reconstruction of avenues through citizen science and social networks. Members: UCAM.
Scientific outreach activities:
1.- Online Seminar Application of machine learning techniques for the management of water resources. Observatorio del Agua. Fundación Botín. 5 May 2020.
2.- MESA REDONDA: IoT, BIGDATA Y CONTROL INTELIGENTE AL SERVICIO DEL AGUA. Jornadas Smart Water. DIATIC 2019. May 17, 2019.
3.- Assembly of the ICT Technology Center, before the 50 partner companies of the center that can be consulted at https://centic.es/quienes-somos/nuestras-empresas/, where the results obtained from the project were exposed.
National and International Congresses:
1.- Guillén-Navarro, M Ángel; Martínez-España, Raquel; Bueno-Crespo, Andrés; Ayuso, Belén; Moreno, Jose Luis; Cecilia, José M; ,An LSTM Deep Learning Scheme for Prediction of Low Temperatures in Agriculture.,Intelligent Environments (Workshops),130-138,2019.
2.- Fernández-Martínez, Nicolás José; Periñán-Pascual, Carlos; Felices-Lago, Ángel Miguel; A linguistically-aware model for microtext geocoding. VIII International Conference on Meaning and Knowledge Representation, 2019.
3.- Carlos Periñán-Pascual, José M. Cecilia, Alicia Sepúlveda-Muñoz, Francisco Arcas-Túnez y Nicolás José Fernández-Martínez. Assessing the Impact of Tweets in Flood Events; 1st International Workshop on Social Media Analysis for Intelligent Environment (16th International Conference on Intelligent Environments), 2020.
4.- Fernández Pedauyé, Julio, Periñán-Pascual, Carlos, Arcas Túnez, Francisco y Cecilia Canales, José M. Evaluation of spaCy entity recognizer for crowdsensing; 1st International Workshop on Open and Crowdsourced Location Data (16th International Conference on Intelligent Environments), 2020.
5.- Cadenas, J. M., Garrido, M. C., & Martinez-España, R, Towards the characterization of agricultural regions based on weather conditions – Sustainable Agriculture. Intelligent Environments 2020: Workshop Proceedings of the 16th International Conference on Intelligent Environments (Vol. 28). 143-151 IOS Press, 2020.
6.- Nakamura, K., Manzoni, P., Zennaro, M., Cano, J. C., Calafate, C. T., & Cecilia, J. M. (2020, September). FUDGE: a frugal edge node for advanced IoT solutions in contexts with limited resources. In Proceedings of the 1st Workshop on Experiences with the Design and Implementation of Frugal Smart Objects (pp. 30-35).
7.- Fernández-Pedauye, J., Periñán-Pascual, C., Arcas-Túnez, F., & Cecilia, J. M. (2020). Enhancing the spaCy Named Entity Recognizer for Crowdsensing. In Intelligent Environments 2020 (pp. 361-367). IOS Press.
8.- García, J. M., Llanes, A., Tudela, B. I., & Cecilia, J. M. (2020). Performance Evaluation of Clustering Algorithms on GPUs. In Intelligent Environments 2020 Workshop Proceedings of the 16th International Conference on Intelligent Environments (pp. 400-409). IOS Press.
9.- Pascual, C. P., Cecilia, J. M., Muñoz, A. S., Túnez, F. A., & Martínez, N. J. F. (2020). Assessing the Impact of Tweets in Flood Events. In Intelligent Environments 2020 Workshop Proceedings of the 16th International Conference on Intelligent Environments (pp. 371-380). IOS Press.
10.- Fernández Martínez, Nicolás José and Carlos Periñán-Pascual (2020) “Knowledgebased rules for the extraction of complex, fine-grained locative references from tweets”. RAEL: Revista Electrónica de Lingüística Aplicada 19 (1), pp. 136-163..
11.- Pérez-Sánchez, J., Senent-Aparicio, J., Jimeno-Sáez, P., Casalí, J., Martínez-España, R. Comparison between SWAT and Machine Learning Techniques for Sediment Load Estimation in a Forested Basin. AGU Fall Meeting 2021
Organization of conferences and workshops
1.- International Workshop on Social Media Analysis for Intelligent Environment (SMAIE), organizado por Dra. Raquel Martínez-España y Dr. Andrés Bueno Crespo
2.- International Workshop on Intelligent Systems for Agriculture Production and Environment Protection (ISAPEP), organizado por Dr. Andrés Muñoz Ortega y Dr. José Martín Soriano-Disla
3.- International Workshop on Open and Crowdsourced Location Data(ISOCLOD), organizado por Dr. Fernando Terroso-Sáenz y Dr. Andrés Muñoz Ortega
4.- Special Track on IT for Environmental Intelligence as part of the ACM International Conference on Information Technology for Social Good (GoodIT 2021).
5.- IEEE/ACM DS-RT 2021 The 25th International Symposium on Distributed Simulation and Real Time Applications.
Stay of researchers
Dr. Javier Senent Aparicio
Entidad de realización: Aarhus University
City entidad realización: Aarhus, Dinamarca
Start-end date: 01/02/2020 – 30/04/2020
Duration: 3 months
Name of the programme: Beca Jiménez de la Espada – Fundación Séneca
Objectives of the stay: Posdoctoral
Dr. Javier Senent Aparicio
Entidad de realización: Texas A&M University
Type of entity: University
Faculty, institute, center: Ecosystem Science & Management Department
City entity realization: College Station, United States of America
Start-end date: 01/02/2019 – 30/04/2019
Duration: 3 months
Funding entity: Fulbright Scholarship
Type of entity: Foundation
Name of the programme: Programa Fulbright para Investigadores Posdoctorales
Objectives of the stay: Posdoctoral
Entrepreneurship activities
Researchers José M. Cecilia and Andrés Muñoz have participated in two scientific entrepreneurship activities during the period of implementation of this project. Specifically, these activities are: Award and execution of the concept test “Market Exploration, Commercial Feasibility and Competitive Intelligence Processes of Social Sensor Analysis”, funded by the Fundación Séneca with €30,000 during 2019. Submission of a proposal for a scientific enterprise in the Spin-ON program organized by the Vice-Rectorate of Research and the Instituto Tecnológico de Murcia de la Universidad Católica San Antonio (UCAM). In evaluation.
Entrepreneurship activities
1.- Vielca Ingenieros will lead a research project to optimize the intelligent management of water resources Link
2.- Internet of Things” and Hidroconta technology for efficient water management Link
3. – Research to optimize water management receives 1. 3 M€ from the Ministry Link
4.- Derived news about the publication of WATERSensing Link
5.- Create an application that helps manage disasters through posts on social networks – Levante-EMV Link
6. – Valencian researchers develop a system that analyses floods from publications in networks Link
7.- WATERSensing, an app that helps to manage adverse weather events in real time Link
8.- We spoke with Jasé María Cecilia, researcher at the UPV, about the App “Watersensing” Link
9.- An ‘app’ helps to manage disasters caused by major storms from publications on networks Link
10.- WaterSensing, the application that anticipates with social networks the crisis management of extreme weather events Link
1.- José M. Cecilia. Intervención en MESA REDONDA: IoT, BIGDATA Y CONTROL INTELIGENTE AL SERVICIO DEL AGUA. Guest Lecture at Smart Water Conference. DIATIC 2019 Murcia Thursday, May 16, 2019. Organized by the Professional College of Computer Engineers of the Region of Murcia
2.- José M. Cecilia. Online Seminar Application of machine learning techniques for water resources management. Observatorio del Agua. Fundación Botín. May 5, 2020.
3.- Andrés Muñoz. Webinar “Inteligencia Artificial: la Verdadera Revolución de Nuestra Sociedad”. Organized by Fundación Integra, 10 November 2020.
4.-SensingTools: Digitization for process optimization through data efficient data integration. Online seminar at Queens University. Friday, June 11, 2021.
5.-El uso de sensores sociales en la gestión de servicios públicos SensingTools. Within the “Asistencia Técnica para el Análisis de la Gestión Comercial” carried out for the Instituto Costarricense de Acueductos y Alcantarillados (AyA). May 6, 2021.
6.-El uso de sensores sociales en la gestión de servicios públicos SensingTools. As part of the “Asesoría sobre equipamiento y tecnología para optimizar la gestión comercial y la atención del Agua No Facturada” carried out for the Administración Nacional de Agua Potable y Saneamiento (ANDA) of the República de El Salvador. April 30, 2021.
7.-Fusión de Datos y análisis en tiempo real: claves para el incremento de la eficiencia. Online talk CENTIC Experts. December 10, 2020.
8.-Deep learning, sobrepasando la frontera del Machine Learning. Webinar CENTIC. December 1, 2020.
9.-Planificación y gestión de recursos hídricos a partir del análisis de datos de IoT (WateroT). Instituto Universitario de Investigación Informática. November 19, 2020.
10.-Seminario Online Aplicación de técnicas de machine learning para la gestión de los recursos hídricos. Observatorio del Agua. Fundación Botín. May 5, 2020.
