Laura Melgar García

Verified @upm.es

Assisstant Professor / Artificial Intelligence Department
Universidad Politécnica de Madrid

Laura Melgar García


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https://researchid.co/lmelgar

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Science, Biomedical Engineering
21

Scopus Publications

Scopus Publications

  • Combining values and images in deep learning models for time series forecasting: An electricity market case study
    S. Sáez-Bombín, L. Melgar-García, A. Troncoso
    Energy and AI, 2026
  • Online forecasting using neighbor-based incremental learning for electricity markets
    L. Melgar-García, D. Gutiérrez-Avilés, C. Rubio-Escudero, A. Troncoso
    Neural Computing and Applications, 2025
    Electricity market forecasting is very useful for the different actors involved in the energy sector to plan both the supply chain and market operation. Nowadays, energy demand data are data coming from smart meters and have to be processed in real-time for more efficient demand management. In addition, electricity prices data can present changes over time such as new patterns and new trends. Therefore, real-time forecasting algorithms for both demand and prices have to adapt and adjust to online data in order to provide timely and accurate responses. This work presents a new algorithm for electricity demand and prices forecasting in real-time. The proposed algorithm generates a prediction model based on the k-nearest neighbors algorithm, which is incrementally updated in an online scenario considering both changes to existing patterns and adding new detected patterns to the model. Both time-frequency and error threshold based model updates have been evaluated. Results using energy demand from 2007 to 2016 and prices data for different time periods from the Spanish electricity market are reported and compared with other benchmark algorithms.
  • A novel method based on hybrid deep learning with explainability for olive fruit pest forecasting
    A. M. Chacón-Maldonado, L. Melgar-García, G. Asencio-Cortés, A. Troncoso
    Neural Computing and Applications, 2025
    Predicting the occurrence of crop pests is becoming a crucial task in modern agriculture to facilitate farmers’ decision-making. One of the most significant pests is the olive fruit fly, a public concern because it causes damage that compromises oil quality, increasing acidity and altering its flavor. This paper proposes a hybrid deep learning model to predict the presence of olive flies in crops. This model is based on an autoencoder and an automated deep feed-forward neural network. First, the autoencoder neural network learns a representation of the data and then the automated deep feed-forward neural network automatically determines the best values for the hyperparameters in order to obtain the prediction of the number of flies caught in traps from the dataset generated by the autoencoder. On the other hand, farmers to trust the proposed deep learning models need these models to be explainable. Thus, explainable artificial intelligence techniques are applied to the produced models to interpret the results. Results using a dataset from different sources such as satellite image band data, vegetation indices, and meteorological variables are reported. The performance of the proposed model has been compared with classical benchmark algorithms and a deep learning model recently published in the literature. In addition, the comparison includes the automated deep feed-forward neural network individually to show how the autoencoder network improves the accuracy of predictions.
  • A novel incremental ensemble learning for real-time explainable forecasting of electricity price
    Laura Melgar-García, Alicia Troncoso
    Knowledge Based Systems, 2024
  • Comparative Analysis of Deep Learning and Swarm-Optimized Random Forest for Groundwater Spring Potential Identification in Tropical Regions
    Viet-Ha Nhu, Pham Viet Hoa, Laura Melgar-García, Dieu Tien Bui
    Remote Sensing, 2023
    Identifying areas with high groundwater spring potential is crucial as it enables better decision-making concerning water supply, sustainable development, and the protection of sensitive ecosystems; therefore, it is necessary to predict the groundwater spring potential with highly accurate models. This study aims to assess and compare the effectiveness of deep neural networks (DeepNNs) and swarm-optimized random forests (SwarmRFs) in predicting groundwater spring potential. This study focuses on a case study conducted in the Gia Lai province, located in the Central Highland of Vietnam. To accomplish this objective, a comprehensive groundwater database was compiled, comprising 938 groundwater spring locations and 12 influential variables, namely land use and land cover (LULC), geology, distance to fault, distance to river, rainfall, normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), normalized difference water index (NDWI), slope, aspect, elevation, and curvature. The DeepNN model was trained and fine-tuned using the Adaptive Moment Estimation (ADAM) optimizer, while the SwarmRF model employed the Harris Hawks Optimizer (HHO) to search for optimal parameters. The results indicate that both the DeepNN model (accuracy = 77.9%, F-score = 0.783, kappa = 0.559, and AUC = 0.820) and the SwarmRF model (accuracy = 80.2%, F-score = 0.798, kappa = 0.605, and AUC = 0.854) exhibit robust predictive capabilities. The SwarmRF model displays a slight advantage over the DeepNN model in terms of performance. Among the 12 influential factors, geology emerges as the most significant determinant of groundwater spring potential. The groundwater spring potential maps generated through this research can offer valuable information for local authorities to facilitate effective water resource management and support sustainable development planning.
  • Identifying novelties and anomalies for incremental learning in streaming time series forecasting
    Laura Melgar-García, David Gutiérrez-Avilés, Cristina Rubio-Escudero, Alicia Troncoso
    Engineering Applications of Artificial Intelligence, 2023
  • A novel distributed forecasting method based on information fusion and incremental learning for streaming time series
    Laura Melgar-García, David Gutiérrez-Avilés, Cristina Rubio-Escudero, Alicia Troncoso
    Information Fusion, 2023
  • Streaming big time series forecasting based on nearest similar patterns with application to energy consumption
    P Jiménez-Herrera, L Melgar-GarcÍa, G Asencio-Cortés, A Troncoso
    Logic Journal of the Igpl, 2023
    This work presents a novel approach to forecast streaming big time series based on nearest similar patterns. This approach combines a clustering algorithm with a classifier and the nearest neighbours algorithm. It presents two separate stages: offline and online. The offline phase is for training and finding the best models for clustering, classification and the nearest neighbours algorithm. The online phase is to predict big time series in real time. In the offline phase, data are divided into clusters and a forecasting model based on the nearest neighbours is trained for each cluster. In addition, a classifier is trained using the cluster assignments previously generated by the clustering algorithm. In the online phase, the classifier predicts the cluster label of an instance, and the proper nearest neighbours model according to the predicted cluster label is applied to obtain the final prediction using the similar patterns. The algorithm is able to be updated incrementally for online learning from data streams. Results are reported using electricity consumption with a granularity of $10$ minutes for 4-hour-ahead forecasting and compared with well-known online benchmark learners, showing a remarkable improvement in prediction accuracy.
  • Identification of Anomalies in Urban Sound Data with Autoencoders
    Laura Melgar-García, Maryam Hosseini, Alicia Troncoso
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2023
  • A novel semantic segmentation approach based on U-Net, WU-Net, and U-Net++ deep learning for predicting areas sensitive to pluvial flood at tropical area
    Laura Melgar-García, Francisco Martínez-Álvarez, Dieu Tien Bui, Alicia Troncoso
    International Journal of Digital Earth, 2023
    Floods remain one of the most devastating weather-induced disasters worldwide, resulting in numerous fatalities each year and severely impacting socio-economic development and the environment. Therefore, the ability to predict flood-prone areas in advance is crucial for effective risk management. The objective of this research is to assess and compare three convolutional neural networks, U-Net, WU-Net, and U-Net++, for spatial prediction of pluvial flood with a case study at a tropical area in the north of Vietnam. They are relative new convolution algorithms developed based on U-shaped architectures. For this task, a geospatial database with 796 historical flood locations and 12 flood indicators was prepared. For training the models, the binary cross-entropy was employed as the loss function, while the Adaptive moment estimation (ADAM) algorithm was used for the optimization of the model parameters, whereas, F1-score and classification accuracy (Acc) were used to assess the performance of the models. The results unequivocally highlight the high performance of the three models, achieving an impressive accuracy rate of 96.01%. The flood susceptibility maps derived from this research possess considerable utility for local authorities, providing valuable insights and information to enhance decision-making processes and facilitate the implementation of effective risk management strategies.
  • Feature Selection Guided by CVOA Metaheuristic for Deep Neural Networks: Application to Multivariate Time Series Forecasting
    Manuel Jesús Jiménez-Navarro, Camilo Restrepo-Estrada, Laura Melgar-García, David Gutierrez-Aviles
    Lecture Notes in Networks and Systems, 2023
  • Explainable Artificial Intelligence for Education: A Real Case of a University Subject Switched to Python
    Laura Melgar-García, Ángela Troncoso-García, David Gutiérrez-Avilés, José Francisco Torres, Alicia Troncoso
    Lecture Notes in Networks and Systems, 2023
  • A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture
    Laura Melgar-García, David Gutiérrez-Avilés, Maria Teresa Godinho, Rita Espada, Isabel Sofia Brito, et al.
    Neurocomputing, 2022
  • Nearest neighbors with incremental learning for real-time forecasting of electricity demand
    Laura Melgar-Garcia, David Gutierrez-Aviles, Cristina Rubio-Escudero, Alicia Troncoso
    IEEE International Conference on Data Mining Workshops Icdmw, 2022
  • Discovering three-dimensional patterns in real-time from data streams: An online triclustering approach
    Laura Melgar-García, David Gutiérrez-Avilés, Cristina Rubio-Escudero, Alicia Troncoso
    Information Sciences, 2021
  • Generating a seismogenic source zone model for the Pyrenees: A GIS-assisted triclustering approach
    José L. Amaro-Mellado, Laura Melgar-García, Cristina Rubio-Escudero, David Gutiérrez-Avilés
    Computers and Geosciences, 2021
  • Nearest Neighbors-Based Forecasting for Electricity Demand Time Series in Streaming
    L. Melgar-García, D. Gutiérrez-Avilés, C. Rubio-Escudero, A. Troncoso
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2021
  • Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering
    Laura Melgar-García, Maria Teresa Godinho, Rita Espada, David Gutiérrez-Avilés, Isabel Sofia Brito, et al.
    Advances in Intelligent Systems and Computing, 2021
  • Coronavirus Optimization Algorithm: A Bioinspired Metaheuristic Based on the COVID-19 Propagation Model
    F. Martínez-Álvarez, G. Asencio-Cortés, J. F. Torres, D. Gutiérrez-Avilés, L. Melgar-García, et al.
    Big Data, 2020
  • High-content screening images streaming analysis using the STriGen methodology
    Laura Melgar-García, David Gutiérrez-Avilés, Cristina Rubio-Escudero, Alicia Troncoso
    Proceedings of the ACM Symposium on Applied Computing, 2020
  • A New Forecasting Algorithm Based on Neighbors for Streaming Electricity Time Series
    P. Jiménez-Herrera, L. Melgar-García, G. Asencio-Cortés, A. Troncoso
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2020