Laura Melgar García

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Science, Biomedical Engineering

Scopus Publications

A novel incremental ensemble learning for real-time explainable forecasting of electricity price
Laura Melgar-García and Alicia Troncoso
Elsevier BV



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, and Dieu Tien Bui
MDPI AG
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, and Alicia Troncoso
Elsevier BV



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, and Alicia Troncoso
Elsevier BV



Streaming big time series forecasting based on nearest similar patterns with application to energy consumption
P Jiménez-Herrera, L Melgar-GarcÍa, G Asencio-Cortés, and A Troncoso
Oxford University Press (OUP)
AbstractThis 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, and Alicia Troncoso
Springer Nature Switzerland



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, and David Gutierrez-Aviles
Springer Nature Switzerland



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, and Alicia Troncoso
Springer Nature Switzerland



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, and Alicia Troncoso
Informa UK Limited
ABSTRACT 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.


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, Francisco Martínez-Álvarez, Alicia Troncoso, and Cristina Rubio-Escudero
Elsevier BV



Nearest neighbors with incremental learning for real-time forecasting of electricity demand
Laura Melgar-Garcia, David Gutierrez-Aviles, Cristina Rubio-Escudero, and Alicia Troncoso
IEEE
Electricity demand forecasting is very useful for the different actors involved in the energy sector to plan the supply chain (generation, storage and distribution of energy). Nowadays energy demand data are streaming data coming from smart meters and has to be processed in real-time for more efficient demand management. In addition, this kind of data can present changes over time such as new patterns, new trends, etc. Therefore, real-time forecasting algorithms have to adapt and adjust to online arriving data in order to provide timely and accurate responses. This work presents a new algorithm for electricity demand forecasting in real-time. The proposed algorithm generates a prediction model based on the K-nearest neighbors algorithm, which is incrementally updated as online data arrives. Both time-frequency and error threshold based model updates have been evaluated. Results using Spanish electricity demand data with a ten-minute sampling frequency rate are reported, reaching 2% error with the best prediction model obtained when the update is daily.


Generating a seismogenic source zone model for the Pyrenees: A GIS-assisted triclustering approach
José L. Amaro-Mellado, Laura Melgar-García, Cristina Rubio-Escudero, and David Gutiérrez-Avilés
Elsevier BV



Discovering three-dimensional patterns in real-time from data streams: An online triclustering approach
Laura Melgar-García, David Gutiérrez-Avilés, Cristina Rubio-Escudero, and Alicia Troncoso
Elsevier BV



Nearest Neighbors-Based Forecasting for Electricity Demand Time Series in Streaming
L. Melgar-García, D. Gutiérrez-Avilés, C. Rubio-Escudero, and A. Troncoso
Springer International Publishing



Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering
Laura Melgar-García, Maria Teresa Godinho, Rita Espada, David Gutiérrez-Avilés, Isabel Sofia Brito, Francisco Martínez-Álvarez, Alicia Troncoso, and Cristina Rubio-Escudero
Springer International Publishing



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, R. Pérez-Chacón, C. Rubio-Escudero, J. C. Riquelme, and A. Troncoso
Mary Ann Liebert Inc
This study proposes a novel bioinspired metaheuristic simulating how the coronavirus spreads and infects healthy people. From a primary infected individual (patient zero), the coronavirus rapidly infects new victims, creating large populations of infected people who will either die or spread infection. Relevant terms such as reinfection probability, super-spreading rate, social distancing measures, or traveling rate are introduced into the model to simulate the coronavirus activity as accurately as possible. The infected population initially grows exponentially over time, but taking into consideration social isolation measures, the mortality rate, and number of recoveries, the infected population gradually decreases. The coronavirus optimization algorithm has two major advantages when compared with other similar strategies. First, the input parameters are already set according to the disease statistics, preventing researchers from initializing them with arbitrary values. Second, the approach has the ability to end after several iterations, without setting this value either. Furthermore, a parallel multivirus version is proposed, where several coronavirus strains evolve over time and explore wider search space areas in less iterations. Finally, the metaheuristic has been combined with deep learning models, to find optimal hyperparameters during the training phase. As application case, the problem of electricity load time series forecasting has been addressed, showing quite remarkable performance.


High-content screening images streaming analysis using the STriGen methodology
Laura Melgar-García, David Gutiérrez-Avilés, Cristina Rubio-Escudero, and Alicia Troncoso
ACM
One of the techniques that provides systematic insights into biological processes is High-Content Screening (HCS). It measures cells phenotypes simultaneously. When analysing these images, features like fluorescent colour, shape, spatial distribution and interaction between components can be found. STriGen, which works in the real-time environment, leads to the possibility of studying time evolution of these features in real-time. In addition, data streaming algorithms are able to process flows of data in a fast way. In this article, STriGen (Streaming Triclustering Genetic) algorithm is presented and applied to HCS images. Results have proved that STriGen finds quality triclusters in HCS images, adapts correctly throughout time and is faster than re-computing the triclustering algorithm each time a new data stream image arrives.


A New Forecasting Algorithm Based on Neighbors for Streaming Electricity Time Series
P. Jiménez-Herrera, L. Melgar-García, G. Asencio-Cortés, and A. Troncoso
Springer International Publishing

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