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Atmospheric extreme events cause severe damage to human societies and ecosystems. The frequency and intensity of extremes and other associated events are continuously increasing due to climate change and global warming. The accurate prediction, characterization, and attribution of atmospheric extreme events is, therefore, a key research field in which many groups are currently working by applying different methodologies and computational tools. Machine learning and deep learning methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric extreme events. This paper reviews machine learning and deep learning approaches applied to the analysis, characterization, prediction, and attribution of the most important atmospheric extremes. A summary of the most used machine learning and deep learning techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. The critical literature review has been extended to extreme events related to rainfall and floods, heatwaves and extreme temperatures, droughts, severe weather events and fog, and low-visibility episodes. A case study focused on the analysis of extreme atmospheric temperature prediction with ML and DL techniques is also presented in the paper. Conclusions, perspectives, and outlooks on the field are finally drawn. [ABSTRACT FROM AUTHOR]

Numerical association rule mining offers a very efficient way of mining association rules, where algorithms can operate directly with categorical and numerical attributes. These methods are suitable for mining different transaction databases, where data are entered sequentially. However, little attention has been paid to the time series numerical association rule mining, which offers a new technique for extracting association rules from time series data. This paper presents a new algorithmic method for time series numerical association rule mining and its application in smart agriculture. We offer a concept of a hardware environment for monitoring plant parameters and a novel data mining method with practical experiments. The practical experiments showed the method's potential and opened the door for further extension. [ABSTRACT FROM AUTHOR]

Urgent transition from the dependence on fossil fuels towards renewable energies requires more solar photovoltaic power to be connected to the electricity grids, with reliable supply through accurate solar radiation forecasting systems. This study proposes an innovative hybrid method that integrates convolutional neural network (CNN) with multi-layer perceptron (MLP) to generate global solar radiation (GSR) forecasts. The CMLP model first extracts optimal topological and structural features embedded in predictive variables through a CNN-based feature extraction stage followed by an MLP-based predictive model to generate the GSR forecasts. Predictive variables from observed data and global climate models (GCM) are used to predict GSR at six solar farms in Queensland, Australia. A hybrid-wrapper feature selection method using a random forest-recursive feature elimination (RF-RFE) scheme is used to eradicate redundant predictor features to improve the proposed CMLP model efficiency. The CMLP model has been compared and bench-marked against seven artificial intelligence–based and seven temperature-based deterministic models, showing excellent performance at all solar energy study sites tested over daily, monthly, and seasonal scales. The proposed hybrid CMLP model should be explored as a viable modelling tool for solar energy monitoring and forecasting in real-time energy management systems. [ABSTRACT FROM AUTHOR]

In this paper, we show a new clustering technique (k-gaps) aiming to generate a robust regionalization using sparse climate datasets with incomplete information in space and time. Hence, this method provides a new approach to cluster time series of different temporal lengths, using most of the information contained in heterogeneous sets of climate records that, otherwise, would be eliminated during data homogenization procedures. The robustness of the method has been validated with different synthetic datasets, demonstrating that k-gaps performs well with sample-starved datasets and missing climate information for at least 55% of the study period. We show that the algorithm is able to generate a climatically consistent regionalization based on temperature observations similar to those obtained with complete time series, outperforming other clustering methodologies developed to work with fragmentary information. k-Gaps clusters can therefore provide a useful framework for the study of long-term climate trends and the detection of past extreme events at regional scales. [ABSTRACT FROM AUTHOR]

The prediction of convective clouds formation is a very important problem in different areas such as agriculture, natural hazards prevention or transport-related facilities. In this paper, we evaluate the capacity of different types of evolutionary artificial neural networks to predict the formation of convective clouds, tackling the problem as a classification task. We use data from Madrid-Barajas airport, including variables and indices derived from the Madrid-Barajas airport radiosonde station. As objective variable, we use the cloud information contained in the METAR and SPECI meteorological reports from the same airport and we consider a prediction time horizon of 12 h. The performance of different types of evolutionary artificial neural networks has been discussed and analysed, including three types of basis functions (sigmoidal unit, product unit and radial basis function) and two types of models, a mono-objective evolutionary algorithm with two objective functions and a multi-objective evolutionary algorithm optimised by the two objective functions simultaneously. We show that some of the developed neuro-evolutionary models obtain high quality solutions to this problem, due to its high unbalance characteristic. [ABSTRACT FROM AUTHOR]

In the Era of exponential data generation, increasing the number of paleoclimate records to improve climate field reconstructions might not always be the best strategy. By using pseudo-proxies from different model ensembles, we show how biologically-inspired artificial intelligence can be coupled with different reconstruction methods to minimize the spatial bias induced by the non-homogeneous distribution of available proxies. The results indicate that small subsets of records situated over representative locations can outperform the reconstruction skill of the full proxy network, even in more realistic pseudo-proxy experiments and observational datasets. These locations highlight the importance of high-latitude regions and major teleconnection areas to reconstruct annual global temperature fields and their responses to external forcings and internal variability. However, low frequency temperature variations such as the transition between the Medieval Climate Anomaly and the Little Ice Age are better resolved by records situated at lower latitudes. According to our idealized experiments a careful selection of proxy locations should be performed depending on the targeted time scale of the reconstructed field. [ABSTRACT FROM AUTHOR]

The authors present a novel self-organized climate regionalization (CR) method that obtains a spatial clustering of regions, based on the explained variance of physical measurements in their coverage. This method enables a microscopic characterization of the probabilistic spatial extent of climate regions, using the statistics of the obtained clusters. It also allows for the study of the macroscopic behaviour of climate regions through time by using the dissimilarity among different cluster size probability histograms. The main advantages of the presented method, based on the Second-Order Data-Coupled Clustering (SODCC) algorithm, are that SODCC is robust to the selection of tunable parameters and that it does not require a regular or homogeneous grid to be applied. Moreover, the SODCC method has higher spatial resolution, lower computational complexity, and allows for a more direct physical interpretation of the outputs than other existing CR methods, such as Empirical Orthogonal Function (EOF) or Rotated Empirical Orthogonal Function (REOF). These facts are illustrated with an example of winter wind speed regionalization in the Iberian Peninsula through the period (1979 − 2014). This study also reveals that the North Atlantic Oscillation (NAO) has a high influence over the wind distribution in the Iberian Peninsula in a subset of years in the considered period. [ABSTRACT FROM AUTHOR]

In this paper, we tackle a problem of frequency assignment in Wi-Fi networks with a novel evolutionary-type algorithm. In this version of the problem, we consider the interferences originated by the access points, and also by the clients and all the 11 available channels in the 2.4 GHz Wi-Fi frequency band. The proposed evolutionary-type algorithm is the Coral Reefs Optimization approach with substrate layer (CRO-SL). It is a recently proposed algorithm, which simulates the processes which occur in real coral reefs, including the reproduction and fight for the space of living corals. This version of the algorithm includes a layer of "substrates" which allows using different search patterns jointly in the algorithm. This way, the CRO-SL is able to apply search patterns such as harmony search, differential evolution, Gaussian-based mutations and other traditional and novel search procedures, including local search algorithms, within a single population of solutions. We show the good performance of the proposed approach in a real case study of Wi-Fi frequency assignment, in the Polytechnic School building of the Universidad de Alcalá (Spain), where different realistic scenarios of the problem have been simulated and successfully solved with the CRO-SL algorithm. [ABSTRACT FROM AUTHOR]

Bangladesh, located in the Bay of Bengal, is a developing nation that is prone to devastating flood events that cause loss of lives and several other forms of humanatarian disasters. Identifying, developing and validating new scientific techniques that can be used for flood-risk warning and regular monitoring, including flood risk mitigation and adaptation, can help reduce the catastrophic effect of floods in Bangladesh and other developing countries. In this study, a daily Flood Index (IF) based on daily effective precipitation (PE) is utilized for quantifying floods for two cites in Bangladesh: Dhaka (23.7° N, 90.38° E) and Bogra (24.51° N; 89.22° E), where flooding is a common phenomenon. Based on total daily precipitation (P) data, in this study the IFis determined by calculating the PE using an exponentially decaying time-reduction function, which considers the gradual depletion of water resources over time. PE per day is normalized and compared with the average and standard deviation of yearly maximums, within the considered hydrological period. The start of a flood event is identified for IF ≥ 0. Subsequently, the IF severity ( I F acc ) as consecutively positive IF, duration (DF) as number of days with positive IF, and peak danger ( I F max ) as maximum IF are estimated for identified flood events, using the run-sum theory. The analysis carried out has accurately identified historical flood events at Dhaka station in 1984 and 2007 as having the largest I F acc value (i.e., greatest severity). Flood severity ( I F acc ) and peak danger ( I F max ) parameters have been verified by accumulated precipitation corresponding to the same period. For Bogra station, the 1998 and 1979 events were found to be the most intense. Seasonality analysis of the flood index shows most floods happened during the summer monsoon, although for the Dhaka station, flood events occurred between early June and November, and in Bogra, from late June to October. A rational study of effective precipitation provides a useful severity and danger indicator. The results indicate the practicality of daily IF for flood-risk assessments where the severity, peak danger, and duration need to be considered as a unified index for flood-risk monitoring. The proposed daily flood index can be useful for hydrologists for the purpose of daily operational flood monitoring in high flood-risk nations. [ABSTRACT FROM AUTHOR]

Overhead resource elements (REs) in Long Term Evolution (LTE) networks are used for some control, signaling and synchronization tasks at both the Physical level and Media Access Control sub-level. Accurately computing all the overhead REs is necessary to achieve an efficient system design, which is difficult because LTE is a complex standard that contains a large number of implementation flexibilities and system configurations. The number of such REs depends on both the system configurations and services demanded. Aiming at exploring the influence of overhead on LTE downlink performance, we first parametrize each system configuration—including parameters corresponding to enhancement techniques such as Adaptive Modulation and Coding and Multi-Antenna Transmissions techniques—and those of the resource allocation mechanisms (which depend on users’ services). Second, using such parametrization, we model all overheads for synchronization, controlling and signaling operations in LTE Physical Downlink Shared/Control Channels. This allows for dynamically computing the useful REs (by subtracting the overhead REs from the total ones), both per Transmission Time Interval (TTI) and per frame (and hence, the corresponding bit rates). Our data rate-based performance model is able to accurately compute: (1) the real, exact system data rate or “throughput” (instead of approximations); and (2) the maximum number of simultaneous multi-service users per TTI that is able to support (called here “capacity”). Aiming at understanding the impact of each overhead mechanism, we have carried out a variety of simulations, including different service provision scenarios, such as multi-user with multi-application. The simulation results prove our starting hypothesis that the influence of overhead on LTE performance should not be neglected. The parametrized and dynamic model quantifies to what extent throughput and capacity are modified by overhead—under a combination of system configurations and services, and may provide these performance metrics, throughput and capacity, as inputs to planning, dimensioning and optimization specialized tools. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel autonomous intelligent tool for the optimum design of a wireless relayed communication network deployed over disaster areas. The so-called dynamic relay deployment problem consists of finding the optimum number of deployed relays and their location aimed at simultaneously maximizing the overall number of mobile nodes covered and minimizing the cost of the deployment. In this paper, we extend the problem by considering diverse relay models characterized by different coverage radii and associated costs. To efficiently tackle this problem we derive a novel hybrid scheme comprising: (1) a Harmony Search (HS)-based global search procedure and (2) a modified version of the well-known K-means clustering algorithm as a local search technique. Single- and bi-objective formulations of the algorithm are proposed for emergency and strategic operational planning, respectively. Monte Carlo simulations are run over a emulated scenario based on real statistical data from the Castilla La Mancha region (center of Spain) to show that, in comparison with a standard implementation of the K-means algorithm followed by a exhaustive search procedure over all relay-model combinations, the proposed scheme renders on average better coverage levels and reduced costs providing, at the same time, an intelligent tool capable of simultaneously determining the number and models of the relays to be deployed. [ABSTRACT FROM AUTHOR]