Flooded Areas Research Articles

Optimization of Automatic Well Pattern Deployment in High Water-Cut Oilfield

Abstract In view of the problems such as a plurality of dominant water flow channels formed by flushing the reservoir and inferior development effect in the water injection oilfields, reconstructing the current well pattern and providing well pattern evaluation methods are important ways to enhance oil recovery by improving the injection–production relation and increasing the swept area of water flooding. However, the reservoir engineering methods, the simulation methods, and the artificial intelligence algorithms with few objectives enable comprehensive evaluation of the well pattern. In this article, considering multiple evaluation indexes in oilfield development by the glowworm swarm optimization algorithm and niche technology, automatic well pattern optimization is carried out. The glowworm swarm optimization algorithm has the advantage of efficient global search and simpler algorithm flow, which can speed up the convergence and reduce the parameter adjustment. The niche technology can better maintain the diversity of the solutions and solve the multimodal optimization problems more efficiently, accurately, and reliably. The new method was used to optimize the well pattern of one block in a water-flooding oilfield with high water-cut in a certain oilfield. The optimal well pattern is obtained by multiple iterations to maximize the control degree of the well pattern to the sand body. The results indicate that the injection production correspondence ratio and the reserves control degree of the well pattern to the sand body are improved by 4.48% and 7.94%, respectively.

Probabilistic Analysis of Floods from Tailings Dam Failures: A Method to Analyze the Impact of Rheological Parameters on the HEC-RAS Bingham and Herschel-Bulkley Models

The difficulty in determining the rheological characteristics of tailings inside reservoirs as well as their intrinsic variability adds uncertainty to tailings dam failures in flood studies. Uncertainty propagation in non-Newtonian hydrodynamic models stands as a great scientific challenge. This article explores the sensibility of tailings dam breach flood mapping to rheological parameters in Bingham and Herschel-Bulkley (H-B) models. The developed approach was based on the probabilistic Latin Hypercube Sampling of rheological parameters. It was automated to propagate uncertainty throughout multiple hydrodynamic simulations using the HEC-RAS v.6.1 software. Rheological parameter ranges and distributions were based on a broad bibliographic review. Bingham models were revealed to be more sensitive than H-B in terms of simulated min-max area values: for Bingham, flood areas, maximum depths, and arrival times varied by 17.9%, 9.3%, and 8.2%, respectively; for H-B, variations were 25.7%, 5.1%, and 3.9%. However, Bingham was less sensitive in terms of hydrodynamically associated probability: high probability ratios were related to a small range of simulated areas in Bingham, while H-B presented great variability. Finally, for each model, the parameters that affect uncertainty the most were identified, reinforcing the importance of determining them properly. Furthermore, the identified parameter ranges for both models should be valuable for defining variable value boundaries for flood sensitivity tests on specific tailings materials for other case studies. The automated algorithm can be used or adapted for specific tests with other hydrodynamic simulations.

THE NEST OF THE SCARLET-HEADED BLACKBIRD AMBLYRAMPHUS HOLOSERICEUS IN BRAZIL WITH NOTES ON ITS BREEDING BEHAVIOR

Although it is a common marsh bird of central Brazil, there is little information about the reproductive biology of the Scarlet-headed Blackbird Amblyramphus holosericeus. Here we present data from observations made of two nests —found in November 2005 and March 2008— amid the vegetation of a flooded area in the Pantanal of Mato Grosso, the central-west region of Brazil. The nests were built by the couple attached to marshy vegetation, at least 1 m above the surface of the water. The clutches consisted of two eggs with a greenish background color, spotted with fine brown striations and some small, spaced spots across the surface. In one of the nests the incubation period lasted 13 days. Both male and female cared for the chicks, who left the nest around 10 days after hatching. Despite its Least Concern global conservation status, the species could be experiencing a population decline, primarily due to the destruction of swamp areas and to frequent fires in much of the Pantanal region during drought periods.

Overlooking probabilistic mapping renders urban flood risk management inequitable

Characterizing flood-related hazards has mostly relied on deterministic approaches or, occasionally, on particular uncertainty sources, resulting in fragmented approaches. To analyze flood hazard uncertainties, a fully integrated floodplain modeling information system has been developed. We assessed the most relevant uncertainty sources influencing the European Floods Directive’s third cycle (2022–2027) concerning extreme flood scenarios (a 500-year flood) and compared the results to a deterministic approach. Flood hazards outputs noticeably differed between probabilistic and deterministic approaches. Due to flood quantiles and floodplain roughness characterization, the flood area is highly variable and subject to substantial uncertainty, depending on the chosen approach. Model convergence required a large number of simulations, even though flow velocity and water depth did not always converge at the cell level. Our findings show that deterministic flood hazard mapping is insufficiently trustworthy for flood risk management, which has major implications for the European Floods Directive’s implementation.

Analysing the Impact of Land Subsidence on the Flooding Risk: Evaluation Through InSAR and Modelling

Floods greatly impact human settlements in flood risk areas, such as floodplains and coastal lowlands, following heavy rainfall. The Alto Guadalentin valley, an orogenic tectonic depression, experiences extreme flash floods and land subsidence due to groundwater withdrawal, rendering it one of Europe's fastest subsiding regions. In this study, we compared two 2D flood event models representing different land subsidence scenarios for 1992 and 2016. To determine the flooded area and water depth variations due to land subsidence, the Hydrologic Engineering Centre River Analysis System 2D (HEC-RAS 2D) model was used to simulate flood inundation by the Alto Guadalentin River and its tributaries. Synthetic aperture radar (SAR) satellite (ERS, ENVISAT, and Cosmo-SkyMED) images were employed, along with the interferometric SAR (InSAR) technique, to calculate the magnitude and spatial distribution of land subsidence. By analysing the accumulated subsidence distributions obtained from InSAR, the original topography of the valley in 1992 and 2016 was reconstructed. These digital surface models (DSMs) were then used to generate 2D hydraulic models, simulating flood scenarios in the unsteady mode. The results demonstrated significant changes in the water surface elevation over the 14-year period, with a 2.04 km2 increase in areas with water depths exceeding 0.7 m. These findings were utilized to create a flood risk map and assess the economic flood risk. The data highlight the crucial role of land subsidence in determining the inundation risk in the Alto Guadalentin valley, providing valuable insights for emergency management and civil protection against future potential flooding events.

Effectiveness in storing and reducing floods of the red river upstream reservoirs under the incident of Lienmac dyke

The Red river and Thai Binh river’s dyke system includes 56 dikes classified from grade III to special grade with a total length of 2.207,75km which 37.709km of special grade, 553.70km of grade I, 502,43km of grade II and grade III. At the present, on the Red River and Thai Binh River systems, there are 230 existing weak locations, which have not been completely reinforced yet. It might cause unsafety situations of large flood flush and high water levels of rivers for a long time. Therefore, studying the solutions to support the response under unsafety situations of the dike system is a significant meaning topic. The present paper aims to evaluate the effectiveness of solutions using reservoirs at the Red river upstream in order to store and reduce floods in downstream. This also supports the effort to respond the urgent situations upstream such as huge floods, dyke failures in downstream and dyke failure at Lien Mac. In the scope of this paper, one and two-dimensional hydraulic simulation method was utilized, which describes the scenarios to regulate the upstream reservoir of the Red River and hydraulic calculation of the downstream river network. Then, the effectiveness of flood reduction in each scenario can be estimated. Simulation results show that depending on the calculation scenarios, the capacity of reservoirs can be 5.553 million m3 to cut and reduce flood for downstream. The flooded area might be decreased by 47.528 ha in case using a part of the capacity of the construction. The time when the reservoirs completely close the floodgates and do not discharge the flood into downstream is from 27 to 30 hours, this is an important time for the Lien Mac dike to be completely sealed within 24 hours. The time when the reservoirs completely close the floodgates and do not discharge the flood into downstream is from 27 to 30 hours, this is an important time for the Lien Mac dike to be completely closed within 24 hours.

Infectious diseases following hydrometeorological disasters: current scenario, prevention, and control measures.

Natural disasters are catastrophic occurrences that can seriously harm infrastructure, inflict property damage, and even result in fatalities. Water supply and sanitation systems can be disrupted in flooded areas, raising the risk of infectious diseases. It is advised that public health responders do a disease risk assessment of such a catastrophic event to ascertain the disaster's consequences and the health requirements. This editorial provides an overview of the transmission of infectious illnesses after hydrometeorological disasters. It also discusses the effects of such catastrophes on individuals' psychological and physical health who live in disaster-prone locations.

Flood hazard assessment in the Yesil River basin

Abstract Due to population growth and densification, and expanding construction, disaster risk in the world and its specific parts of the world is steadily increasing. Earthquakes cause the most damage, followed by floods. Floods are characterized by the highest frequency of events. In this research, annual data on 25 hydrological gauging stations of the considered territory were used to study the characteristics of maximum discharges and maximum levels of rivers in the Yesil water management basin. Data on maximum water levels and discharges are generalized because they are most important in the study of floods and the organization of flood control. It is the maximum level that determines the area and depth of flooding of territories. This study assessed the provided values of maximum levels and maximum water discharges by several methods (method of moments, the graph-analytical method for the full distribution, truncated distribution). For assessing flood damage, a map of the distribution of the maximum floodplain inundation depth over the basin area was constructed, taking into account the flood hazard classification by floodplain inundation layer. Quantitative assessment of hydrological extremes characterizing the danger of flooding has been carried out for gauging stations of Yesil water management basin during this research.

River flood risk assessment for the Chinese road network

Frequent floods substantially impact transportation infrastructure. This study assesses river flood risk to Chinese road networks by expected annual damage (EAD) and expected annual network efficiency loss (EAEL). The results show that the EAD of Chinese roads under river floods ranges from 2.04 to 3.42 billion USD, and the EAEL of 366 cities ranges from 0 to 31.0‰. The EAD high-risk cities are concentrated in areas of frequent flooding with high road network densities, while EAEL high-risk cities have relatively sparse roads and a high percentage of failed nodes and edges under river floods. For different income groups, the average EAD is the highest in the high income group, while EAD relative to the GDP and EAEL are the highest in the low and lower middle income groups, respectively, placing greater financial pressure on local governments. Our study provides an integrated view for assessing and managing road flood risk.

Assessment on the Efficiency of Flood Mitigation Measures

Storms and floods are frequent occurrences that can disrupt communities and harm ecosystems. An effective flood management plan requires a decision-making process that balances financial, social, and environmental benefits. This paper provides an overview of a study that applied a decision-making process using engineering analysis to determine the most effective approach in selecting the most effective flood mitigation measures. This study used hydrodynamic modelling to determine the effectiveness of proposed measures, such as the construction of a flood wall and river improvement works. The results showed that the combination of both measures could provide 100 ARI level protection, reducing the flood area from 6.11 km2 to 0.00 km2 areas without flooding. The study highlights the importance of considering financial, social, and environmental benefits in selecting effective flood mitigation measures.

Flash flood detection and susceptibility mapping in the Monsoon period by integration of optical and radar satellite imagery using an improvement of a sequential ensemble algorithm

Rainfall monsoons and the resulting flooding have always been cataclysmic disasters that have heightened global concerns in light of climate change. Flood susceptibility modeling is an indirect method for reducing flood disaster losses. This study aimed to improve flood susceptibility modeling by developing a sequential ensemble (extreme gradient boosting (XGBoost)) model utilizing three swarm-based algorithms (bacterial foraging optimization (BFO), cuckoo search (CS), and artificial bee colony (ABC) algorithms). Initially, an integration of optical (Landsat-8) and radar (Sentinel-1) satellite images were used to monitor the flooded areas during the July 2022 monsoon in the Kazerun region, Iran. A total of 1358 flood occurrence points were considered from the monitored flood areas; 70% (952 points) and 30% (406 points) were used for modeling and evaluating the models, respectively. Based on flood points and thirteen spatial criteria influencing floods, four flood models ((XGBoost, XGBoost-ABC, XGBoost-BFO, and XGBoost-CS)) were used to generate a flood susceptibility map (FSM). According to the results, the XGBoost-CS (area under the curve (AUC) = 0.96), XGBoost-BFO (AUC = 0.953), XGBoost-ABC (AUC = 0.941), and XGBoost (AUC = 0.939) models have greater accuracy in flood susceptibility modeling, respectively. The results indicated that the models coupled with the three metaheuristic algorithms (XGBoost-ABC, XGBoost-BFO, and XGBoost-CS) exhibited higher flood susceptibility modeling accuracy than the standalone XGBoost model.

Urban Flood Loss Assessment and Index Insurance Compensation Estimation by Integrating Remote Sensing and Rainfall Multi-Source Data: A Case Study of the 2021 Henan Rainstorm

To address the problems of traditional insurance compensation methods for flood losses, such as difficulty in determining losses, poor timeliness, a complicated compensation process and moral hazard, an urban flood index insurance tiered compensation model integrating remote sensing and rainfall multi-source data was proposed. This paper first extracted the area of water bodies using the Normalized Difference Water Index and estimates the urban flood area loss based on the flood loss model of remote sensing pixels. Second, the tiered compensation mechanism triggered by rainfall was determined, and the urban flood index insurance tiered compensation model was constructed using remote sensing and rainfall multi-source data. Finally, the economic losses and flood insurance compensation in urban flood were estimated. The results show that: (1) the geo-spatial distribution of flood-affected areas by remote sensing inversion is consistent with the actual rainfall characteristics of Henan Province, China; (2) based on the flood losses model of remote sensing pixels, the estimated flood losses for Henan Province are CNY 110.20 billion, which is consistent with the official data (accuracy ≥ 90%); and (3) the proposed model has good accuracy (R2 = 0.98, F = 1379.42, p < 0.05). The flood index insurance compensation in Henan Province is classified as a three-tier payout, with a total compensation of CNY 24,137 million. This paper can provide a new approach to estimate large-scale urban flood losses and the scientific design of flood index insurance products. It can also provide theoretical and technical support to many countries around the world, particularly those with underdeveloped flood insurance systems.

Estimation of expected peak discharge and flood volume of the Heliopolis basin, East Cairo, Egypt, using RS and WMS program

In recent years, rainfall-induced floods in various Egyptian towns affected several regions, and the area in east Cairo was the worst. The present study aims to detect the rainfall-runoff relationships of the Heliopolis watershed and its sub-basins, to estimate peak discharge and flood volume for the storm event (12 March 2020) and predicting the runoff volume and peak discharge of the flows for various return intervals. This was achieved by applying the Hydrological models (HEC-1and HMS) program using a unit Hydrograph namely SCS-CN. The drainage system and the identification of sub-basinswere established using SRTM DEM, 90 m resolution. The maximum daily rainfall per year was investigated and determined for various return times using the statistical analysis tool Hyfran. Results reveal that the peak discharge (Qp) and flood volume value (Qv) of the Heliopolis watershed are 686.8 m3/s and 48039.8 m3, respectively. The obtained results for the various recurrence times during the regression periods of 5 and 100 years, reveal that the peak flood inflow of the Heliopolis basin ranged between 19.47 and 742.16 m3/s and the flood volume ranged between 1,295,110 and 53,752,968 m3. This shows that these valleys move a significant volume of water to be released into the mainstream. The recognition and forecasting of the degree of dangerous rain events can help planners and decision-makers in finding flooded areas before the future development of the new settlements.

Flood susceptibility mapping using hybrid models optimized with Artificial Bee Colony

Floods are the most common type of natural hazard causing economic and human losses. Mapping the susceptibility to flooding is essential for the effective management of flood risk. This paper aims to propose and evaluate four new hybrid models. The Spercheios river basin in Greece was chosen as a case study. The ensemble Random Forest (RF) and Extreme Gradient Boosting (XGBoost) algorithms were combined with the statistical methods of Frequency Ratio (FR) and Weight of Evidence (WoE). The models optimized by the metaheuristic Artificial Bee Colony (ABC) method. Flood inventory of the study (564 locations) developed by Synthetic Aperture Radar (SAR) imagery and flood archive. Flood locations associated with twelve conditioning factors and the dataset randomly split into training and testing sets (70%-30%). Feature selection was performed using the Information Gain Ratio (IGR) method. Input data for the training of the models were the results of FR and WoE. Receiver Operating Characteristic (ROC) curve and statistical metrics were carried out for the validation of the models. It was proved that all models had an AUC value>0.95 and the most precise model was WoE-RF-ABC (AUC = 0.9675). Variable importance of factors showed agricultural lowlands and artificial areas are more likely to be flooded. This study showed that these four optimized hybrid models could accurately predict flood areas prone to flooding and help the decision-makers.

Water distribution based on SAR and optical data to improve hazard mapping

Climate projections foresee intense precipitation and long-term drought events is increasing with consequent rapid changes in surface water bodies in a short period. In areas with drastic hydrological changes, achieving accurate and rapid mapping of these phenomena in combination with hydrologic variability characteristics is a key of effective emergency management and disaster risk reduction plans. This study presents an automatic method for mapping drought and flood hazards, particularly in regions with significant hydrological changes. We use Sentinel-1/2 and Landsat data to extract surface water and classify permanent and seasonal water bodies in historical periods, which serve as the basis for identifying flood or drought areas. The water extraction method combines index-based analysis for optical data and the region-Otsu method for radar data, ensuring accurate identification of water. The effectiveness of this approach is demonstrated through comparisons with existing products in Poyang Lake (China), the Po River Plain (Italy), and the Indus River Plain (Pakistan). Findings show a high similarity between the two, and our results can provide more specific details. Our method is particularly well-suited for areas with fluctuating hydrological conditions, can also map quickly without optical data. By effectively identifying areas affected by drought and flood hazards while mitigating errors from natural hydrological dynamics, this methodology contributes valuable insights to enhance emergency management and disaster risk reduction plans.

Geographic-Information-System-Based Risk Assessment of Flooding in Changchun Urban Rail Transit System

The frequent occurrence of urban flooding in recent years has resulted in significant damage to ground-level infrastructure and poses a substantial threat to the metro system. As the central city’s core transportation network for public transit, this threat can have unpredictable consequences on travel convenience and public safety. Therefore, assessing the risk of urban flooding in the metro system is of utmost importance. This study is the first of its kind to employ comprehensive natural disaster risk assessment theory, establishing an assessment database with 22 indicators. We propose a GIS-based method combined with the analytical hierarchy process (AHP) and an improved entropy weight method to comprehensively evaluate the urban flood risk in Changchun City’s metro systems in China. This study includes a total of nine metro lines, including those that are currently operational as well as those that are in the planning and construction phases, situated in six urban areas of Changchun City. In this study, we utilize the regional risk level within the 500 m buffer zone of the metro lines to represent the flood risk of the metro system. The proposed method assesses the flood risk of Changchun’s rail transit system. The results reveal that over 30% of Changchun’s metro lines are located in high-risk flood areas, mainly concentrated in the densely populated and economically prosperous western part of the central city. To validate the risk assessment, we vectorized the inundation points and overlaid them with the regional flood risk assessment results, achieving a model accuracy of over 90%. As no large-scale flood events have occurred in the Changchun rail transit system, we employed receiver operating characteristic (ROC) curves to verify the accuracy of the flood risk assessment model, resulting in an accuracy rate of 91%. These findings indicate that the present study is highly reliable and can provide decision makers with a scientific basis for mitigating future flood disasters.

One Health Approach to Leptospirosis: Human-Dog Seroprevalence Associated to Socioeconomic and Environmental Risk Factors in Brazil over a 20-Year Period (2001-2020).

Despite being considered a neglected, re-emerging and the most widespread zoonotic disease worldwide, human-dog leptospirosis has not been subjected to One Health approach, and neither were its socioeconomic and environmental risk factors, as well as concomitant spatial analysis over time. Accordingly, notified human leptospirosis cases, incidence rate and urban hotspot areas, in addition to a systematic review of dog leptospirosis cases, were performed nationwide from 2001 to 2020 in Brazil. Data on Gross Domestic Product (GDP), flooding and study areas were also assessed and tabulated. Human-dog leptospirosis cases were simultaneously mapped with overlapping flooding areas, along with the main circulant serovars. Comparative outcome has shown that dogs may be exposed similarly to humans, becoming important sentinels and/or reservoirs for human leptospirosis in larger geographic areas. Moreover, the study herein can help in the decision and implementation of public policies in Brazil and may serve as a model for other tropical countries worldwide.

A new multi-source remote sensing image sample dataset with high resolution for flood area extraction: GF-FloodNet

ABSTRACT Deep learning algorithms show good prospects for remote sensing flood monitoring. They mostly rely on huge amounts of labeled data. However, there is a lack of available labeled data in actual needs. In this paper, we propose a high-resolution multi-source remote sensing dataset for flood area extraction: GF-FloodNet. GF-FloodNet contains 13388 samples from Gaofen-3 (GF-3) and Gaofen-2 (GF-2) images. We use a multi-level sample selection and interactive annotation strategy based on active learning to construct it. Compare with other flood-related datasets, GF-FloodNet not only has a spatial resolution of up to 1.5 m and provides pixel-level labels, but also consists of multi-source remote sensing data. We thoroughly validate and evaluate the dataset using several deep learning models, including quantitative analysis, qualitative analysis, and validation on large-scale remote sensing data in real scenes. Experimental results reveal that GF-FloodNet has significant advantages by multi-source data. It can support different deep learning models for training to extract flood areas. There should be a potential optimal boundary for model training in any deep learning dataset. The boundary seems close to 4824 samples in GF-FloodNet. We provide GF-FloodNet at https://www.kaggle.com/datasets/pengliuair/gf-floodnet and https://pan.baidu.com/s/1vdUCGNAfFwG5UjZ9RLLFMQ?pwd=8v6o.

The Timing of Global Floods and Its Association With Climate and Topography

AbstractUntil recently, the development of a global geography of floods was challenged by the fragmentation and heterogeneity of in situ data and the high costs of processing large amounts of remote sensing data. Such geography would facilitate the exploration of large‐scale drivers of flood extent and timing including wide latitudinal, climate, and topographic effects. Here we used a monthly data set spanning 30 years (Global Surface Water) to develop a worldwide geographical characterization of slow floods (1‐degree grid), weighting the relative contribution of seasonal, interannual, and long‐term fluctuations on overall variability, and quantified precipitation‐flooding delays where seasonality dominated. We explored the dominance of different flooding timings across five main Köppen‐Geiger climates and seven topography classes derived from modeled water table depths (i.e., hydro‐topography) to contribute top‐down insight about the most salient, cross‐regional flooding patterns and their likely large‐scale drivers. Our results showed that, globally, the mean extent of floods averaged 0.48% of the global land area, predominantly associated with hydro‐topography (>2× more extensive in flatter landscapes). Climate drove flood timings, with predictable, seasonally dominated fluctuations in cold regions, interannual, and mixed patterns in temperate climates, and more irregular (higher variability) and unpredictable (less seasonal) patterns in arid regions. Net gains of flooded area dominated temporal variability in 9% of the cells including boreal clusters likely affected by warming trends. We propose that this new geographical perspective of floods can aid different avenues of hydrological research in the upscaling and extrapolation of field studies and the parsimonious representation of floods in hydroclimatic models.

Machine learning algorithms for classifying flood areas from synthetic aperture radar images

El uso de imágenes de radar de apertura sintética (SAR) representa unafuente valiosa de información para caracterizar regiones geográficas susceptibles de inundaciones, como en el sureste de México, ya que éstas no son sensibles a condiciones de nubosidad y/u oscuridad. En estainvestigación se presenta una metodología para identificar cuerpos de agua en una región del sureste de México. Se aplicaron tres algoritmosde aprendizaje automático: bosque aleatorio (RF), potenciación delgradiente (GB) y máquina de soporte vectorial (SVM) para clasificar las tres clases objetivo A: agua, áreas inundadas y cuerpos de agua; I: infraestructura urbana y/o suelo desnudo, y V: vegetación a partir de imágenes SAR. La imagen SAR utilizada cubre una zona geográficaproyectada UTM Zona 15 Norte WGS84, localizada en los estados de Tabasco y Chiapas, la cual fue preprocesada para disminuir errores en la imagen. Los modelos RF, GB y SVM se implementaron en lenguaje Python,que fueron entrenados y probados en predicción a partir de una base de datos de 12 000 muestras, con valores de amplitud de la imagen SAR. El modelo RF obtuvo una precisión global de clasificación (푃푃푃푃) de 0.979(+/-0.003); GB obtuvo푃푃푃푃= 0.979(+/-0.003), y SVM푃푃푃푃 = 0.974(+/-0.005).Los tres modelos obtuvieron un valor de F1_score superior a 0.99 parapredecir la clase A; el clasificador RF obtuvo valores de 퐴퐴퐴퐴퐴퐴 = 1 para las tres clases objetivo evaluadas. Este estudio permite mostrar el uso potencial de las imágenes satelitales SAR y el alto desempeño de los modelos de aprendizaje automático RF, GB y SVM para clasificar e identificar los cuerpos de agua, así como resaltar su importancia en estudios de los posibles impactos de las inundaciones