Search Results (681)

With the increase in climate-change-related hazardous events alongside population concentration in urban centres, it is important to provide resilient cities with tools for understanding and eventually preparing for such events. Machine learning (ML) and deep learning (DL) techniques have increasingly been employed to model susceptibility of hazardous events. This study consists of a systematic review of the ML/DL techniques applied to model the susceptibility of air pollution, urban heat islands, floods, and landslides, with the aim of providing a comprehensive source of reference both for techniques and modelling approaches. A total of 1454 articles published between 2020 and 2023 were systematically selected from the Scopus and Web of Science search engines based on search queries and selection criteria. ML/DL techniques were extracted from the selected articles and categorised using ad hoc classification. Consequently, a general approach for modelling the susceptibility of hazardous events was consolidated, covering the data preprocessing, feature selection, modelling, model interpretation, and susceptibility map validation, along with examples of related global/continental data. The most frequently employed techniques across various hazards include random forest, artificial neural networks, and support vector machines. This review also provides, per hazard, the definition, data requirements, and insights into the ML/DL techniques used, including examples of both state-of-the-art and novel modelling approaches. Full article

Dams significantly impact the environment, industries, residential areas, and agriculture. Efficient dam management can mitigate negative impacts and enhance benefits such as flood and drought reduction, energy efficiency, water access, and improved irrigation. This study tackles the critical issue of predicting dam occupancy levels precisely to contribute to sustainable water management by enabling efficient water allocation among sectors, proactive drought management, controlled flood risk mitigation, and preservation of downstream ecological integrity. Our research suggests that combining physical models of water inflow and outflow “such as evapotranspiration using the Penman–Monteith equation, along with parameters like water consumption, solar radiation, and rainfall” with data-driven models based on historical reservoir data is crucial for accurately predicting occupancy levels. We implemented various prediction models, including Random Forest, Extra Trees, Long Short-Term Memory, Orthogonal Matching Pursuit CV, and Lasso Lars CV. To strengthen our proposed model with robust evidence, we conducted statistical tests on the mean absolute percentage errors of the models. Consequently, we demonstrated the impact of physical model parameters on prediction performance and identified the best method for predicting dam occupancy levels by comparing it with findings from the scientific literature. Full article

Accurate river ice mapping is crucial for predicting and managing floods caused by ice jams and for the safe operation of hydropower and water resource facilities. Although satellite multispectral images are widely used for river ice mapping, atmospheric contamination limits their effectiveness. This study developed river ice mapping models for the Han River in South Korea using atmospherically uncorrected Landsat-8 Operational Land Imager (OLI) multispectral reflectance data, addressing atmospheric influences with a Random Forest (RF) classification approach. The RF-based river ice mapping models were developed by implementing various combinations of input variables, incorporating the Landsat-8 multispectral top-of-atmosphere (TOA) reflectance, normalized difference indices for snow, water, and bare ice, and atmospheric factors such as aerosol optical depth, water vapor content, and ozone concentration from the Moderate Resolution Imaging Spectroradiometer observations, as well as surface elevation from the GLO-30 digital elevation model. The RF model developed using all variables achieved excellent performance in the classification of snow-covered ice, snow-free ice, and water, with an overall accuracy and kappa coefficient exceeding 98.4% and 0.98 for test samples, and higher than 83.7% and 0.75 when compared against reference river ice maps generated by manually interpreting the Landsat-8 images under various atmospheric conditions. The RF-based river ice mapping model for the atmospherically corrected Landsat-8 multispectral surface reflectance was also developed, but it showed very low performance under atmospheric conditions heavily contaminated by aerosol and water vapor. Aerosol optical depth and water vapor content were identified as the most important variables. This study demonstrates that multispectral reflectance data, despite atmospheric contamination, can be effectively used for river ice monitoring by applying machine learning with atmospheric auxiliary data to mitigate atmospheric effects. Full article

Flashiness refers to the rapidity and frequency of fluctuations in river flow. It can provide insights into flooding, by capturing dramatic increases in river flow, as well as contaminant transport, relating to concentrations of diffuse pollution. Despite a very well gauged river system, there is limited research in Great Britain targeting this component of river flow. This study addresses that gap in knowledge, with a detailed spatio-temporal analysis of river flashiness in Great Britain. Using 513 gauging stations, with historical records of at least 30 years, the average Richards–Baker flashiness index (RBI¯) was calculated for 1990–2020, showing an overall west- (0.6–0.8) to east-coast (0.1–0.2) gradient, being higher in the west (with the exception of some gauges in the south-east). Employing random forest models, the main predictor for flashiness was found to be soil composition, with some additional region-specific predictors. These include flood attenuation by reservoirs and catchment areas, affecting flashiness in the north and west of Great Britain. Additionally, using a subset of 208 gauging stations with data recorded from 1970 to 2020, a temporal analysis examined significant breakpoints and/or trends in yearly flashiness, using the Pettitt test and Mann–Kendall trend test, respectively. Increases in flashiness were found mainly in the north-east and south-west of Great Britain, with implications in flooding and river health. On a seasonal scale, and using a monthly RBI¯, the timing of flashy events was found to oscillate between autumn and spring over the 50 years, gravitating around winter. Full article

The prediction of the liquid build-up height in gas wells is a crucial aspect of reservoir development and is essential for the efficient execution of drainage and gas extraction operations. Excessive liquid accumulation can lead to well flooding and operational shutdowns, resulting in significant economic losses. To prevent such occurrences, accurate estimation of the liquid height in gas well tubing is necessary. However, existing petroleum engineering models face numerous challenges in predicting liquid height, including complex theoretical solution steps and reliance on fundamental well parameters and extensive empirical data. The paper proposes an innovative blend of the Crayfish Optimization Algorithm (COA) with the eXtreme Gradient Boosting (XGBoost) methodology to forecast the liquid loading heights in gas wells. The COA is employed to optimize eight hyperparameters of the XGBoost, including the number of trees, maximum depth, minimum child weight, learning rate, minimum loss reduction, subsample, L1 regularization, and L2 regularization. After fine-tuning the hyperparameters, the XGBoost undergoes a retraining process, followed by an evaluation. Through comparative analysis with actual measurements from 32 wells in a gas field as well as support vector regression (SVR), XGBoost, random forest (RF), and PLATA (which predict liquid volume in the tubing and annulus), the proposed COA–XGBoost demonstrates a high degree of alignment with the measured values. It provides the most accurate predictions, with a mean relative error of only 2.25%. Compared with the traditional XGBoost, the COA–XGBoost reduced the mean relative error in predicting gas well tubing liquid loading height by 32.63%. Compared with the previous PLATA, the proposed model achieved a 3.52% decrease in mean relative error, enabling more accurate assessment of the severity of liquid loading in gas wells. Full article

Global warming and climate change cause large temperature oscillations and uneven annual rainfall patterns. The rainy cycles characterized by frequent high-intensity rainfall in the area of the Stubo–Rovni water reservoir, which in 2014 peaked at 129 mm of water in 24 h (the City of Valjevo, the Republic of Serbia), caused major floods in the wider area. Such extremes negatively affect erosion processes, sediment production, and the occurrence of flash floods. The erosion coefficient before the construction of the water reservoir was Z_m = 0.40, while the specific sediment production was about 916.49 m³∙km⁻²∙year⁻¹. A hydrological study at the profile near the confluence of the Jadar and Obnica rivers, i.e., the beginning of the Kolubara river, the right tributary of the Sava (in the Danube river basin), indicates that the natural riverbed can accommodate flows with a 20% to 50% probability of occurrence (about 94 m³/s), while centennial flows of about 218 m³/s exceed the capacities of the natural riverbed of the Jadar river, causing flooding of the terrain and increasing risks to the safety of the population and property. The paper presents the impacts of the man-made Stubo–Rovni water reservoir on the catchment area and land use as the primary condition for preventing erosion processes (specific sediment production has decreased by about 20%, the forest cover increased by about 25%, and barren land decreased by 90%). Moreover, planned and controlled management of the Stubo–Rovni reservoir has significantly influenced the downstream flow, reducing the risks of flash floods. Full article

Flood variability associated with urbanization, ecological change, and climatic change is of increasing economic and social concern in and around Flagstaff, Arizona, where flood hydrology is influenced by a biannual precipitation regime and the relatively unique geologic setting at the edge of the San Francisco Volcanic Field on the southern edge of the Colorado Plateau. There has been limited long-term gauging of the ephemeral channels draining the developed lands and dry coniferous forests of the region, resulting in a spaciotemporal gap in observation-based assessments of large-scale flooding patterns. We present new data from over 10 years of flood monitoring using a crest stage gauge network, combined with other channel monitoring records from multiple agency sources, to assess inter-decadal patterns of flood change in the area, with a specific emphasis on examining how various controls and disturbances have altered the character and seasonality of peak annual flows. Methods of analysis included the following: using Fisher’s Exact Test to compare the seasonality of flooding between historic data spanning the 1970s and contemporary data obtained since 2010; summarizing GIS-based spatial data and meteorological timeseries to characterize study catchment conditions and changes between flood study periods; and relating spatiotemporal patterns of flood seasonality and occurrences of notably large floods with catchment characteristics and environmental changes. Our results show systematic patterns and changes in Flagstaff-area flood regimes that relate to geologic and topographic controls of the varied catchment systems, and in response to records of climate variations and local catchment disturbances, including urbanization and, especially, high-severity wildfire. For most catchments there has been a shift from predominantly late winter to spring snowmelt floods, or mixed seasonal flood regimes, towards monsoon-dominated flooding, patterns which may relate to observed local warming and precipitation changes. Post-wildfire flooding has produced extreme flood discharges which have likely exceeded historical estimates of flood magnitude over decade-long monitoring periods by one to two orders of magnitude. We advocate for continued monitoring and the expansion of local stream gauge networks to enable seasonal, magnitude-frequency trend analyses, improved climate and environmental change attribution, and to better inform the many planned and ongoing flood mitigation projects being undertaken in the increasingly developed Flagstaff region. Full article

Understanding the dynamics of water bodies is crucial for managing water resources and protecting ecosystems, especially in regions prone to climatic extremes. The Tumen River Basin, a transboundary area in Northeast Asia, has seen significant water body changes influenced by natural and anthropogenic factors. Using Landsat 8 and Sentinel-1 data on Google Earth Engine, we systematically analyzed the spatiotemporal variations and drivers of water body changes in this basin from 2015 to 2023. The water body extraction process demonstrated high accuracy, with overall precision rates of 95.75% for Landsat 8 and 98.25% for Sentinel-1. Despite observed annual fluctuations, the overall water area exhibited an increasing trend, notably peaking in 2016 due to an extraordinary flood event. Emerging Hot Spot Analysis revealed upstream areas as declining cold spots and downstream regions as increasing hot spots, with artificial water bodies showing a growth trend. Utilizing Random Forest Regression, key factors such as precipitation, potential evaporation, population density, bare land, and wetlands were identified, accounting for approximately 81.9–85.3% of the observed variations in the water body area. During the anomalous flood period from June to September 2016, the Geographically Weighted Regression (GWR) model underscored the predominant influence of precipitation, potential evaporation, and population density at the sub-basin scale. These findings provide critical insights for strategic water resource management and environmental conservation in the Tumen River Basin. Full article

(1) Background: Willow forests are well established as nature-based solutions contributing to flood protection in the riverine environment. With climate change, storm surges in winter may increasingly expose downstream floodplain forests to seawater pulses. The effects of seawater pulses on willows are unknown, as previous studies focused on long-term exposure effects. (2) Methods: We studied the resilience of the floodplain forest foundation species Salix alba L. to seawater pulses during winter. This corresponds to the effects of storm surges in the North Sea region on floodplain willow trees in downstream river stretches during their dormant stage. Seawater pulses were applied from November to May on vegetative propagules. The plants were placed on flooding stairways at three levels in a mesocosm experiment under ambient conditions in Zealand, NL. (3) Results: Twice-applied 48 h seawater pulses during winter led to increasing salinity in the soil where vegetative propagules were placed. Ninety-five percent of the plants developed leaves, shoots, and roots, and juvenile trees were established in the following spring. Although the aboveground and belowground dry masses decreased with increasing short-term seawater flooding, they increased from April to May. (4) Conclusions: The seawater pulse caused a growth-delaying effect in the young experimental propagation plants of Salix alba. Contrary to earlier findings on the growth-inhibiting effects on S. alba under long-term salinity treatments, we show that S. alba is resilient to short-term seawater pulses experienced during the dormant (winter) stage. This is good news for the inclusion of S. alba in nature-based flood defense schemes in downstream riverine stretches. Full article

China experiences frequent heavy rainfall and flooding events, which have particularly increased in recent years. As flood storage zones (FDZs) play an important role in reducing disaster losses, their ecological restoration has been receiving widespread attention. Hongze Lake is an important flood discharge area in the Huaihe River Basin of China. Previous studies have preliminarily analyzed the protection of vegetation zones in the FDZ of this lake, but the future growth trend of typical vegetation in the area has not been considered as a basis for the precise protection of vegetation diversity and introductory cultivation of suitable species in the area. Taking the FDZ of Hongze Lake as an example, this study investigated the change trend of the suitability of typical vegetation species in the Hongze Lake FDZ based on future climate change and the distribution pattern of the suitable areas. To this end, the distribution of potentially suitable habitats of 20 typical vegetation species in the 2040s was predicted under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 climate scenarios using the latest Coupled Model Intercomparison Project CMIP6. The predicted distribution was compared with the current distribution of potentially suitable habitats. The results showed that the model integrating high-performance random forest, generalized linear model, boosted tree model, flexible discriminant analysis model, and generalized additive model had significantly higher TSS and AUC values than the individual models, and could effectively improve model accuracy. The high sensitivity of these 20 typical vegetation species to temperature and rainfall related factors reflects the climatic characteristics of the study area at the junction of subtropical monsoon climate and temperate monsoon climate. Under future climate scenarios, with reference to the current scenario of the 20 typical species, the suitability for Nelumbo nucifera Gaertn decreased, that for Iris pseudacorus L. increased in the western part of the study area but decreased in the eastern wetland and floodplain, and the suitability of the remaining 18 species increased. This study identified the trend of potential suitable habitat distribution and the shift in the suitability of various typical vegetation species in the floodplain of Hongze Lake. The findings are important for the future enhancement of vegetation habitat conservation and suitable planting in the study area, and have implications for the restoration and conservation of vegetation diversity in most typical floodplain areas. Full article

There has been growing interest in the application of smart technologies for hazard management. However, very limited studies have reviewed the trends of such technologies in the context of flash floods. This study reviews innovative technologies such as artificial intelligence (AI)/machine learning (ML), the Internet of Things (IoT), cloud computing, and robotics used for flash flood early warnings and susceptibility predictions. Articles published between 2010 and 2023 were manually collected from scientific databases such as Google Scholar, Scopus, and Web of Science. Based on the review, AI/ML has been applied to flash flood susceptibility and early warning prediction in 64% of the published papers, followed by the IoT (19%), cloud computing (6%), and robotics (2%). Among the most common AI/ML methods used in susceptibility and early warning predictions are random forests and support vector machines. However, further optimization and emerging technologies, such as computer vision, are required to improve these technologies. AI/ML algorithms have demonstrated very accurate prediction performance, with receiver operating characteristics (ROC) and areas under the curve (AUC) greater than 0.90. However, there is a need to improve on these current models with large test datasets. Through AI/ML, IoT, and cloud computing technologies, early warnings can be disseminated to targeted communities in real time via electronic media, such as SMS and social media platforms. In spite of this, these systems have issues with internet connectivity, as well as data loss. Additionally, Al/ML used a number of topographical variables (such as slope), geological variables (such as lithology), and hydrological variables (such as stream density) to predict susceptibility, but the selection of these variables lacks a clear theoretical basis and has inconsistencies. To generate more reliable flood risk assessment maps, future studies should also consider sociodemographic, health, and housing data. Considering future climate change impacts, susceptibility or early warning studies may be projected under different climate change scenarios to help design long-term adaptation strategies. Full article

Tidal freshwater forests are usually located at or above the level of mean high water. Some Louisiana coastal forests are below mean high water, especially bald cypress (Taxodium distichum (L.) Rich.) forests because flooding has increased due to the combined effects of global sea level rise and local subsidence. In addition, constructed channels from the coast inland act as conduits for saltwater. As a result, saltwater intrusion affects the productivity of Louisiana’s coastal bald cypress forests. To study the long-term effects of hydrology and salinity on the health of these systems, we fitted dendrometer bands on selected trees to record basal area increment as a measure of growth in permanent forest productivity plots established within six bald cypress stands. Three stands were in freshwater sites with low salinity rooting zone groundwater (0.1–1.3 ppt), while the other three had higher salinity rooting zone groundwater (0.2–4.9 ppt). Water level was logged continuously, and salinity was measured monthly to quarterly on the surface and in groundwater wells. Higher groundwater salinity levels were related to decreased bald cypress radial growth, while higher freshwater flooding increased radial growth. With these data, coastal managers can model rates of bald cypress forest change as a function of salinity and flooding. Full article

The correct prediction of heating network pipeline failure rates can increase the reliability of the heat supply to consumers in the cold season. However, due to the large number of factors affecting the corrosion of underground steel pipelines, it is difficult to achieve high prediction accuracy. The purpose of this study is to identify connections between the failure rate of heating network pipelines and factors not taken into account in traditional methods, such as residual pipeline wall thickness, soil corrosion activity, previous incidents on the pipeline section, flooding (traces of flooding) of the channel, and intersections with communications. To achieve this goal, the following machine learning algorithms were used: random forest, gradient boosting, support vector machines, and artificial neural networks (multilayer perceptron). The data were collected on incidents related to the breakdown of heating network pipelines in the cities of Kazan and Ulyanovsk. Based on these data, four intelligent models have been developed. The accuracy of the models was compared. The best result was obtained for the gradient boosting regression tree, as follows: MSE = 0.00719, MAE = 0.0682, and MAPE = 0.06069. The feature «Previous incidents on the pipeline section» was excluded from the training set as the least significant. Full article

Methane (CH₄) emissions exert large effects on the global climate. Tree stems are vital sources of emissions in ecosystem CH₄ budgets. This paper reviewed the number of publications, journals, authors, keywords, research hotspots, and challenges. A total of 990 articles from 2006 to 2022 were collected based on the Web of Science database. The intellectual base was analyzed using CiteSpace 6.3.1 and VOSviewer 1.6.20 softwares. The results illustrated a growing trend in the study of tree stem methane emissions. The United States was the most research-active country; however, the most active institution was the Chinese Academy of Sciences in China. The research on stem methane emission by Vincent Gauci, Katerina Machacova, Zhi-Ping Wang, Kazuhiko Terazawa, Kristofer R. Covey, and Sunitha R. Pangala has had a significant impact. Current research indicates that stem CH₄ emissions significantly vary among different tree species and are influenced by leaf type, forest type, tree height, whether the trees are alive or dead, and other environmental conditions (such as soil water content, air temperature, CO₂ fluxes, and specific density). Soil CH₄ fluxes and production by methanogens in heartwood were the primary sources of tree stem methane. Some pectin or cellulose from trees may also be converted into methane. Moreover, methane can be produced and released during the decomposition of deadwood by basidiomycetes. Furthermore, there are some trends and challenges for the future: (1) distinguishing and quantifying emissions from various sources; (2) accurately assessing the impact of floods on methane emissions is crucial, as the water level is the main factor affecting CH₄ emissions; and (3) addressing the limited understanding of the microbial mechanisms of methane production in different tree species and investigating how microbial communities affect the production and emission of methane is vital. These advances will contribute to the accurate assessment of methane emissions from global ecosystems. Full article

Sugarcane suffers from the increased frequency and severity of droughts and floods, negatively affecting growing conditions. Climate change has affected cultivation, and the growth dynamics have changed over the years. The identification of the development stages of sugarcane is necessary to reduce its vulnerability. Traditional methods are inefficient when detecting those changes, especially when estimating sugarcane maturity—a critical step in sugarcane production. Hence, the study aimed to develop a cost- and time-effective method to estimate sugarcane maturity using high spatial-resolution remote sensing data. Images were acquired using a drone. Field samples were collected and measured in the laboratory for brix and pol values. Normalized Difference Water Index, Green Normalized Difference Vegetation Index and green band were chosen (highest correlation with field samples) for further analysis. Random forest (RF), Support Vector Machine (SVM), and multi-linear regression models were used to predict sugarcane maturity using the brix and pol variables. The best performance was obtained from the RF model. Hence, the maturity index of the study area was calculated based on the RF model results. It was found that the field plot has not yet reached maturity for harvesting. The developed cost- and time-effective method allows temporal crop monitoring and optimizes the harvest time. Full article