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Perturbations linked to the direct and indirect impacts of human activities during the Anthropocene affect the structure and functioning of aquatic ecosystems to varying degrees. Some perturbations involve stress to aquatic life, including soil and water acidification, soil erosion, loss of base cations, release of trace metals/organic compounds, and application of essential nutrients capable of stimulating primary productivity. Superimposed onto these changes, climate warming impacts aquatic environments via altering species’ metabolic processes and by modifying food web interactions. The interaction stressors is difficult to predict because of the differential response of species and taxonomic groups, interacting additively, synergistically, or antagonistically. Whenever different trophic levels respond differently to climate warming, food webs are restructured; yet, the consequences of warming-induced changes for the food web structure and long-term population dynamics of different trophic levels remain poorly understood. Such changes are crucial in lakes, where food web production is mainly due to ectotherms, which are highly sensitive to changes in their surrounding environment. Due to its remarkable physical inertia, including thermal stability, global warming also has a profound effect on groundwater ecosystems. Combining contemporary and palaeo data is essential to understand the degree to which mechanisms of stressors impact on lake biological communities and lake ecosystem functioning. The degree to which alterations can affect aquatic ecosystem structure and functioning also requires functional diversity to be addressed at the molecular level, to reconstruct the role different species play in the transfer of material and energy through the food web. In this issue, we present examples of the impact of different stressors and their interaction on aquatic ecosystems, providing long-term, metabolic, molecular, and paleolimnological analyses.

multivariate analyses --- risk assessment --- aquatic insects --- crustaceans --- lab-microcosms --- nonmetric multidimensional scaling --- adaptation --- porous aquifer --- PERMANOVA --- Planktothrix rubescens --- species conservation --- distribution patterns of species --- Cyanobacteria --- fossil Cladocera --- high throughput sequencing --- machine learning model --- stability --- small lakes --- environmental factor --- non-metric multi-dimensional scaling (NMDS) --- stream ecosystem --- lake vulnerability --- PCA --- functional diversity --- ecological resilience --- nitrification --- deep lake --- metabolism --- South–North Water Diversion Project --- endemic species --- EPT taxa --- trophic interactions --- stable isotope analysis --- environmental change --- bioassessment --- generalized procrustes analysis --- freshwater pollution --- colonization --- paleolimnology --- Tychonema bourrellyi --- plankton --- subalpine lakes --- random forest model --- Danjiangkou Reservoir --- trophic degree --- multiple scale --- biodiversity --- copepods --- zooplankton --- groundwater --- genetic variability --- respirometry --- ammonium impact --- Stable Isotopes Analysis --- trophic gradient --- seasonality

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The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things.

atmospheric --- on-line monitoring --- LoRa --- embedded system --- smart environments --- Internet of Things --- indoor occupancy --- machine learning --- data analysis --- landslide susceptibility --- China-Nepal Highway --- LSTM --- remote sensing images --- IoT --- network traffic --- monitoring --- DDoS --- packet classification --- indoor localization --- channel state information --- device-free passive --- WiFi fingerprint --- naive Bayes classification --- feature fusion --- posture recognition --- indoor positioning --- wireless body area network --- Kalman filtering --- multi-sensor combination --- prognostic and health management --- integrative framework --- internet of things --- convolutional neural network --- conditioned-based maintenance --- IoT platform --- intelligent monitoring robot --- active CCTV --- learning model --- electrical devices --- classification --- energy management --- smart environment --- architecture --- blockchain --- communication constraints --- decentralized application --- Ethereum --- Internet of things --- sensing and control --- computational efficiency --- robotic manipulators --- hysteresis --- adaptive control --- wireless sensor network (WSN) --- energy --- ant colony optimization (ACO) --- routing algorithm --- quantum-inspired evolutionary algorithms

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The impacts of climate change on water resource management, as well as increasingly severe natural disasters over the last decades, have caught global attention. Reliable and accurate hydrological forecasts are essential for efficient water resource management and the mitigation of natural disasters. While the notorious nonlinear hydrological processes make accurate forecasts a very challenging task, it requires advanced techniques to build accurate forecast models and reliable management systems. One of the newest techniques for modeling complex systems is artificial intelligence (AI). AI can replicate the way humans learn and has great capability to efficiently extract crucial information from large amounts of data to solve complex problems. The fourteen research papers published in this Special Issue contribute significantly to the uncertainty assessment of operational hydrologic forecasting under changing environmental conditions and the promotion of water resources management by using the latest advanced techniques, such as AI techniques. The fourteen contributions across four major research areas: (1) machine learning approaches to hydrologic forecasting; (2) uncertainty analysis and assessment on hydrological modeling under changing environments; (3) AI techniques for optimizing multi-objective reservoir operation; (4) adaption strategies of extreme hydrological events for hazard mitigation. The papers published in this issue will not only advance water sciences but also help policymakers to achieve more sustainable and effective water resource management.

water resources management --- landslide --- dammed lake --- flood risk --- time-varying parameter --- GR4J model --- changing environments --- temporal transferability --- western China --- cascade hydropower reservoirs --- multi-objective optimization --- TOPSIS --- gravitational search algorithm --- opposition learning --- partial mutation --- elastic-ball modification --- Snowmelt Runoff Model --- parameter uncertainty --- data-scarce deglaciating river basin --- climate change impacts --- generalized likelihood uncertainty estimation --- Yangtze River --- cascade reservoirs --- impoundment operation --- GloFAS-Seasonal --- forecast evaluation --- small and medium-scale rivers --- highly urbanized area --- flood control --- whole region perspective --- coupled models --- flood-risk map --- hydrodynamic modelling --- Sequential Gaussian Simulation --- urban stormwater --- probabilistic forecast --- Unscented Kalman Filter --- artificial neural networks --- Three Gorges Reservoir --- Mahalanobis-Taguchi System --- grey entropy method --- signal-to-noise ratio --- degree of balance and approach --- interval number --- multi-objective optimal operation model --- feasible search space --- Pareto-front optimal solution set --- loss–benefit ratio of ecology and power generation --- elasticity coefficient --- empirical mode decomposition --- Hushan reservoir --- data synthesis --- urban hydrological model --- Generalized Likelihood Uncertainty Estimation (GLUE) --- Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) --- uncertainty analysis --- NDVI --- Yarlung Zangbo River --- machine learning model --- random forest --- Internet of Things (IoT) --- regional flood inundation depth --- recurrent nonlinear autoregressive with exogenous inputs (RNARX) --- artificial intelligence --- machine learning --- multi-objective reservoir operation --- hydrologic forecasting --- uncertainty --- risk