The effects of climate change on water resources management have drawn worldwide attention. Water quality predictions that are both reliable and precise are critical for an effective water resources management. Although nonlinear biological and chemical processes occurring in a lake make prediction complex, advanced techniques are needed to develop reliable models and effective management systems. Artificial intelligence (AI) is one of the most recent methods for modeling complex structures. The applications of machine learning (ML), as a part of AI, in hydrology and water resources management have been increasing in recent years. In this paper, the ability of deep neural networks (DNNs) to predict the quality parameter of dissolved oxygen (DO), in Lake Kastoria, Greece, is tested. The available dataset from 11 November 2015, to 15 March 2018, on an hourly basis, from four telemetric stations located in the study area consists of (1) Chl-a (μg/L), (2) pH, (3) temperature—Tw (°C), (4) conductivity (μS/cm), (5) turbidity (NTU), (6) ammonia (NH₄, mg/L), (7) nitrate nitrogen (N–NO₃, mg/L), and (8) dissolved oxygen (DO) (mg/L). Feed-forward deep neural networks (FF-DNNs) of DO, with different structures, are tested for all stations. All the well-trained DNNs give satisfactory results. The optimal selected FF-DNNs of DO for each station with a high efficiency (NSE > 0.89 for optimal selected structures/station) constitute a good choice for modeling dissolved oxygen. Moreover, they provide information in real time and comprise a powerful decision support system (DSS) for preventing accidental and emergency conditions that may arise from both natural and anthropogenic hazards.

Climate change is impacting urban areas, especially through extreme rainfall that stresses conventional water management systems. Rainwater resulting from impervious runoffs, stormwater leads to an increase in the amount of wastewater that requires treatment and an overflow of the combined sewer system. Therefore, untreated wastewater is released into the surroundings or, in some cases, causes damage to urban systems. Nevertheless, many cities in the world are in the process of establishing a sustainable approach to urban water management. Sustainable stormwater management practices are essential for overcoming various environmental challenges and promoting community sustainability and resilience. The purpose of this paper is to communicate Oslo’s success in implementing sustainable stormwater management in urban areas. By analyzing successful cases, researchers may be able to record patterns that offer potential answers to the stormwater management challenge. The present case study presents an approach that can enlighten urban planners and policymakers about the potential outcomes of sustainable stormwater management under the conditions presented.

The scope of this paper is to evaluate the short-term predictive capacity of the stochastic models ARIMA, Transfer Function (TF) and Artificial Neural Networks for water parameters, specifically for 1, 2 and 3 steps forward (m = 1, 2 and 3). The comparison of statistical parameters indicated that ARIMA models could be proposed as short-term prediction models. In some cases that TF models resulted in better predictions, the difference with ARIMA was minimal and since the latter are simpler in their construction, they are proposed for short-term prediction. Artificial Neural Networks didn’t show a good short-term predictive capacity in comparison with the aforementioned models.