Water demand forecasting: review of soft computing methods.

Demand forecasting plays a vital role in resource management for governments and private companies. Considering the scarcity of water and its inherent constraints, demand management and forecasting in this domain are critically important. Several soft computing techniques have been developed over the last few decades for water demand forecasting. This study focuses on soft computing methods of water consumption forecasting published between 2005 and 2015. These methods include artificial neural networks (ANNs), fuzzy and neuro-fuzzy models, support vector machines, metaheuristics, and system dynamics. Furthermore, it was discussed that while in short-term forecasting, ANNs have been superior in many cases, but it is still very difficult to pick a single method as the overall best. According to the literature, various methods and their hybrids are applied to water demand forecasting. However, it seems soft computing has a lot more to contribute to water demand forecasting. These contribution areas include, but are not limited, to various ANN architectures, unsupervised methods, deep learning, various metaheuristics, and ensemble methods. Moreover, it is found that soft computing methods are mainly used for short-term demand forecasting.

Working under the Shadow of Drones: Investigating Occupational Safety Hazards among Commercial Drone Pilots.

TECHNICAL ABSTRACTBackground: Commercial drones are rapidly transforming business operations, however there is a paucity of research evaluating occupational hazards and risks associated with drone deployment in the workplace.Purpose: We aimed to identify challenges of human-drone collaborations and assess drone pilot perceptions of workplace safety.Methods: An online questionnaire was generated and sent to 308 drone pilots working in different industries. A total of 75 of responses were included for data analysis. Descriptive statistics, principal component analysis, and association rule mining were employed to extract knowledge from the obtained data.Results: Our results indicate that human factors are the main contributors to workplace drone mishaps. Poor communication, information display, and control modes were found to be chief obstacles to effective human-drone collaboration. Drone pilots indicated a propensity for complying with and participating in safety practices. Following safety procedures, receiving technical training, and flying outdoors may all be associated with a lower risk of drone mishaps.Conclusions: Offering professional training to pilots and following safety procedures could decrease the risks associated with occupational drones.

A long-standing debate has surrounded the factors that lead to drone mishaps. The results of our study indicate that, from the perspective of drone pilots, situational awareness, decision-based, and skill-based errors are the primary human-factors relevant causes of drone mishaps. Additionally, deficiencies in drone interfaces should be addressed comprehensively to ensure humans can more precisely control drones. Our findings suggest that following safety procedures, receiving technical training, and flying outdoors were associated with a reduced risk of drone-related mishaps at work.

Forecasting ATM Cash Demand Before and During the COVID-19 Pandemic Using an Extensive Evaluation of Statistical and Machine Learning Models.

The overarching goal of this paper is to accurately forecast ATM cash demand for periods both before and during the COVID-19 pandemic. To achieve this, first, ATMs are categorized based on accessibility and surrounding environmental factors that significantly affect the cash withdrawal pattern. Then, several statistical and machine learning models under different algorithms and strategies are employed. In aiming to provide the feature matrix for machine learning models, some new influential variables are added to the literature. Finally, a modified fitness measure is proposed for the first time to correctly choose the most promising model by considering both the prediction errors and accuracy of direction's change simultaneously. The results obtained by a comprehensive analysis-a statistical analysis together with grid search and k-fold cross-validation techniques-reveal that (i) category-wise prediction enhances forecasting quality; (ii) before COVID-19 and in times when there are only minor disturbances in withdrawal patterns, forecasting quality is higher, and in general, the machine learning models can more appropriately forecast ATM's cash demand; (iii) despite studies in the literature, sophisticated models will not always outperform simpler models. It is found that during COVID-19 and in times when there is a sudden shock in demand and massive volatility in withdrawal patterns, the statistical models of the autoregressive integrated moving average (ARIMA) and seasonal ARIMA (SARIMA) can mainly provide better forecasting likely due to high performance of such models for short-term prediction, while minimizing overfitting. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42979-021-01000-0.

AN "ENVIRO-INFORMATIC" ASSESSMENT OF SAGINAW BAY (LAKE HURON, USA) PHYTOPLANKTON: DATA-DRIVEN CHARACTERIZATION AND MODELING OF MICROCYSTIS (CYANOPHYTA)(1).

Phytoplankton and Microcystis aeruginosa (Kütz.) Kütz. biovolumes were characterized and modeled, respectively, with regard to hydrological and meteorological variables during zebra mussel invasion in Saginaw Bay (1990-1996). Total phytoplankton and Microcystis biomass within the inner bay were one and one-half and six times greater, respectively, than those of the outer bay. Following mussel invasion, mean total biomass in the inner bay decreased 84% but then returned to its approximate initial value. Microcystis was not present in the bay during 1990 and 1991 and thereafter occurred at/in 52% of sample sites/dates with the greatest biomass occurring in 1994-1996 and within months having water temperatures >19°C. With an overall relative biomass of 0.03 ± 0.01 (mean + SE), Microcystis had, at best, a marginal impact upon holistic compositional dynamics. Dynamics of the centric diatom Cyclotella ocellata Pant. and large pennate diatoms dominated compositional dissimilarities both inter- and intra-annually. The environmental variables that corresponded with phytoplankton distributions were similar for the inner and outer bays, and together identified physical forcing and biotic utilization of nutrients as determinants of system-level biomass patterns. Nonparametric models explained 70%-85% of the variability in Microcystis biovolumes and identified maximal biomass to occur at total phosphorus (TP) concentrations ranging from 40 to 45 µg · L(-1) . From isometric projections depicting modeled Microcystis/environmental interactions, a TP concentration of <30 µg · L(-1) was identified as a desirable contemporary "target" for management efforts to ameliorate bloom potentials throughout mussel-impacted bay waters.