In recent years, many cities have suffered from a shortage of drinking water, mainly due to population growth. Hence, the desire to curb undue water consumption through the identification of the main factors affecting consumer behavior has become very important in managing drinking water supplies. Modifying the consumption pattern means institutionalizing of a sustainable culture in water consumption among consumers and the identification of the main criteria affecting their behavior. In 2018, a survey was applied to examine the role of mass media advertising in modifying the water consumption pattern in Iran. An integration of fuzzy AHP and fuzzy VIKOR was proposed based on group decision making, and fuzzy trapezoidal sets used to model linguistic variables and to deal with uncertainty in opinions. We devised and conducted a questionnaire with 24 main criteria and 8 sub-criteria to measure the impact of advertising on water consumption. The case study population in this study included all urban households over 15 cities of Iran. A total of 5630 questionnaires were distributed among the various populations with cluster method. Then, by analyzing the results, advertisements using animation had the highest impact on consumer behavior, among the available alternatives, and could play a significant role in modifying the water consumption pattern. Additionally, a fuzzy evaluation technique is performed to validate the result of the applied method. Subsequently, a sensitivity analysis was conducted to validate the stability of the final ranking. Finally, the prioritization results of the types of advertising by the proposed method were compared with the results of the fuzzy AHP method.

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Precipitation as a key parameter in hydrometeorology and other water-related applications always needs precise methods for assessing and predicting precipitation data. In this study, an effort has been conducted to downscale and evaluate a satellite precipitation estimation (SPE) product using artificial neural networks (ANN), and to impose a residual correction method for five separate daily heavy precipitation events localized over northeast Austria. For the ANN model, a precipitation variable was the chosen output and the inputs were temperature, MODIS cloud optical, and microphysical variables. The particle swarm optimization (PSO), imperialist competitive algorithm,(ICA), and genetic algorithm (GA) were utilized to improve the performance of ANN. Moreover, to examine the efficiency of the networks, the downscaled product was evaluated using 54 rain gauges at a daily timescale. In addition, sensitivity analysis was conducted to obtain the most and least influential input parameters. Among the optimized algorithms for network training used in this study, the performance of the ICA slightly outperformed other algorithms. The best-recorded performance for ICA was on 17 April 2015 with root mean square error (RMSE) = 5.26 mm, mean absolute error (MAE) = 6.06 mm, R 2 = 0.67, bias = 0.07 mm. The results showed that the prediction of precipitation was more sensitive to cloud optical thickness (COT). Moreover, the accuracy of the final downscaled satellite precipitation was improved significantly through residual correction algorithms.As precipitation varies greatly in space and time, gridded precipitation data in high spatiotemporal resolution is a considerable requirement as input for hydrometeorological and water resources management applications. This type of data is highly important for timely action (e.g., the initiation of landslide and mudslide movement) and decision making, such as evacuating an area with high potential for flooding, or to secure food and water supply [1]. However, high spatiotemporal resolution datasets are usually available only on a country level or cover a specific geographical region. These type of datasets might be obtained by interpolation of in situ observations and reanalysis products, or derived through remote sensing observations [2].The monitoring of precipitation from space opens a new era of precipitation observation. Hence, precipitation and hydrologic cycle studies are a hot topic in atmospheric science. High-resolution satellite precipitation estimates (SPEs) provide an effective global source of uninterrupted data for various water-related applications, especially over regions where ground-based observations are often lacking or sparse. These factors are underlying reasons why the increasing use of satellite data, which provide much better geographical coverage despite the potential limited accuracy, depends on precipitation products. The limited accuracy could be caused by various reasons e.g., algorithm, orographic effects, cloudy and/or snowy conditions, the relatively s...

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