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.

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, R² = 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.

Climate change affects many meteorological parameters that could result in spatiotemporal variations of the hydrological cycle. These variations can affect local rainfall intensities or design storms; therefore, it is necessary to assess the local effects of climate change in different areas. Therefore, the current research aims at evaluating the accuracy of the precipitation data of the most recent Coupled Model Intercomparison Project phases 5 and 6 (CanESM2 from CMIP5 and CanESM5 from CMIP6 models), over a historical period from 1953 to 2010, as well as the predicted data for the future between 2010 and 2050 for the Quebec City rain gauge Station (Jean Lesage Intl). In this regard, precipitation data were analyzed using a statistical index to find the most accurate model for the study area. The results of this evaluation showed that CanESM5 is more accurate than CanESM2 for most of the evaluation indices. However, both of these models did not perform well since the precipitation prediction for CanESM5 (as the accurate model) R index was 0.48 for the monthly and was 0.75 in the seasonal scale. In addition, the Bias index revealed that both models underestimated rainfall prediction with negative index values for both scales and models. The trend of future precipitation under socio–economic scenarios (4.5 (pessimistic) and 8.5 (optimistic)) shows that the changes in future precipitation are not significant. In addition, for scenario 4.5, the trend of precipitation decreases for almost half of the year, while for scenario 8.5, the magnitude of the decrease and the number of months with a decreasing trend of precipitation are significantly reduced when compared to scenario 4.5.

Droughts have extensive consequences, affecting the natural environment, water quality, public health, and exacerbating economic losses. Precise drought forecasting is essential for promoting sustainable development and mitigating risks, especially given the frequent drought occurrences in recent decades. This study introduces the Improved Outlier Robust Extreme Learning Machine (IORELM) for forecasting drought using the Multivariate Standardized Drought Index (MSDI). For this purpose, four observation stations across British Columbia, Canada, were selected. Precipitation and soil moisture data with one up to six lags are utilized as inputs, resulting in 12 variables for the model. An exhaustive analysis of all potential input combinations is conducted using IORELM to identify the best one. The study outcomes emphasize the importance of incorporating precipitation and soil moisture data for accurate drought prediction. IORELM shows promising results in drought classification, and the best input combination was found for each station based on its results. While high Area Under Curve (AUC) values across stations, a Precision/Recall trade-off indicates variable prediction tendencies. Moreover, the F1-score is moderate, meaning the balance between Precision, Recall, and Classification Accuracy (CA) is notably high at specific stations. The results show that stations near the ocean, like Pitt Meadows, have higher predictability up to 10% in AUC and CA compared to inland stations, such as Langley, which exhibit lower values. These highlight geographic influence on model performance.

Accurate ice-period river monitoring is essential for characterizing ice jam floods (IJFs) and associated jam-release waves (JAVEs). However, physics-based and machine-learning (ML) approaches remain constrained by data scarcity, uncertainty, and incomplete observations. This study develops a physics-verified data fusion and preprocessing workflow combined with Bayesian-optimized ML classification (PV-DFBML) for six IJF/JAVE states at Beauceville on the Chaudière River. The workflow utilizes a three-stage data refinement pipeline. First, categorical fusion of remote sensing (Sentinel-1/2, Landsat-8, and MODIS) and meteorological data isolates physically relevant ice regimes. This is followed by spatiotemporal validation and gap-filling of water level data to preserve longitudinal hydraulic consistency, alongside multi-step anomaly screening (DBSCAN and CUSUM) cross-checked against upstream–downstream consistency to isolate sensor noise. Among five Bayesian-optimized classifiers, B-RF achieved the strongest performance under the common 5-fold validation framework, with BACC = 0.76, MCC = 0.89, and minority-class F1 = 0.83. Full-winter input records provided the strongest classification skill, while physics-verified preprocessing improved minority-event detection. Random undersampling modestly increased BACC and minority F1 to 0.79 and 0.84, respectively, while reducing MCC to 0.85, indicating a trade-off in imbalance treatment. More stringent leave-one-winter-out validation yielded mean BACC = 0.68 ± 0.10, MCC = 0.61 ± 0.13, and minority F1 = 0.65 ± 0.09, demonstrating greater difficulty in generalizing to unseen winters. Transfer to Saint-Joseph-de-Beauce without site-specific retraining further reduced BACC to 0.67, MCC to 0.71, and minority-class F1-score to 0.67. Overall, the results demonstrate the importance of physically consistent data refinement while highlighting limitations associated with rare-event representation and temporal and spatial generalization.