RESUMEN
Water is stored in reservoirs for various purposes, including regular distribution, flood control, hydropower generation, and meeting the environmental demands of downstream habitats and ecosystems. However, these objectives are often in conflict with each other and make the operation of reservoirs a complex task, particularly during flood periods. An accurate forecast of reservoir inflows is required to evaluate water releases from a reservoir seeking to provide safe space for capturing high flows without having to resort to hazardous and damaging releases. This study aims to improve the informed decisions for reservoirs management and water prerelease before a flood occurs by means of a method for forecasting reservoirs inflow. The forecasting method applies 1- and 2-month time-lag patterns with several Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), Artificial Neural Network (ANN), Regression Tree (RT), and Genetic Programming (GP). The proposed method is applied to evaluate the performance of the algorithms in forecasting inflows into the Dez, Karkheh, and Gotvand reservoirs located in Iran during the flood of 2019. Results show that RT, with an average error of 0.43% in forecasting the largest reservoirs inflows in 2019, is superior to the other algorithms, with the Dez and Karkheh reservoir inflows forecasts obtained with the 2-month time-lag pattern, and the Gotvand reservoir inflow forecasts obtained with the 1-month time-lag pattern featuring the best forecasting accuracy. The proposed method exhibits accurate inflow forecasting using SVM and RT. The development of accurate flood-forecasting capability is valuable to reservoir operators and decision-makers who must deal with streamflow forecasts in their quest to reduce flood damages.
RESUMEN
Water is a vital element that plays a central role in human life. This study assesses the status of indicators based on water resources availability relying on hydro-social analysis. The assessment involves countries exhibiting decreasing trends in per capita renewable water during 2005-2017. Africa, America, Asia, Europe, and Oceania encompass respectively 48, 35, 43, 20, and 5 countries with distinct climatic conditions. Four hydro-social indicators associated with rural society, urban society, technology and communication, and knowledge were estimated with soft-computing methods [i.e., artificial neural networks, adaptive neuro-fuzzy inference system, and gene expression programming (GEP)] for the world's continents. The GEP model's performance was the best among the computing methods in estimating hydro-social indicators for all the world's continents based on statistical criteria [correlation coefficient (R), root mean square error (RMSE), and mean absolute error]. The values of RMSE for GEP models for the ratio of rural to urban population (PRUP), population density, number of internet users and education index parameters equaled (0.084, 0.029, 0.178, 0.135), (0.197, 0.056, 0.152, 0.163), (0.151, 0.036, 0.123, 0.210), (0.182, 0.039, 0.148, 0.204) and (0.141, 0.030, 0.226, 0.082) for Africa, America, Asia, Europe and Oceania, respectively. Scalable equations for hydro-social indicators are developed with applicability at variable spatial and temporal scales worldwide. This paper's results show the patterns of association between social parameters and water resources vary across continents. This study's findings contribute to improving water-resources planning and management considering hydro-social indicators.
RESUMEN
This study deals with the development of an LED-curable methacrylated gelatin (GelMA) synthesis via microwave (MW) irradiation with a reaction and purification time-, energy-, and methacrylation reagent-saving approach. To investigate the efficiency of MW irradiation in GelMA synthesis, characteristics of the GelMAs prepared by using glycidyl methacrylate (GMA) or methacrylic anhydride (MA) via the MW-assisted (MWA) method were compared comprehensively with those synthesized via the conventional heating method. Moreover, MWA reaction conditions were optimized in terms of methacrylation reagent concentrations (C), reaction time (t), and MW power (P). Characterization and assessment of the GelMAs were conducted with 1H NMR, FT-IR, and Raman spectroscopy along with physical-mechanical, thermal, and hydrophilicity analysis. The results demonstrated that the MWA synthesized GMA-GelMA hydrogels were possessed of increased methacrylation degree (MD), gel fraction (GF), tensile strength (TS), elongation at break (EB), glass transition temperature (T g), and water contact angle (WCA) as well as decreased swelling degree (SD) values in comparison to those of MA-GelMA and GMA-GelMA hydrogels prepared via the MWA and conventional method, respectively. Enhanced properties of the MWA synthesized GMA-hydrogels suggested an effective methacryloyl conjugation leading to a greater amount of covalent crosslinking density justified by the dipolar moment calculations. Optimal GMA C, t, P, and purification time for a highly crosslinked GelMA hydrogel (MD: 96.1%, GF: 98.3%, SD: 10.11%, TS: 6.7 MPa, EB: 175.2%, T g: 75.34 °C, and WCA: 72.22°) were found to be a 5 times molar excess over the primary amine groups of gelatin, 5 min, 500 W, and 24 h, respectively. Thus, the optimized MW conditions offer a promising green method to prepare GelMAs for bio applications.