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Assessing and improving the quality of education in universities can play a prominent role in developing countries. This study aims to demonstrate an extensive methodology with a related algorithm for assessing the quality of education in Water Resource Engineering (WRE) based on Klein's learning model and using the hybrid fuzzy-AHP-TOPSIS (FAT) method. Four out of the top ten universities in Iran, including Iran University of Science and Technology (IUST), Amirkabir University of Technology (AUT), Shiraz University (SU), and Khajeh Nasir al-Din Toosi University of Technology (KUT), are considered as case studies. First, participants answered questions based on Klein's model so that the weight coefficients according to the fuzzy-AHP technique were extracted. Second, these coefficients were transferred to the TOPSIS environment, where the previously prioritized criteria were utilized to select the ideal solution. Finally, the relative closeness of universities (CC) as a performance evaluation criterion in the form of CC(IUST) = 0.54, CC(AUT) = 0.49, CC(SU) = 0.45, and CC(KUT) = 0.39 was obtained. The sensitivity analysis was performed based on the number and type of Klein's qualitative criteria on the model, and Fourier series expansion curves were used to better compare the results of the proposed algorithm. The presented algorithm in this research can be a good basis for education assessment models in universities. [ABSTRACT FROM AUTHOR]

Given the importance of input data, particularly precipitation, in hydrologic modeling for streamflow simulation, there has been growing emphasis on developing frameworks that harness multiple data sources concurrently to achieve more precise results. In the proposed framework of this study, which relies on the integrated capabilities of the multiobjective optimization model [nondominated sorting genetic algorithm-II (NSGA-II)], the ensemble Kalman filter data assimilation method, and data fusion, rainfall data from multiple sources are incorporated. The utilized framework leads to an improvement in the mean absolute relative error (MARE) index of streamflow simulation results. The innovation of the proposed methodology is the calculation of optimal weights corresponding to the simulated runoff time-series in the fusion model. This is accomplished through a competitive process among a multitude of optimized scenarios simulated within the framework provided. MARE as the main index identified in the objective functions and standard deviation, centered root mean square distance, and the correlation coefficient as auxiliary indices have been considered in this process. In this framework, satellite-based and in situ precipitation data sets are used as the forcing data. The main challenge has been to choose the greatest scenario for fusion among the selected scenarios, which the proposed methodology has overcome. The performance of the suggested methodology is demonstrated for the Siakh-Darengon catchment located in the Fars Province of Iran. According to the results, an average of 14.07% improvement in the MARE index has been achieved after applying the proposed methodology. By utilizing the proposed method, satellite-based rainfall data are integrated alongside ground-based rainfall data in the flood modeling process, resulting in enhanced accuracy in simulation outcomes within the utilized watersheds. Practical Applications: Today, influenced by factors such as climate change and anthropogenic alterations to the environment, the issue of flooding and its associated hazards has garnered unprecedented attention from researchers. One of the crucial components in flood modeling is rainfall data, which are collected through various means such as ground stations and satellite sensing instruments. In the past, the primary focus in the process of flood modeling has been on rainfall data recorded at ground stations; nowadays, efforts are being made to further enhance the role of satellite-derived rainfall data in flood modeling, aiming at enhancing their precision. In this study, a fusion model has been developed using the data fusion method and simultaneous utilization of ground-based and satellite rainfall data. Various flood simulation scenarios have been generated using a multiobjective optimization model, and the best scenario is selected through a competitive process. By implementing the proposed methodology in the Siakh-Darengon watershed located in Fars Province, Iran, improvements in simulation results have been achieved, resulting in based on the calculated performance indicators. [ABSTRACT FROM AUTHOR]

Human activities, climate change, and land-use alterations accelerated soil erosion in recent decades and imposed significant threats to soil fertility and stability worldwide. Understanding and quantifying the spatiotemporal variation of soil erosion risks is crucial for adopting the best management practices for surface soils conservation. Here, we present a novel high-resolution (30 m) soil erosion framework based on the G2 erosion model by integrating satellite and reanalysis datasets and Machine Learning (ML) models to assess soil erosion risks and hazards spatiotemporally. The proposed method reflects the impacts of climate change in 1 h time resolutions and land use in 30 m scales on soil erosion risks for almost 4 decades (between 1985 and 2017). The soil erosion hazardous maps were generated/evaluated using Extreme Value Analysis (EVA), utilizing long-term annual soil erosion estimations/projections to aid policymakers in developing management strategies to protect lands against extreme erosion. The proposed framework is evaluated in the Sultanate of Oman, which lacks soil erosion estimation/assessment studies due to data scarcity. Results indicate that soil erosion has increasing perilous trends in high altitudes of the Sultanate of Oman that may cause substantial risks to soil health and stability. [ABSTRACT FROM AUTHOR]

Designing reservoir outlets is a challenging subject in flood management among water resource managers. However, although several kinds of research are dedicated to flood mitigation strategies, studies considering flood uncertainties in reservoir outlet designs are rare in the literature. This study fills this knowledge gap by developing a MATLAB® Toolbox application, entitled Release Allocation and Weir Design (RAWD), based on MATLAB® App Designer, to obtain optimum reservoir outlet designs for effective flood management considering four outlets types, including an orifice as well as a proportional, a triangular, and a rectangular weir. The RAWD application handles uncertainties using non-linear interval number programming (NINP) and conditional value-at-risk (CVaR) approaches within an optimization model. Results show that the RAWD Toolbox application’s user can effectively and accurately meet downstream water demands and minimize water allocation shortages during the flood for a reservoir subjected to different shapes of inflow hydrographs. [ABSTRACT FROM AUTHOR]

Improving the quality of education in universities can play a prominent role in the development of countries. The purpose of this study is to develop a methodology for assessing the quality of education in Water Resources Engineering, one of the sub-disciplines of Civil Engineering, based on Klein's learning model and using the hybrid fuzzy-AHP-TOPSIS method. Four out of the top ten universities in Iran, including Iran University of Science and Technology (IUST), Amirkabir University of Technology (AUT), Shiraz University (SU), and Khajeh Nasir al-Din Toosi University of Technology (KUT) are considered as case studies. First, the weight coefficients were determined by surveying the students in the fuzzy environment using the AHP method, and then these coefficients were transferred to the TOPSIS environment. Finally, the relative closeness of universities (CC) as a performance evaluation criterion in the form of CC (IUST) = 0.54, CC (AUT) = 0.49, CC (SU) = 0.45, and CC (KUT) = 0.39 were obtained. The sensitivity analysis was performed based on the number and type of Klein's qualitative criteria on the model, and Fourier series expansion curves were used to observe the exact behavior of the model and better compare the results. This model of evaluation can have a considerable influence on the education methods improvement in Civil Engineering departments and related fields. [ABSTRACT FROM AUTHOR]