This research aims to estimate the overflow capacity of a curved labyrinth using different intelligent prediction models, namely the adaptive neural-fuzzy inference system, the support vector machine, the M5 model tree, the least-squares support vector machine and the least-squares support vector machine-bat algorithm (LSSVM-BA). A total of 355 empirical data for 6 different congressional overflow models were extracted from the results of a laboratory study on labyrinth overflow models. The parameters of the upstream water head to overflow ratio, the lateral wall angle and the curvature angle were used to estimate the discharge coefficient of curved labyrinth overflows. Based on various statistical evaluation indicators, the results show that those input parameters can be relied upon to predict the discharge coefficient. Specifically, the LSSVM-BA model showed the best prediction accuracy during the training and test phases. Such a low-cost prediction model may have a remarkable practical implication as it could be an economic alternative to the expensive laboratory solution, which is costly and time-consuming.

In this study, in addition to studying the effects of several LID schemes on urban ood control, the Analytic Hierarchy Process-Preference Ranking Organization Method for Enrichment Evaluation (AHP-PROMETHEE) combination method has been used to select the best design. This paper investigates the drainage system in Golestan town of Semnan under a 5-year return period. The LID methods have been selected based on the region's conditions and available facilities. Then Rain Barrel (RB), Permeable Pavement (PP), and In ltration Trench (IT) were considered as LID methods. Seven scenarios with the names RB, PP, IT, IT-PP, IT-RB, PP-RB, and IT-PP-RB have been considered to provide the best LID usage combination. Four analytical ranking criteria were selected for the ranking procedure, including implementation cost, hydraulic performance, environmental impact during implementation, and ease of implementation. Then the weight of these criteria was obtained using Analytic Hierarchy Process (AHP). Finally, after determining the weight criteria, the LID designs were ranked using the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method. The results of hydraulic studies indicate the effectiveness of the PP-RB scenario with an average reduction of 90% of peak discharge and an average reduction of 80% of total ood volume. Also, the weakest performance is related to the IT scenario, with an average decrease of 60% of peak ow and 47% of total ow volume. AHP-PROMETHEE analysis showed that the simultaneous use of RB and IT with a coverage percentage of 5% and a cost of $ 57,710 reduced the total volume by 51.54% and the peak discharge by 48.8% compared to the results of the current system. According to AHP-PROMETHEE, IT-RB-5 is the best project proposed among the 70 projects studied. This study showed that the AHP-PROMETHEE method could be used as a practical method to choose from several LID schemes for ood control.

Predicting the amount of sediment in water resource projects is one of the most important measures to be taken, while sediments have an unknown nature in their behavior. In this research, using the data recorded at the Mazrae station between 2002 and 2013, the amount of sediment in the catchment area of Maku Dam has been predicted using different models of intelligent algorithms. Recorded data including river flow (m3/s), sediment concentration (mg/L), and temperature (°C) were considered input data, and sediment load (ton/day) was considered output data. Initially, using the correlation test, the relationship between each input data with output data was considered. The results show high correlation of sediment concentration data and river flow with sediment load and low correlation of temperature data with these data. In order to find the best combination of data for prediction, the combination of single, binary, and triple data was considered in sensitivity analysis. In order to achieve the purpose of this study, first with the classical adaptive neuro-fuzzy inference system (ANFIS), the amount of sediment load was predicted, and then using evolutionary algorithms in ANFIS training, their performance was examined. The intelligent algorithms used in this study were ant colony optimization extended to continuous domain, particle swarm optimization, differential evolution, and genetic algorithm. The results showed that adaptive neuro-fuzzy inference system–ant colony optimization extended to continuous domain, adaptive neuro-fuzzy inference system–particle swarm optimization, adaptive neuro-fuzzy inference system–genetic algorithm, adaptive neuro-fuzzy inference system–differential evolution, and classical ANFIS had the best performance in predicting the amount of sediment load. In the meantime, it was observed that the coefficient of determination, root mean square error, and scatter index in the test mode for the adaptive neuro-fuzzy inference system–ant colony optimization extended to continuous domain algorithm with the best prediction dataset (sediment concentration + river flow) are equal to 0.991, 13.001, and (ton/day), 0.112, and those for the ANFIS with the weakest prediction (temperature + river flow) are equal to 0.490, 107.383 (ton/day), and 0.929, respectively. The present study showed that the use of intelligent algorithms in ANFIS training has been able to improve its performance in predicting the amount of sediment load in the catchment area of Maku Dam.

In this study, in addition to studying the effects of several LID schemes on urban flood control, the Analytic Hierarchy Process-Preference Ranking Organization Method for Enrichment Evaluation (AHP-PROMETHEE) combination method has been used to select the best design. This paper investigates the drainage system in Golestan town of Semnan under a 5-year return period. The LID methods have been selected based on the region's conditions and available facilities. Then Rain Barrel (RB), Permeable Pavement (PP), and Infiltration Trench (IT) were considered as LID methods. Seven scenarios with the names RB, PP, IT, IT-PP, IT-RB, PP-RB, and IT-PP-RB have been considered to provide the best LID usage combination. Four analytical ranking criteria were selected for the ranking procedure, including implementation cost, hydraulic performance, environmental impact during implementation, and ease of implementation. Then the weight of these criteria was obtained using Analytic Hierarchy Process (AHP). Finally, after determining the weight criteria, the LID designs were ranked using the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method. The results of hydraulic studies indicate the effectiveness of the PP-RB scenario with an average reduction of 90% of peak discharge and an average reduction of 80% of total flood volume. Also, the weakest performance is related to the IT scenario, with an average decrease of 60% of peak flow and 47% of total flow volume. AHP-PROMETHEE analysis showed that the simultaneous use of RB and IT with a coverage percentage of 5% and a cost of $ 57,710 reduced the total volume by 51.54% and the peak discharge by 48.8% compared to the results of the current system. According to AHP-PROMETHEE, IT-RB-5 is the best project proposed among the 70 projects studied. This study showed that the AHP-PROMETHEE method could be used as a practical method to choose from several LID schemes for flood control.

One method of estimating the evaporation rate (ER) is to use a variety of evaporation pans, such as the Class A standard evaporation pan (CASEP) and the Colorado Sanken standard evaporation pan (CSSEP). In this study, the rate of evaporation of CASEP and CSSEP have been investigated and compared with each other. This study was conducted in Semnan, Iran. CSSEP was used as a test pan, which was performed in an open space around the Faculty of Civil Engineering, Semnan University. Evaporation was recorded daily for 123 days. The evaporation of the CASEP pan was obtained from the synoptic station of Semnan, which is located at a distance of 2.39 km from the test site. Meteorological data were also obtained from the synoptic station of Semnan and compared with experimental evaporation data. The results of this study showed that the daily ER from CASEP and CSSEP in the tested time periods were not significantly different. Based on the Klomogorov-Sminrov method, the best statistical distributions for CASEP and CSSEP were calculated as Error and Gamma, respectively. The coefficient of determination (R 2 ) between the two pans was estimated to be about 93%. Also, by examining the ER with other meteorological data, it was observed that the ER has the highest correlation with the average daily air temperature.