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هدف از پژوهش پیش رو، پهنه بندی احتمال وقوع سیل در حوزه آبخیز سالیان تپه، واقع در استان گلستان میباشد. بدین منظور، از دو مدل معروف و شناخته شده‌ی داده‌کاوی یعنی مدل جنگل تصادفی(RF) و مدل ماشین بردار پشتیبان (SVM) به عنوان بنچ‌مارک و به لحاظ الگوریتم محاسباتی توانمند در زمینه ارزیابی فرایند وقوع سیلاب استفاده شد. شواهد سیلاب با استفاده از بازدیدهای میدانی،گزارش‌ها و اطلاعات سازمانی موجود ثبت و در سامانه اطلاعات جغرافیاییGIS)) در قالب نقشه تهیه شد. همچنین، با توجه به مرور منابع گسترده، سیزده عامل زمینه‌ساز شامل فاصله از آبراهه، واحدهای سنگ‌شناسی، درصد شیب، بافت خاک، جهت شیب، کاربری اراضی، انحنای طولی و عرضی دامنه، شاخص رطوبت، شاخص توان فرسایشی آبراهه و طبقات ارتفاعی به‌عنوان عوامل موثر بر وقوع سیل در منطقه مورد مطالعه انتخاب و لایه‌های مذکور در سامانه اطلاعات جغرافیایی تهیه شدند. در این مطالعه بعد از آماده‌سازی لایه‌ها، برای آنالیز این داده‌ها و بررسی هم‌خطی آنها از نرم‌افزار SPSS استفاده شد. به‌منظور ارزیابی نتایج مدلها، از مقدار مساحت زیر منحنی تشخیص عملکرد نسبی ROC)) استفاده شد. سه سری متفاوت از نقاط وقوع خطر سیل (S1, S2, S3 ( شامل 70 درصد برای آموزش مدل و 30 درصد برای اعتبار سنجی به صورت تصادفی آماده شد تا دقت و صداقت مدل مورد ارزیابی قرار بگیرد. نتایج نشان داد نقشه میانگین حاصل از مدل جنگل تصادفی در مرحله اعتبارسنجی با مساحت زیر منحنی 96 درصد و صداقت 001/0 کارایی بهتری نسبت به مدل ماشین بردار پشتیبان در پهنه‌بندی سیلاب در حوضه مورد مطالعه دارد .

هدف از این پژوهش، توسعه یک مدل کوپل شده شبیه ساز- بهینه ساز برای برنامه ریزی و مدیریت صحیح تخصیص به منابع و مصارف بالادست تالاب شادگان است. طوری که علاوه بر حداکثر نمودن درصد تامین نیازهای حوضه در طول دوره بهره برداری، میزان شوری جریان ورودی به تالاب شادگان نیز کاهش یابد. با توجه به اهمیت این تالاب به عنوان زیستگاه فصلی پرندگان و همچنین یکی از جاذبه های مهم توریستی و لزوم حفاظت از اکوسیستم آن، توسعه یک مدل بهینه سازی کمی-کیفی برای بهره برداری بهینه از منابع آب موجود هدف این تحقیق می باشد. ابتدا بر اساس وضع موجود، مدل تهیه شده تحت عنوان سناریوی مرجع برای یک دوره30 ساله آتی(2021-2050) توسعه داده شد. برای دستیابی به بهترین عملکرد سیستم بر اساس معیارهای کمی و کیفی، بهینه سازی سیستم به کمک الگوریتمII-NSGA انجام شد. نتایج نشان داد مدل بهینه‌ساز، عملکرد خوبی در تامین نیازهای مختلف و همچنین کاهش شوری جریان ورودی به تالاب شادگان نسبت به سناریوی دارد. طوری که در سناریوی بهینه علاوه بر تامین مناسب نیازها با اطمینان پذیری بیش از92 درصد در کل سیستم، مقدار شوری رودخانه در محل ورودی به تالاب شادگان بخصوص در ماههای کم آب در حدود 50 درصد کاسته شود. مدل کوپل شده پیشنهادی در این تحقیق قابل کاربرد برای سایر مناطق مطالعاتی با رویکرد بهره برداری کمی- کیفی می باشد و قادر است نقاط بحرانی رودخانه از نظر کمی و کیفی را شناسایی و راه حل های بهینه برای بهبود وضعیت رودخانه و همچنین اکوسیستم پایین دست ارایه دهد.

در مطالعۀ حاضر به‌وسیله یک مدل ترکیبی نحوۀ انتقال رسوبات درون مجاری فاضلاب‌رو مدل‌سازی می‌شود. به‌بیان‌دیگر، توسّط سیستم استنباط فازی عصبی تطبیقی و الگوریتم ژنتیک یک الگوریتم ترکیبی(ANFIS-GA) برای تخمین عدد فرود جریان سه­فازی (هوا، آب و رسوب) ارائه داده می‌شود. در این مطالعه، از الگوریتم ژنتیک برای افزایش کارایی سیستم استنباط فازی عصبی تطبیقی با تنظیم توابع عضویّت و سپس به حداقل رساندن مقادیر خطا استفاده گردیده است. الگوریتم ژنتیک (GA) یک روش فرا ابتکاری با الهام از فرایند انتخاب طبیعی است که متعلق به طبقه‌بندی گسترده‌تری از الگوریتم‌های تکاملی (EA) است. سپس با استفاده از مقادیر ورودی، 127 مدل ترکیبی ANFIS-GA معرفی گردید. همچنین مقادیر درصد میانگین مطلق خطا و خطای جذر میانگین مربعات برای مدل برتر به ترتیب مساوی 529/5 و 315/0 به دست آمد.

The aim of this research is to predict fluctuations in the equivalent thickness of groundwater using GRACE satellite data and modeling it using artificial intelligence hybrid models. The study area of this research is the basin area of Lake Urmia located in the northwest of Iran. For this purpose, 180 GRACE satellite data between April 2002 and March 2017 were used. The output of GRACE satellites includes 6 pixels located on the selected watershed, of which 2 points that overlapped the most with the watershed area were selected for modeling with artificial intelligence tools. The GA-ANN, ICA-ANN and PSO-ANN hybrid models were used for this purpose. The results showed that the output of the ICA-ANN model had the best fit with the observation data with a correlation coefficient equal to 0.915 and 0.942 in the two selected pixels 2 and 5 in the test phase, and the results of this model had the best and closest distribution of points. Considering the importance of knowing the changes in the equivalent thickness of groundwater as one of the most important parameters of the water budget, the artificial intelligence models used in this research can be recommended, especially for areas without basic statistics or in situations where it is not possible to use mathematical models. Without the need for complex relationships and equations to investigate the effect of surface and groundwater interaction and only based on satellite data, the equivalent thickness of groundwater can be predicted in the studied plain in dry and wet periods with great accuracy. [ABSTRACT FROM AUTHOR]

The Dez-River's surface water resources system between the Dez regulatory dam and Bandare-Ghir is the focus of the current study to create a qualitative-quantitative-model that can be used to determine the best operating strategies. for replicate the existing operational state, a dynamic link between quantitative and qualitative models is built under the "referencescenario" such that hydraulic linkages are generated between all of the system's components in the coupled system. The monthly environmental demands of the river are one of the choice factors in the optimization-scenario. The goals are to maximize the percentage of needs met and minimize quality standard violations. The implementation of the optimization scenario increased the reliability of providing all the needs of the plain with any priority. This problem illustrates how the reservoir should operate in an ideal state. In many places along the river, particularly the agricultural water withdrawal sites, the minimum violation of water quality standards has happened, according to a comparison of the pollution and quality parameters in the optimization scenario and the reference scenario. The amount of pollution and quality parameters has also improved. The findings show that it is possible to plan more effectively for the appropriate use of currently available water resources by taking into account all stakeholders and utilizing the qualitative-quantitative dynamic connection method of water resources to develop a coupled model using the MOPSO-algorithm. This will ensure that, in addition to meeting needs, the quality and pollution of the river remain close to the standard limits during the operation period. [ABSTRACT FROM AUTHOR]

The Dez River, as the third largest river in the country, plays an essential role in the economic, social and environmental life of North Khuzestan. Also, it is very important because it supplies water to more than 125 thousand hectares from the Dez plain and the lands downstream of this plain to the place of Bituminous Dam and provides a significant part of the environmental needs of the Karun River. The purpose of this research is to investigate the trend of quality and pollution along the Dez River and evaluate the effects of urban, agricultural and industrial wastewater discharges on the trend of pollution and quality of the Dez River in 131 intervals based on the QUAL2Kw model. In the calibration and validation stages of the model, it was determined that wide parameters such as flow geometry, river hydraulic conditions, water flow rate, aeration coefficients, pollutant entry points, nitrification rate, oxygen demand, etc. in the river are involved in qualitative modeling results. The results showed that QUAL2Kw water quality model is highly accurate in simulating quality parameters and pollution in Dez River. The trend of changes in quality parameters and pollution in the Dez River showed that this river is better in terms of BOD pollution due to the discharge of urban and industrial sewage, in terms of EC pollution due to the discharge of agricultural land drains (mainly sugarcane plan) and in terms of N-NH4 pollution It is in a critical condition due to urban and industrial sewage and drainage of agricultural lands. Therefore, it is suggested that all the wastewater of Dezful city, fish farming effluent, Haft Tepe complex and Pars paper factory should be treated and then discharged, but regarding agricultural drains, corrective measures such as improvement in the type of irrigation systems, fertilizers, the cultivation pattern of the area, etc., should be done at the source. [ABSTRACT FROM AUTHOR]

Background and Objectives: scouring is a phenomenon that occurs due to the passage of water flow at the boundaries of contact with other objects in hydraulic structures. The basis of this phenomenon is the creation of a vacuum at the contact boundaries of two environments, due to the water velocity gradient. In general, scouring occurs in the vicinity of various structures such as bridge foundations, submerged spillways, downstream of spillways. With the increase in the dimensions of the scour hole, there is a possibility of overturning and destroying these structures. So that every year significant costs are spent on the repair and reconstruction of various structures, including bridge foundations that have been destroyed due to erosion. Estimation and prediction of scouring around the piers play a significant role to design these structures since with increasing dimensions of scour hole, stability of the pier is threatened; as a result, the structure may be destructed. In this study, scour hole in the vicinity of twin and three piers was estimated by using fuzzy c-means clustering of ANFIS (ANFIS-FCM) network technique. Methodology: Firstly, the parameters affecting scour hole around twin and three piers including Froude number (Fr), the ratio of the pier diameter to the flow depth (D/h), and the ratio of the distance between the piers to the flow depth (d/h) were detected. Subsequently, seven ANFIS-FCM models were defined by means of these dimensional input parameters. It should be stated that 70% of the experimental data were utilized to training the models and 30% of the rest were applied to testing. Then, the superior ANFIS-FCM model and the most important input parameter were introduced by implementing a sensitivity analysis. The premium model as a function of all input parameters simulated the scour values with a reasonable accuracy. Findings: For instance, the correlation coefficient (R), the scatter index (SI), and the Nash-Sutcliff efficiency coefficient (NSC) are respectively computed to be 0.988, 0.106, and 0.976. Furthermore, the Froude number was considered as the most important input parameter. Finally, a computer code was introduced to simulate the scour hole around the twin and three piers. Conclusion: In this study, a neuro-fuzzy technique of the new artificial intelligence method called ANFIS Network Fuzzy C-Mean Classification (ANFIS-FCM) was used to simulate the scour depth in the vicinity of paired and triple bridge foundations. To validate the results of the simulations, 70% of the observed values were used to train the artificial intelligence model and the remaining 30% were used to test it. Then, using the input parameters, seven ANFIS-FCM models were defined, and by analyzing the results Modeling, the best model and the most effective input parameters were introduced. The superior model (ANFIS-FCM 1) simulated scour values with acceptable accuracy. This model estimated scour values according to all input parameters and Fr and D/h parameters were identified as the most effective input parameters. For example, the values of RMSE, MAE and VAF were calculated as 0.025, 0.019 and 97.507 respectively for the test conditions of ANFIS-FCM 1 model (superior model). It should be mentioned that in order to estimate scour depth in the vicinity of double and triple bridge foundations, a computer code was proposed for use in practical work for engineers. One of scour hole, stability of the pier is threatened; as a result, the structure may be destructed. In this study, scour hole in the vicinity of twin and three piers was estimated by using fuzzy c-means clustering of ANFIS (ANFIS-FCM) network technique. Firstly, the parameters affecting scour hole around twin and three piers including Froude number (Fr), the ratio of the pier diameter to the flow depth (D/h), and the ratio of the distance between the piers to the flow depth (d/h) were detected. Subsequently, seven ANFIS-FCM models were defined by means of these dimensional input parameters. It should be stated that 70% of the experimental data were utilized to training the models and 30% of the rest were applied to testing. Then, the superior ANFISFCM model and the most important input parameter were introduced by implementing a sensitivity analysis. The premium model as a function of all input parameters simulated the scour values with a reasonable accuracy. For instance, the correlation coefficient (R), the scatter index (SI), and the Nash-Sutcliff efficiency coefficient (NSC) are respectively computed to be 0.988, 0.106, and 0.976. Furthermore, the Froude number was considered as the most important input parameter. Finally, a computer code was introduced to simulate the scour hole around the twin and three piers. [ABSTRACT FROM AUTHOR]

To investigate the release of nitrogen. Phosphorus and potassium elements from biochar, an experiment was conducted in a completely randomized design in three replications. Biochars were used by 2% in weight (W/W). The experimental treatments included control soil (CS), Soil+ Grape waste biochar (GSB), Soil + wheat straw and stubble (WSB) + brown walnut shell biochar (NSB) and Soil. The samples were incubated for about four months and sampling was done at days of 0, 7, 14, 28, 42, 56, 88 and 120. The results showed that the biochar addition significantly increased the concentrations of nitrogen, phosphorus and potassium in all biochar-amended soils compared to the control soil. Nitrogen concentration ranged from 766.6 mg kg-1 in control (average of sampling times) to 1272.2, 1366.7, and 1488.9 mg kg-1 in GSB, WSB and NSB treatments, respectively. Phosphorus concentration enhanced from 44.5 mg kg-1 in control (time-average of sampling) to 79.1, 67.1, and 70.8 mg kg-1 in GSB, WSB and NSB, respectively, and the concentration of potassium in the control increased from 103 mg kg-1 to 656.5, 293.8, and 125.6 mg kg-1 in the GSB, WSB, and NSB treatments, respectively. The WSB and NSB treatments released an average of 52% and 50.5% of their nitrogen content, respectively during 120 days, increased while GSB released only 28.6% of its total nitrogen on average. The order of nitrogen release rates in biochar were as follows: WSB≥ NSB> GSB. Regarding the release of phosphorus, NSB was considered to be the most efficient biochar, which was able to release 5% of its phosphorus during 4 months, while, GSB was the lowest effective biochar releasing only 0.5% of its phosphorus during the corresponding time. The order of phosphorus release from biochar treatments was as follows: NSB > WSB > GSB. The amount of available potassium in GSB was 2.3 to 6.7 times higher than the other treatments. The order of potassium release from biochar treatments was as follows: GSB > WSB > NSB. The results of infrared spectroscopy showed that the dissolution of amides containing nitrogen is the main mechanism of N release. The decrease in pH caused by nitrification, probably facilitated Ca-P dissolution from biochar and led to a higher phosphorus release. [ABSTRACT FROM AUTHOR]

Background and Objectives Artificial intelligence models as powerful methods in modeling complex nonlinear problems have a significant ability, and this has been proven in numerous articles. Artificial intelligence has been used in various issues, including engineering, medicine, etc. The success of these methods compared to analytical and numerical methods, their easiness, speed, and accuracy. Today, one of the challenges of human life is the issues related to water resources management. This study has investigated the performance of artificial intelligence and regression models in water resources problems. Various researches have been done in the case of modeling and parametric analysis of water resources. However, this study used artificial intelligence (Learning Machine) models to simulate water's qualitative and quantitative parameters. The models used in this study are SelfAdapting Extreme Learning Machine (SAELM), Least Square Support Vector Machine (LSSVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR). Due to the growing global population, one of the most critical challenges is access to safe drinking water. Due to its location in the semi-arid region and low rainfall in our country, Iran, this danger is felt more than ever. One of the serious issues is the salinity leakage into groundwater resources. In this study, an attempt has been made to simulate the leakage of salinity dynamic flow into the freshwater resources of the coastal aquifer, using artificial intelligence and statistical models. Methodology The study area in this work is Mighan Wetland in Markazi province. Annual rainfall occurs in small amounts in this area. According to the statistical results, the maximum and minimum rainfall values range from 461 mm in the northeast to 208 mm in the center of the Arak plain. In this study, qualitative and quantitative parameters: water salinity, total dissolved solids (TDS), chloride ion (Cl), sampling time (t), electrical conductivity (EC), salinity, and groundwater level (GWL) were simulated. This work used ANFIS, LSSVM, SAELM, and MLR models for simulation. In this study, data from 173 months of sampling were used. 80% of the sample size was used for training and 20% for testing models. After simulation and obtaining the results, the Wilson Score method performed the uncertainty analysis without continuity correction. In this method, the prediction error (ei), the mean prediction error (Mean), and the standard deviation of the error are (Se). If the mean error value in predicting the target variable is positive, the model's performance is Over Estimated (OS). Also, if the average value of the model error is negative, the model's performance is Under Estimated (US). Moreover, the Uncertainty Analysis results with a significance of 5% were obtained. Also, five approaches measured the performance of the models. The proposed approaches were: 1) Evaluation of prediction by accuracy chart, 2) Performance evaluation by mathematical indices, 3) Performance evaluation by Uncertainty Analysis by Wilson Score method without continuity correction, 4) Accuracy evaluation by error distribution charts, and 5) Performance evaluation by discrepancy rate (DR) charts. Finally, all the results are given at the end of each section, respectively. Findings The simulation was performed using artificial intelligence and regression models. The simulation results showed higher accuracy of artificial intelligence models. The results showed that different models were successful in predicting water parameters. Approach 1- According to the prediction accuracy charts, 16 charts were drawn, and the most accurate models are depicted in Figures 4 to 7. After modeling, the results showed that the most accurate models in simulating groundwater parameters were the SAELM model in GWL simulation. According to the results, the SAELM model in GWL and EC simulation, LSSVM in TDS simulation, and MLR in Salinity simulation were superior models.Approach 2- According to the performance measurement indices, the results showed that the SAELM model was the best in simulating parameters (EC) and (GWL). Then the LSSVM model was also the most accurate in modeling the (TDS) parameter. MLR was the best model in (Salinity) parameter simulation. Approach 3- Uncertainty analysis was performed based on the Wilson score method. The performance of the models in the simulation showed that the SAELM model's performance was underestimated, and other superior models in the simulation had overestimated performance. Approach 4- The best accuracy was assigned to SAELM and MLR models based on the error distribution diagrams. Approach 5- Based on the DR diagrams, SAELM and MLR models were the most accurate models in the simulation. Conclusion In all five performance approaches, the top models were introduced as follows:According to the performance measurement indices, each model successfully estimated one of the quantitative and qualitative parameters. Finally, study results showed that the SAELM was the best model for simulating EC and GWL parameters. It had the least computational error. Then LSSVM was also the most accurate model in estimating and modeling the TDS parameter. Also, MLR was the best model in Salinity parameter simulation. The uncertainty analysis results showed that the performance of the SAELM was underestimated, and other superior models were overestimated. The best accuracy was assigned to SAELM and MLR models based on the DR diagrams. SAELM and MLR models were the most accurate models based on the error distribution charts.Finally, it should be noted that based on the simulation results, the following predictions are possible: - The graphs gradually showed more insignificant changes in estimating GWL changes by the SAELM model. However, these changes will continue their downward procedure. - Based on the prediction diagrams, EC changes are almost like a time series and will continue similar to past behavior in the future changes. These changes will also occur in less time. - Based on the estimation of the TDS changes, the LSSVM charts behave like time series and seem the future behavior is similar to past time series. The changes occur in the same range. - Based on the estimation of Salinity changes, the MLR showed the changes of this parameter within a higher range. This tolerance occurred with higher values than before. [ABSTRACT FROM AUTHOR]

In the present study, the potential map of groundwater springs was prepared using the famous and efficient Maxent model as a benchmark in Namroud catchment, Tehran province. In this study, the effect of three different scenarios on the number of presence points (100,500,800 springs), raster resolution (100,50,30) and sample size (10/90, 20/80, 30/70, 50/50) on the predicted map was evaluated. First, 18 factors affecting the emergence of springs including lithological formations, the distance from the fault, fault density, elevation classes, slope percentage, slope direction, slope length factor, curvature maps, the distance from the waterway, waterway density, the distance from the road, topographic moisture index, relative slope position, flow power index, soil texture, surface roughness index and land use cover were selected and their maps were prepared in the ArcGIS10.5 and SAGA systems. After the correlation test and classification of the effective layers, the percentage and frequency of groundwater potential in each class were obtained using the frequency ratio method, which calculates the exact weight of each class. The relative operating characteristic curve (ROC) was used to evaluate the performance of these models. The results showed that the combination of raster resolution scenarios: 30-number of points: 100-sample size: 90/20 with the highest AUC (0.953 in training phase and 0.927 in validation phase) has goodness-of-fit and prediction accuracy compared to the combinations of other scenarios. In addition, about 9.14% of the study area had high and very high potential for groundwater. Based on the combination of the best scenario, the factors of distance from the waterway, relative slope position, drainage density and topographic moisture index with values of 17.3, 14.2, 13.8 and 10.2%, respectively, are the main factors of spatial control in the study area influencing the spring occurrence. [ABSTRACT FROM AUTHOR]

The use of coupled simulation-optimization models to extract the optimal role curve of dams is one of the effective strategies for optimal management of reservoirs. In certain optimization techniques, historical data of the inflow to the reservoir is usually used to extract the optimal role curve of the dam. It is possible that in the coming years, with the change of the inflow to dams, the parameters based on which the optimal role curve was extracted may no longer work and the results may be unexpected. The objective of this research is to provide a solution for extracting the optimal role curve in real time so that by changing the inflow to the dam in the future without re-executing the optimization algorithm and using artificial intelligence techniques in the shortest time, the optimal role curve compatible with the new conditions can be obtained. In this research, the integration of the NSGA-II multi-objective algorithm and the WEAP simulation model is used to derive optimal policies based on historical data. Then, using the support vector machine method and the results obtained from the output of the optimization algorithm, a new structure is developed so that the optimal role curve can be obtained in real time and based on new inputs. The results indicate that the average error of the optimal role curve extracted from support vector machines is less than 2.5% compared to the role curve obtained from the NSGA-II algorithm in the calibration and validation stages. Therefore, the developed support vector machine model has the ability to quickly provide optimal operation policies in such a way that provides the possibility of optimal management of the system in real time, according to the new data of the inflow to the dam. [ABSTRACT FROM AUTHOR]

The objective of the current study was to zone flood probability in the Marzdaran watershed. Since the allocated budget for management work is limited and it is not possible to carry out operations in the whole area, having a map that has prioritized different areas in terms of the probability of flood occurrence will be very useful and necessary. A well-known data mining model namely MaxEnt (ME) is applied due to its robust computational algorithm. Flood inventories are gathered through several field surveys using local information and available organizational resources, and the corresponding map is created in the geographic information system. The twelve predisposing variables are selected and the corresponding maps are generated in the geographic information system by reviewing several studies. The area under the curve (ROC) is used to evaluate the modeling results. Then, the most prone areas of flood occurrence which are prioritized for management operations are identified based on the prepared map. Based on the results, about 100 km2 of the study area is identified as the most prone area for management operations. The results showed that the accuracy of the maximum entropy model is 98% in the training phase and 95% in the validation phase. The distance from the river, drainage density, and topographic wetness index are identified as the most effective factors in the occurrence of floods, respectively. [ABSTRACT FROM AUTHOR]

In this research, the scour hole depth at the downstream of cross-vane structures with different shapes (i.e., J, I, U, and W) was simulated utilizing a modern artificial intelligence method entitled "Outlier Robust Extreme Learning Machine (ORELM)". The observational data were divided into two groups: training (70%) and test (30%). Then, using the input parameters including the ratio of the structure length to the channel width (b/B), the densimetric Froude number (Fd), the ratio of the difference between the downstream and upstream depths to the structure height (Δy/hst), and the structure shape factor (φ), eleven different ORELM models were developed for estimating the scour depth. Subsequently, the superior model and also the most effective input parameters were identified through the conduction of uncertainty analysis. The superior model simulated the scour values by the dimensionless parameters b/B, Fd, Δy/hst. For this model, the values of the correlation coefficient (R), the variance accounted for (VAF), and the Nash-Sutcliffe efficiency (NSC) for the superior model in the test mode were obtained 0.956, 91.378, and 0.908, respectively. Also, the dimensionless parameters b/B and Δy/hst were detected as the most effective input parameters. Furthermore, the results of the superior model were compared with the extreme learning machine model and it was concluded that the ORELM model was more accurate. Moreover, an uncertainty analysis exhibited that the ORELM model had an overestimated performance. Besides, a partial derivative sensitivity analysis (PDSA) model was performed for the superior model. [ABSTRACT FROM AUTHOR]

Optimization of artificial intelligence (AI) models is a significant issue because it enhances the performance and flexibility of the numerical models. In this study, scour depth around bridge abutments with different shapes was estimated by means of ANFIS and ANFIS-Genetic Algorithm. In other words, the membership functions of the ANFIS model were optimized using the genetic algorithm, finding that the performance of ANFIS model was increased. Firstly, effective input parameters on the scour depth around bridge abutments were defined. Then, by using the input parameters, eleven ANFIS and ANFIS-GA models were produced. Next, the superior ANFIS and ANFIS-GA models were introduced by analyzing the numerical results. For example, the correlation coefficient and scatter index for ANFIS model were calculated to be 0.979 and 0.070; for ANFIS-GA, these were 0.986 and 0.056, respectively. In addition, the average discrepancy ratio (DRave) for ANFIS and ANFIS-GA models was 0.984 and 0.988, respectively. Also, it was shown that the ANFIS-GA models had more accuracy, as compared to the ANFIS models. Moreover, a sensitivity analysis showed that Froude number (Fr) and ratio of flow depth to radius of scour hole (h/L) were the most influential input parameters for simulating the scour depth around bridge abutments. [ABSTRACT FROM AUTHOR]

Due to drought and climate change, estimation and prediction of rainfall is quite important in various areas all over the world. In this study, a novel artificial intelligence (AI) technique (WGEP) was developed to model long-term rainfall (67 years period) in Anzali city for the first time. This model was combined using Wavelet Transform (WT) and Gene Expression Programming (GEP) model. Firstly, the most optimized member of wavelet families was chosen. Then, by analyzing the numerical models, the most accurate linking function and fitness function were selected for the GEP model. Next, using the autocorrelation function (ACF), the partial autocorrelation function (PACF) and different lags, 15 WGEP models were introduced. The GEP models were trained, tested and validated in 37, 20- and 10-years periods, respectively. Also, using sensitivity analysis, the superior model and the most effective lags for estimating long-term rainfall were identified. The superior model estimated the target function with high accuracy. For instance, correlation coefficient and scatter index for this model were 0.946 and 0.310, respectively. Additionally, lags 1, 2, 4 and 12 were proposed as the most effective lags for simulating rainfall using hybrid model. Furthermore, results of the superior hybrid model were compared with GEP model that the hybrid model had more accuracy. [ABSTRACT FROM AUTHOR]

In this study, the discharge coefficient of sharp-crested weirs located on circular channels was modeled using the ANFIS and ANFIS-Firefly (ANFIS-FA) algorithm. Also, the Monte Carlo simulations (MCs) were used to enhance the compatibilities of the soft computing models. However, the k-fold cross validation method (k=5) was used to validate the numerical models. According to the input parameters, four models of ANFIS and ANFIS-FA were introduced. Analyzing the numerical results showed that the superior model simulated the discharge coefficient as a function of the Froude number (Fr) and the ratio of flow depth over weir crest to the weir crest height (h/P). The values of the mean absolute relative error (MARE), root mean square error (RMSE) and correlation coefficient (R) for the superior model were calculated 0.001, 0.002 and 0.999, respectively. However, the maximum error value for this study was less than 2%. [ABSTRACT FROM AUTHOR]

In this study, the groundwater level (GWL) of the Sarab Qanbar region located in the south of Kermanshah, Iran, was estimated using the Wavelet- Self- Adaptive Extreme Learning Machine (WA- SAELM) model. An artificial intelligence method called "Self- Adaptive Extreme Learning Machine" and the "Wavelet transform" method were implemented for developing the numerical model. First, by using the autocorrelation function (ACF), the partial autocorrelation function (PACF) and the effective lags in estimating GWL, eight distinctive SAELM and WA- SAELM models were developed. Later, the values of the observational well were normalized for estimating GWL. Next, the most optimized mother wavelet was chosen for the modeling. By evaluating the results of SAELM and WA- SAELM, it was concluded that the WA- SAELM models could estimate the values of the objective function with higher accuracy. Then, the superior model was introduced, showing that it could be very accurate in forecasting the GWL. In the test mode, for example, the values of R (correlation coefficient), Main absolute error (MAE) and the NSC- Sutcliffe efficiency coefficient (NSC) for the superior model were calculated to be 0.995, 0.988 and 0.990, respectively. Furthermore, an uncertainty analysis was conducted for the numerical models, proving that the superior model had an underestimated performance. [ABSTRACT FROM AUTHOR]

In this study, the discharge coefficient of the circular side orifices was predicted using a new hybrid method. Combinations made in this study were divided into two sections: 1) the combination of two algorithms including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) and providing the PSOGA algorithm 2) using the PSOGA algorithm in order to optimize the Adaptive Neuro Fuzzy Inference Systems (ANFIS) network and providing the ANFIS-PSOGA method. Next, by identifying the parameters affecting on the discharge coefficient of the circular side orifices, 11 different combinations were provided. Then, the sensitivity analysis conducted by ANFIS showed that the Froude number and the ratio of the flow depth to the orifice diameter (Ym/D) were identified as the most effective parameters in modeling the discharge coefficient. Also, the best combination including the Froude number (Fr), the ratio of the main channel width to the side orifice diameter (B/D), the ratio of the orifice crest height to its diameter (W/D) and the ratio of the flow depth to the orifice diameter (Ym/D) for estimating the discharge coefficient was introduced. For this model, the values of Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and correlation coefficient (R) were obtained 0.021, 0.020 and 0.871, respectively. Additionally, the performance of the ANFIS-PSOGA method was compared with the ANFIS-PSO and ANFIS methods. The results showed that the ANFISPSOGA method for predicting the discharge coefficient was the superior model [ABSTRACT FROM AUTHOR]

مقدمه: پژوهش‌های انجام گرفته در حوزه­ی اعتیاد بر این نکته تاکید دارند که علاوه بر مداخلات پزشکی، درمان اعتیاد، بدون توجه به عوامل روان­شناختی، ممکن نخواهد بود لذا پژوهش حاضر به بررسی مقایسه‌ای ناگویی هیجانی و ابعاد خشونت در دو گروه از معتادین به مواد مخدر و محرک با همتایان سالم پرداخته است. روش­کار: در این پژوهش علی-مقایسه‌ای در سال 1395، 120 نفر مورد بررسی قرار گرفتند. تعداد 80 نفر وابسته به سوء­مصرف مواد در مرکز دلشدگان شهر تهران (40 نفر معتاد به مواد مخدر و 40 نفر معتاد به مواد محرک) و 40 نفر غیر وابسته به لحاظ ویژگی‌های جمعیت­شناختی در گروه‌های همتا، قرار گرفتند. داده‌ها با مقیاس ناگویی هیجانی تورنتو و سیاهه بروز خشم حالت-صفت اشپیل­برگر گردآوری و با روش تحلیل واریانس چندمتغیره (مانووا) تحلیل گردیدند. یافته­ها: بین سه گروه افراد سالم، معتاد به محرک‌ و مواد مخدر، در ناگویی هیجانی (01/0>P، 17/8=F) و خشونت (01/0>P، 20/7=F) تفاوت‌های معنی­داری مشاهده شد که در این میان افراد معتاد به مخدر، سطوح بالایی از ناگویی هیجانی و خشونت را از خود گزارش می‌کنند. نتیجه­گیری: بین سه گروه افراد سالم، معتاد به محرک­ و مواد مخدر از نظر میزان ناگویی هیجانی و خشونت، تفاوت، وجود دارد. به نظر می­رسد افراد وابسته به مواد مخدر نسبت به افراد سالم و افراد وابسته به محرک، از میزان خشونت و ناگویی هیجان بیشتری برخوردارند.