Soil Erosion Control in Drylands

This book focuses on drylands such as arid, semi-arid and dry sub-humid areas where they form the main part of ecosystems, e.g., in Iran, but also around the world. Mismanagement and improper exploitation of these areas lead to more degradation day by day. Besides an introduction to the role and importance of vegetation cover in conserving soil against wind and water erosion, this book gives a scope of appropriate techniques and methods for vegetation establishment and maintenance, indicators for suitable plants selection for soil conservation, and soil erosion prevention and combat. It provides methods of soil erosion prevention and combating through the application of plants, using bioengineering systems for soil erosion control and the role of agroforestry in soil erosion prevention. This book can be helpful to those with an interest in countries with similar climates to Iran. In particular, this includes Dubai, Kuwait, Saudi Arabia, Afghanistan, and Pakistan

Assessment of perceived social support among selected hospital personnel in Isfahan

كي از مهمترين شرايط براي عملكرد مؤثر سازمانها، فراهم بودن عامل انساني است. در اين ميان، حمايت اجتماعي يـک عامـل روانشناختي مهم در محيط کار ميباشد که بر عملكرد نيروي انساني تاثيرگذار است. لذا اين مطالعه با هدف تعيين حمايت اجتمـاعي درک شده در بعد پشتيباني عاطفي در ميان کارکنان بيمارستان انجام شد. در اين مطالعهي مقطعي، ۱۲۰ نفر از کارکنان يک بيمارسـتان منتخب در شهر اصفهان به روش نمونهگيري در دسترس جهت بررسي انتخاب شدند. گردآوري اطلاعات با استفاده از پرسشنامههاي مشخصات فردي و حمايت اجتماعي كه محقق ساخته بود، انجام شد. روايي ابزار با اسـتفاده از روايـي محتـوا و پايـايي آن بـا روش بازآزمايي اخذ شدند و در نهايت دادهها با استفاده از آمار توصيفي و استنباطي (آزمون مجذورکاي) مورد تجزيه و تحليل قرار گرفتند. يافتهها نشان داد، حمايت اجتماعي درک شده در بعد پشتيباني عاطفي از سوي همکاران با ميـانگين نمـرهي ۹/۰ ± ۳۴/۳ بـيشتـر از حمايت اجتماعي درک شده در همين بعد از سوي مديران با ميانگين نمره ۸۸/۰ ±۵۸/۲ بوده است. همچنين، بين حمايت اجتماعي در بعد پشتيباني عاطفي با مشخصات فردي مثل سن و سابقهي کار مطابق آزمون square-Chi دو رابطهي معنيدار آماري وجـود داشـت (05/0<P .(با توجه به آنكه حمايت اجتماعي از کارکنان بيمارستان براي عملكرد مؤثر سازماني الزامي اسـت، مـديران بيمارسـتانهـا ميتوانند از طريق تحکيم روابط خود با کارکنان بر افزايش کارايي آنها مؤثر باشند

Application of Group Method of Data Handling and New Optimization Algorithms for Predicting Sediment Transport Rate under Vegetation Cover

Planting vegetation is one of the practical solutions for reducing sediment transfer rates. Increasing vegetation cover decreases environmental pollution and sediment transport rate (STR). Since sediments and vegetation interact complexly, predicting sediment transport rates is challenging. This study aims to predict sediment transport rate under vegetation cover using new and optimized versions of the group method of data handling (GMDH). Additionally, this study introduces a new ensemble model for predicting sediment transport rates. Model inputs include wave height, wave velocity, density cover, wave force, D50, the height of vegetation cover, and cover stem diameter. A standalone GMDH model and optimized GMDH models, including GMDH honey badger algorithm (HBA) GMDH rat swarm algorithm (RSOA)vGMDH sine cosine algorithm (SCA), and GMDH particle swarm optimization (GMDH-PSO), were used to predict sediment transport rates. As the next step, the outputs of standalone and optimized GMDH were used to construct an ensemble model. The MAE of the ensemble model was 0.145 m3/s, while the MAEs of GMDH-HBA, GMDH-RSOA, GMDH-SCA, GMDH-PSOA, and GMDH in the testing level were 0.176 m3/s, 0.312 m3/s, 0.367 m3/s, 0.498 m3/s, and 0.612 m3/s, respectively. The Nash Sutcliffe coefficient (NSE) of ensemble model, GMDH-HBA, GMDH-RSOA, GMDH-SCA, GMDH-PSOA, and GHMDH were 0.95 0.93, 0.89, 0.86, 0.82, and 0.76, respectively. Additionally, this study demonstrated that vegetation cover decreased sediment transport rate by 90 percent. The results indicated that the ensemble and GMDH-HBA models could accurately predict sediment transport rates. Based on the results of this study, sediment transport rate can be monitored using the IMM and GMDH-HBA. These results are useful for managing and planning water resources in large basins.Comment: 65 pages, 10 figures, 5 table

Machine Learning Methods for Better Water Quality Prediction

In any aquatic system analysis, the modelling water quality parameters are of considerable significance. The traditional modelling methodologies are dependent on datasets that involve large amount of unknown or unspecified input data and generally consist of time-consuming processes. The implementation of artificial intelligence (AI) leads to a flexible mathematical structure that has the capability to identify non-linear and complex relationships between input and output data. There has been a major degradation of the Johor River Basin because of several developmental and human activities. Therefore, setting up of a water quality prediction model for better water resource management is of critical importance and will serve as a powerful tool. The different modelling approaches that have been implemented include: Adaptive Neuro-Fuzzy Inference System (ANFIS), Radial Basis Function Neural Networks (RBF-ANN), and Multi-Layer Perceptron Neural Networks (MLP-ANN). However, data obtained from monitoring stations and experiments are possibly polluted by noise signals as a result of random and systematic errors. Due to the presence of noise in the data, it is relatively difficult to make an accurate prediction. Hence, a Neuro-Fuzzy Inference System (WDT-ANFIS) based augmented wavelet de-noising technique has been recommended that depends on historical data of the water quality parameter. In the domain of interests, the water quality parameters primarily include ammoniacal nitrogen (AN), suspended solid (SS) and pH. In order to evaluate the impacts on the model, three evaluation techniques or assessment processes have been used. The first assessment process is dependent on the partitioning of the neural network connection weights that ascertains the significance of every input parameter in the network. On the other hand, the second and third assessment processes ascertain the most effectual input that has the potential to construct the models using a single and a combination of parameters, respectively. During these processes, two scenarios were introduced: Scenario 1 and Scenario 2. Scenario 1 constructs a prediction model for water quality parameters at every station, while Scenario 2 develops a prediction model on the basis of the value of the same parameter at the previous station (upstream). Both the scenarios are based on the value of the twelve input parameters. The field data from 2009 to 2010 was used to validate WDT-ANFIS. The WDT-ANFIS model exhibited a significant improvement in predicting accuracy for all the water quality parameters and outperformed all the recommended models. Also, the performance of Scenario 2 was observed to be more adequate than Scenario 1, with substantial improvement in the range of 0.5% to 5% for all the water quality parameters at all stations. On validating the recommended model, it was found that the model satisfactorily predicted all the water quality parameters (R2 values equal or bigger than 0.9). © 201

Toward bridging future irrigation deficits utilizing the shark algorithm integrated with a climate change model

Climate change is one of the most effectual variables on the dam operations and reservoir water system. This is due to the fact that climate change has a direct effect on the rainfall&ndash;runoff process that is influencing the water inflow to the reservoir. This study examines future trends in climate change in terms of temperature and precipitation as an important predictor to minimize the gap between water supply and demand. In this study, temperature and precipitation were predicted for the period between 2046 and 2065, in the context of climate change, based on the A1B scenario and the HAD-CM3 model. Runoff volume was then predicted with the IHACRES model. A new, nature-inspired optimization algorithm, named the shark algorithm, was examined. Climate change model results were utilized by the shark algorithm to generate an optimal operation rule for dam and reservoir water systems to minimize the gap between water supply and demand for irrigation purposes. The proposed model was applied for the Aydoughmoush Dam in Iran. Results showed that, due to the decrease in water runoff to the reservoir and the increase in irrigation demand, serious irrigation deficits could occur downstream of the Aydoughmoush Dam

Assessing the Predictability of an Improved ANFIS Model for Monthly Streamflow Using Lagged Climate Indices as Predictors

The current study investigates the effect of a large climate index, such as NINO3, NINO3.4, NINO4 and PDO, on the monthly stream flow in the Aydoughmoush basin (Iran) based on an improved Adaptive Neuro Fuzzy Inference System (ANFIS) during 1987&ndash;2007. The bat algorithm (BA), particle swarm optimization (PSO) and genetic algorithm (GA) were used to obtain the ANFIS parameter for the best ANFIS structure. Principal component analysis (PCA) and Varex rotation were used to decrease the number of effective components needed for the streamflow simulation. The results showed that the large climate index with six-month lag times had the best performance, and three components (PCA1, PCA2 and PCA3) were used to simulate the monthly streamflow. The results indicated that the ANFIS-BA had better results than the ANFIS-PSO and ANFIS-GA, with a root mean square error (RMSE) 25% and 30% less than the ANFIS-PSO and ANFIS-GA, respectively. In addition, the linear error in probability space (LEPS) score for the ANFIS-BA, based on the average values for the different months, was less than the ANFIS-PSO and ANFIS-GA. Furthermore, the uncertainty values for the different ANFIS models were used and the results indicated that the monthly simulated streamflow by the ANFIS was computed well at the 95% confidence level. It can be seen that the average streamflow for the summer season is 75 m3/s, so that the stream flow for summer, based on climate indexes, is more than that in other seasons