Flood is one of the most dangerous and destructive phenomena which endangers people’ s lives and properties all around the world. According to statistics of a 30-year period (1974-2003), about 2162 major floods have occurred which constitute 34% of the world's disasters (Tajbakhsh and Khodashenas, 2012). Floods are frequent and ruinous in Iran due to severe weather condition. Several factors intensify the risk of flood in urban areas including urbanization, land use changes, inappropriate drainage systems, and impermeable area development (Sabeti, 2011). Likak has faced numerous floods due to high rain density, high rate of urban development, unsafe and unproductive urban development, ignoring safety criteria in developing urban areas, road watering issues, inefficient drainage systems, and inefficacious water channels. Water channels and drainage issues have never been evaluated in this town. Applying an effective RUNOFF management plan is the ultimate solution for the problem of flood in Likak. Storm Water Management Model (SWMM) is one of the most reliable and prevailing models for evaluating and managing the urban RUNOFF issue. SWMM is a dynamic rainfall-RUNOFF simulator which can be used for simulating the quantity and quality of the run-off for a single raining event or continuous long-term rains (Gironas et al, 2010). Yu et al. (2014) adapted and calibrated SWMM to Jinan, a typical piedmont city in China. Fourteen storms were used for model calibration and validation, finally verifying large-scale applicability of the model to piedmont cities. Results of this study verified that SWMM is applicable to large-scale cities....

Background: In coastal cities, wastewater discharge into the sea is one of the options for sewage DISPOSAL that in case of non- compliance with health standards in wastewater DISPOSAL will be led to the spread of infection and disease. On the other hand, water resources preservation and using them efficiently are the principles of sustainable development of each country. This study was aimed to investigate the contamination of discharged RUNOFF from the surface water DISPOSAL channels of Bushehr city in 2012 - 13.Materials and Methods: In this study, Sampling was conducted by composite sampling method from output of the five main surface water DISPOSAL channels leading to the Persian Gulf located in the coastal region of Bushehr city during two seasons including wet (winter) and dry (summer) in 2012- 13. Then, experimental tests of BOD5, total coliform and fecal coliform were done on any of the 96 samples according to the standard methodResults: Analysis of the data showed that the BOD5, total coliform and fecal coliform of effluent RUNOFF of the channels were more than the national standard output of DISPOSAL wastewaters into the surface waters, and the highest and lowest amount of BOD5 which obtained were 160 mg/L and 28 mg/L, respectively.Conclusion: considering the fact that discharged RUNOFF from surface water DISPOSAL channels link from shoreline to sea in close distance and they often are as natural swimming sites and even fishing sites of Bushehr city, and also according to high level of organic and bacterial load of these channels, it is urgently required to be considered by the authorities.

The rainfall-RUNOFF relationship is one of the most complex hydrological phenomena. In recent years, hydrologists have successfully applied backpropagation neural NETWORK as a tool to model various nonlinear hydrological processes because of its ability to generalize patterns in imprecise or noisy and ambiguous input and output data sets. However, the backpropagation neural NETWORK convergence rate is relatively slow and solutions can be trapped at local minima. Hence, in this study, a new evolutionary algorithm, namely, particle swarm optimization is proposed to train the feedforward neural NETWORK. This particle swarm optimization feedforward neural NETWORK is applied to model the daily rainfall-RUNOFF relationship in Sungai Bedup Basin, Sarawak, Malaysia. The model performance is measured using the coefficient of correlation and the Nash-Sutcliffe coefficient. The input data to the model are current rainfall, antecedent rainfall and antecedent RUNOFF, while the output is current RUNOFF. Particle swarm optimization feedforward neural NETWORK simulated the current RUNOFF accurately with R = 0.872 and E2 = 0.775 for the training data set and R = 0.900 and E2 = 0.807 for testing data set. Thus, it can be concluded that the particle swarm optimization feedforward neural NETWORK method can be successfully used to model the rainfall-RUNOFF relationship in Bedup Basin and it could be to be applied to other basins.

Objectives: Ahawzas one of the metropolises of Iran in classified among lowland plains with low slope in terms of topography. In this city, unsystematic construction and immediate rainfalls and showers are the mainsources of flooded street.Method: The present study has a descriptive-analytical approach and is based on causality method. using geographic information system software (GIS) and River tools techniques, this study seeks to identify and manage of surface waters and urban floods in District 1 of Ahwaz during rainfalls and then the maps of slope, their direction and areas with flooding potentials are prepared.Findings/ Results: given the low slope of the area, the gravity drainage and pumping of water to Karoon River is impossible due tohighcosts. However, using GIS analysis, the natural routes are determined for water drainage and finally, the map of proposed surface water DISPOSAL system in the study area is presented.Conclusion and Suggestion: In District 1 of Ahwaz, the tree orhierarchical surface water collection NETWORK has been predicted based on urban streets and alleys. The collected wateris directed to the channels and then transferred to Karoon River.

From Longley, the various equations for determining the RUNOFF to water management are presented by the researchers that are widely used in hydrologic sciences. In this study by using observational data, was an evaluated empirical, artificial neural NETWORK (ANN) model in estimation of RUNOFF coefficient. The study area was Bar Ariyeh Neishabour watershed. The data of 33 flood events during 1952 to 2006 were collected. Among Characteristics from precipitation hytographs as model input variables were extracted include The average intensity of rainfall, average rainfall, 1 to 4rd quartiles of rainfall, 1 to 4rd quartiles intensity of rainfall, total precipitation of five days before, the index ϕ. Therefore, using these parameters and different combinations in the input layer NETWORK, different NETWORKs were implemented. Artificial neural NETWORK is used learning algorithm with Levenberg-Marqwart and Hyperbolic tangant trained and performed with various inputs. The results showed, NETWORK with 1 to 4rd quartiles intensity of rainfall, average rainfall, time of rainfall, total precipitation of five days before and ϕ index as input layer with Hyperbolic tangant transfer function could predict storm RUNOFF coefficient with determination coefficient 0.98 and the Root Mean Squared Error 0.0337 and Mean Absolute Error 0.0275.

Accurate simulation RUNOFF process can have a significant role in water resources management and related issues. The inherent complexity of this process makes difficult the use of physical and numerical models. In recent years, application of intelligent models is increased a powerful tool in hydrological modeling. The aim of this study was the application of the Gamma test to select the optimal combination of input variables for RUNOFF modeling in Sofi Chay. Streamflow modeling was performed based on the optimum number of the selected variables using the artificial neural NETWORK (ANN) and Support vector machine (SVM) methods. Gamma test results showed that monthly RUNOFF with six antecedent RUNOFF values provide better results to predict. RUNOFF simulation using support vector machines and artificial neural NETWORK models also showed that the best input structure will be delayed until six to predict of next month RUNOFF. Among to models with the same input structure, support vector machine have relatively high efficiency compared to artificial neural NETWORK.

IntroductionThe rainfall-RUNOFF process, which is affected by various hydrological parameters, is one of the most complex hydrological processes and one of the most basic hydrological topics related to understanding and predicting the processes of RUNOFF production and transfer. It is the outlet point of the watershed. Planning and optimal utilization of RUNOFF is one of the essential issues in watersheds. Therefore, knowing the natural capacity of RUNOFF production and simulating rainfall-RUNOFF is very important. Artificial intelligence and the use of neural NETWORK models are one of the methods of rainfall-RUNOFF forecasting. An artificial neural NETWORK is a method with the ability to learn, understand, master relationships, and resist errors. Today, artificial intelligence black box methods such as self-constructing and self-learning functions have a wide ability to model and predict complex problems.Materials and MethodsThe purpose of this research is to evaluate the performance of the artificial neural NETWORK model for rainfall-RUNOFF simulation in the Saghez sub-basin in Kurdistan province. To carry out this research, 18-year (2001-2018) data received daily from the Meteorological Organization and Saghez Regional Water and Hydrometry Company have been used. In this study, two types of meteorological and hydrometric data were used. The meteorological parameters used include precipitation, evaporation, average temperature, maximum and minimum temperature, and the hydrometric parameter used in this research was only discharge. In the Saghez basin, rainfall-RUNOFF changes have always been considered one of the prominent hydrological indicators. Since the turpentine sub-basin is considered an open basin in terms of its nature, precipitation can be considered a suitable alternative for investigating discharge in the study area of this research. As a result, precipitation is selected as a potential input variable and the adequacy of the remaining variables will be tested separately for the neural NETWORK model. In this research, the meteorological parameters used include precipitation, evaporation, average temperature, and maximum and minimum temperature, and the hydrometric parameter used in this research was only Dubai. Finally, to simulate rainfall-RUNOFF using an artificial neural NETWORK model, scenarios with different input variables were considered. To evaluate and validate the performance results of the simulated model in different scenarios of this study, using four statistical criteria of correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe index (NSE) was done.Results and DiscussionSix investigated scenarios were randomly selected by combining different inputs. In the first scenario, the input variable includes precipitation and the output variable is discharge. In the second scenario, the input variables include precipitation and evaporation and the output variable is discharge. In the third scenario, the input variables include precipitation and average temperature and the output variable is discharge. In the fourth scenario, the input variables include precipitation and flow variables with a one-day delay and the output variable of flow. In the fifth scenario, the input variables include precipitation, average temperature, maximum and minimum temperature, and the output variable is discharge. In the sixth scenario, the input variables include precipitation, evaporation, average temperature, maximum and minimum temperature, and the output variable of discharge. In all six scenarios, the output variable is the flow rate. Also, in the modeling, 70% of the data for the training section and 30% of the data for the test section were examined. According to the final results, the performance of the artificial neural NETWORK model in scenario number four (input variables including rainfall and discharge with a one-day delay) among the six developed scenarios, with correlation coefficient values of 0.92, mean squared error of 6.65, the average absolute error is 2.04 and the Nash-Sutcliffe index is 0.84 in the education section with the values of 0.91, 5.34, 1.57, and respectively 0.82 selected as the best combination in the test section, and in terms of statistical performance indicators, the results of the Nash-Sutcliffe index values in the training and test section were closer to one, which indicates a good match between the observed values and It is simulated. Also, the correlation coefficient specifies the amount of agreement and distribution of observational data with the predicted results, which can be said that the error measurement indicators and data distribution in the training and test section are a favorable result for prediction. The amount of discharge in this scenario shows that it has a much better performance than the rest of the scenarios. Also, in the fourth scenario, changes in the time series of observed discharge values against the simulated values in the training and test phase were investigated in the artificial neural NETWORK model. According to this figure, compared to the observed value, the simulated flow rate had good accuracy and an acceptable error value.ConclusionThe obtained results showed that for the sub-basin of turpentine, the algorithm of the artificial neural NETWORK model for simulating rainfall-RUNOFF on a suitable daily scale has obtained suitable and acceptable results. So it can be said that artificial neural NETWORK modeling has high accuracy and low error for the study area. Also, artificial intelligence models can be used as a useful tool and a reliable approach for water resource managers.

Estimates, forecasts and RUNOFF management have always been an interest to researchers. Therefore, using any of the methods commonly used in estimating seemingly destructive phenomenon that, unfortunately, due to the complexity of the relationship between rainfall and RUNOFF and non-linearity of the relationship, the results are not good. Todays, with the advancement of science and the development of new techniques in all aspects of science, understanding and settle in for a good hope to have created such relationship. One of approaches that have attracted attention of researchers in recent decades is using of Neural NETWORKs. In this study, the Neural- Wavelet NETWORK for Estimating RUNOFF in Khersan catchment area is used. The results obtained from this model with results from a Neural NETWORK of Return Propagation and Neural NETWORK of Fundamental-Radial, as older models, compared and analyzed. Comparison of results was performed by correlation coefficient and Root Mean Square Error. The results show that the accuracy of the Neural- Wavelet NETWORK compared to Neural NETWORK of Return Propagation and Neural NETWORK of Fundamental-Radial is better.