Parastoo Yavari; Ali Akbar Akhtari; Arash Azari
Abstract
In the operation of water distribution networks in cities, leakage from pipes always causes problems for human health and for the environment. Leakage openings in pipes may exist in different shapes. Circular holes are common in corroded and punched pipes. In the leakage studies, the area of these openings ...
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In the operation of water distribution networks in cities, leakage from pipes always causes problems for human health and for the environment. Leakage openings in pipes may exist in different shapes. Circular holes are common in corroded and punched pipes. In the leakage studies, the area of these openings is usually assumed to be fixed and the leakage exponent is about 0.5. In this study, an analytical equation has been presented with two purposes. First, Examining the changes in the leak area and leakage exponent of circular holes. Second, providing an equation that contains more parameters than the general leakage equations. By using such an equation, the accuracy of leakage estimation is increased due to the direct involvement of the effective parameters. Also, for the possibility of modeling different leakage equations, including the present equation, a new hydraulic analysis model has been developed. This model tries to improve leakage modeling by including more capabilities than the existing hydraulic analysis models. Results showed that the leak area in circular holes is not fixed and changes due to different parameters. Comparison of the present equation and the orifice equation showed a significant difference which confirms that the orifice equation cannot be always used for circular leaks. In the study of leakage exponent, it was found that for polyethylene pipes, the leakage exponent is higher than value of 0.5 mentioned in the other studies and it can take different values depending on the leakage position in the network. Increasing the hole diameter did not affect the leakage exponent, but increased the leakage coefficient. On the other hand, for steel pipes, the leakage coefficient was fixed and the exponent remained around 0.5. Finally, the results showed the usefulness of the developed hydraulic analysis model for implementing the scenarios defined in this study.
Ayoob Moradi; Ali Akbar Akhtari; Arash Azari
Abstract
During the recent few decades, the use of various models has been regarded as a promising option to predict groundwater level (GWL) in any given region using a wide variety of data and relevant equations. The lack of trustworthy and comprehensive data is, nevertheless, one of the most significant obstacles ...
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During the recent few decades, the use of various models has been regarded as a promising option to predict groundwater level (GWL) in any given region using a wide variety of data and relevant equations. The lack of trustworthy and comprehensive data is, nevertheless, one of the most significant obstacles that must be overcome in order to analyze and anticipate the depletion of groundwater in the context of water management. Because of this, the implementation of artificial intelligence (AI) models that are able to predict the GWL with high accuracy using a reduced amount of data is unavoidable. In this work, the GWL variations of Lur plain were simulated using GMS model by utilizing the available data and maps. The accuracy of model was assessed at both phases i.e. validation and calibration. Following that, GA-ANN and ICA-ANN approaches, together with ELM, ORELM, and GMDH models, were used in order to fulfill the demand for too smaller volumes by AI procedures. According to the results, the ORELM output had the highest correlation with the observed information, which indicates that it is the most accurate model in this regard. The correlation coefficient for this model was 0.976. Because of this, instead of utilizing a complicated GMS model that needs a significant amount of data for the simulation, an ORELM model can be used to reliably forecast the GWL in the Lur plain. This simple model allows the researchers to accurately predict changes in GWL during rainy and non-rainy years compared to other complicated and time-consuming numerical models.
Dlpak Ahmed Hamaamin; Amjad Maleki; Arash Azari; Azzadeen Darwesh; Mohammed Ahmadi
Abstract
Flood is inherently an uncertain phenomenon and the certainty and credibility of flood forecasting and warning systems will cause errors regardless of the sources of uncertainty. Extreme rainfall events are one of the most important input data to rainfall-runoff models, which always have uncertainty. ...
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Flood is inherently an uncertain phenomenon and the certainty and credibility of flood forecasting and warning systems will cause errors regardless of the sources of uncertainty. Extreme rainfall events are one of the most important input data to rainfall-runoff models, which always have uncertainty. Considering this issue the uncertainty of the design flood hydrograph can be investigated for different return periods. In this research first to simulate the flood hydrograph the HEC-HMS model was calibrated and validated based on the hourly flood hydrographs recorded at the basin outlet. Historical data were collected on the 24-hour maximum rainfall of Gharesoo Basin stations with 30-year statistics and the affected basins were identified. Then in each station 30 series of 30 years of artificial data with a maximum 24-hour rainfall were produced. For each of these produced stochastic series the best statistical distribution was fitted and in each series extreme values with a return period of 25 50 100 and 1000 years were calculated. Finally in each return period by combining 30 different amounts of rainfall obtained from stochastic series, the uncertainty bandwidth of the flood hydrograph was obtained during this return period. The results indicated that the highest predicted peak discharge for different return periods was between 1.2 and 1.7 times the historically recorded discharge during that return period. Generally the maximum discharge of different return periods was between 1.5 and 3 times the minimum discharge.
Mostafa Bayesteh; Arash Azari
Abstract
One of the most important issues in planning and managing water resources is the accurate estimation of monthly input discharge of the reservoirs in the future years, which is always associated with uncertainty. To cover these uncertainties, synthetic stream flow data generation models have been used ...
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One of the most important issues in planning and managing water resources is the accurate estimation of monthly input discharge of the reservoirs in the future years, which is always associated with uncertainty. To cover these uncertainties, synthetic stream flow data generation models have been used by various researchers to generate stochastic time series. The computational basis of different stochastic models for generating monthly data has been different and this can have a significant effect on their performance. Therefore, selection of the best model of stochastic data generation for accurate planning and management of a water resource system is one of the major concerns of water resources specialists. In this research, the performance of parametric models of synthetic stream flow generation including Thomas-Fiering, Fragment and ARMA (1,1) and ARMA (1,2) combined with Valencia-Schaake and Mejia and Rousselle models were compared and evaluated. For this purpose, 30 years data of monthly discharge of Marun river in Khuzestan province were used and 900 synthetic monthly flow time series were generated using each of the models mentioned above. Based on the obtained results, the ARMA (1,2) model combined with the Valencia-Schaake model was recognized as the best model, considering the very desired performance in preserving the statistical parameters of historical data and generating maximum and minimum discharges related to wet and dry periods in different probabilities. This model can be used with greater confidence to analyze river systems and reservoirs, manage drought and apply water rationing rules in future drought conditions.
