In this paper two models are presented based on Data-Driven Modeling (DDM) techniques (Artificial Neural Network and neuro-fuzzy systems) for more comprehensive and more accurate prediction of the pipe failure rate and an improved assessment of the reliability of pipes.Furthermore, a multivariate regression approach has been developed to enable comparison with the DDM-based methods. Unlike the existing simple regression models for prediction of pipe failure rates in which only few factors of diameter, age and length of pipes are considered, in this paper other parameters such as pressure and pipe depth, are also included. Furthermore, an investigation is carried out on most commonly used mechanical reliability relationships and the results of incorporation of the proposed pipe failure models in the reliability index are compared.The proposed models are applied to a real case study involving a large water distribution network in Iran and the results of model predictions are compared with measured pipe failure data.Compared with the results of neuro-fuzzy and multivariate regression models, the outcomes of the artificial neural network model are more realistic and accurate in the prediction of pipe failure rates and evaluation of mechanical reliability in water distribution networks.

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Background: Nitrogen leaching from agricultural lands is a major threat to groundwater and surface waters. This study investigated the relationship between the characteristics of wheat-straw biochar produced at different temperatures and its impact on the uptake of NO3-N. Methods: Three types of biochar were produced from wheat straw at three different pyrolysis temperatures of 300, 400 and 500°C, and sampling was done 3 times for each biochar. Physical and chemical characteristics of biochar were determined using a variety of methods including specific surface with methylene blue adsorption method, and elemental content with elemental analyzer, and water solubility with standard ASTM (D5029-28) method. Statistical analysis was performed using Freundlich and Langmuir models. Nitrate concentration was measured using a UV-V spectrophotometer with a wavelength of 500 nm. Results: It was indicated that with an increase in biochar pyrolysis temperature from 300 to 500°C, the hydrogen, oxygen and nitrogen in the biochar were significantly decreased (P < 0.05) while the carbon content, surface area, density and water solubility in biochar (P < 0.05) were increased. The results also showed that the maximum nitrate adsorptive capacity of the three types of biochar occurred at pH=6 and contact time of 120 minutes. With increasing the temperature of biochar preparation, the efficiency of biochar nitrate adsorption increased significantly. Conclusion: The present study shows that pyrolysis temperature greatly influences the biochar chemical and physical characteristics, and subsequently nitrate adsorption ability of the biochars. The wheat straw biochar, which is produced at a pyrolysis temperature of 500°C, has the highest adsorption capacity for nitrate.