The saturation percentage (SP) of soils is an important index in hydrological studies. In this paper, artificial neural networks (ANNs), multiple regression (MR), and adaptive neural-based fuzzy inference system (ANFIS) were used for estimation of saturation percentage of soils collected from Boukan region in the northwestern part of Iran. Percent clay, silt, sand and organic carbon (OC) were used to develop the applied methods. In additions contributions of each input variable were assessed on estimation of SP index. Two performance functions, namely root mean square errors (RMSE) and determination coefficient (R 2 ), were used to evaluate the adequacy of the models. ANFIS method was found to be superior over the other methods. It is, then, proposed that ANFIS model can be used for reasonable estimation of SP values of soils.
Up to day, drip irrigation systems have reached to a high level of technology. But, these systems are not able to show their potential benefits, due to various reasons. Emitter clogging can affect distribution uniformity and the system performance, which has direct relationship with water quality. In this study five types of emitters with different nominal discharges, with or without self-flushing system and with or without pressure compensating system were evaluated under three management schemes; untreated well water (S1), acidic treated water (S2) and magnetic treated water (S3) in order to reduce chemical clogging. Flow reduction rate, statistical uniformity coefficient (Uc), emission uniformity coefficient (Eu) and variation coefficient of emitters' performance in the field (Vf) were monitored. The emitter performance indexes (Uc and Eu) decreased during the experiment due to emitter clogging. The Uc and Eu values in different management schemes confirmed that the acidification has better performance than the magnetic water in order to control emitter clogging and keep high distribution uniformity. Regarding to Vf values, the priority of untreated and treated water was as S2>S3>S1 for each emitter.
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