Effects of Operating Pressure, Nozzle Diameter and Wind Speed on the Performance of Sprinkler in Irrigation System during Water Application

Nigatie, Dereje Bishaw; Dinka, Megersa Olumana; Hordofa, Tilahun

doi:10.18034/abcjar.v7i2.78

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Some studies found that meteorological parameters, such as humidity and temperature, influence water distribution patterns in sprinkler irrigation systems [6,8]. The authors of [9] conducted experiments to evaluate how water pressure, nozzle diameter, and wind speed affect the performance of sprinkler irrigation systems during irrigation. They found that nozzle diameter and operating pressure positively influence the CUC.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Neural Networks versus Multiple Linear Regressions to Predict the Christiansen Uniformity Coefficient in Sprinkler Irrigation

Skhiri,

Gabsi,

Dewidar

et al. 2023

Agronomy

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The Christiansen Uniformity Coefficient (CUC) describes the distribution of water in a sprinkler system. In this study, two types of models were developed to predict the Christiansen Uniformity Coefficient (CUC) of sprinkler irrigation systems: Artificial Neural Network (ANN), specifically the feed-forward neural networks, and multiple linear regression (MLR) models. The models were trained on a dataset of published research on the CUC of sprinkler irrigation systems, which included data on a variety of design, operating, and meteorological condition variables. In order to build the predictive model of CUC, 10 input parameters were used including sprinkler height (H), working pressure (P), nozzle diameter (D and da), sprinkler line spacing (SL), sprinkler spacing (SS), wind speed (WS), wind direction (WD), temperature (T), and relative humidity (RH). Fifty percent (50%) of the data was used to train ANN models and the remaining data for cross-validation (25%) and for testing (25%). Multiple linear regression models were built using the training data. Four statistical criteria were used to evaluate the model’s predictive quality: the correlation coefficient (R), the index of agreement (d), the root mean square error (RMSE), and the mean absolute error (MAE). Statistical analysis demonstrated that the best predictive ability was obtained when the models (ANN and MLR) utilized all the input variables. The results demonstrated that the accuracy of ANN models, predicting the CUC of sprinkler irrigation systems, is higher than that of the MLR ones. During the training stage, the ANN models were more accurate in predicting CUC than MLR, with higher R (0.999) and d (0.999) values and lower MAE (0.167) and RMSE (0.456) values. The R values of the MLR model fluctuated between 0.226 and 0.960, the d values oscillated from 0.174 to 0.979, the MAE values were in the range of 2.458% and 10.792%, and the RMSE values fluctuated from 2.923% to 13.393%. Furthermore, the study revealed that WS and WD are the most influential climatic parameters. The ANN model can be used to develop more accurate tools for predicting the CUC of sprinkler irrigation systems. This can help farmers to design and operate their irrigation systems more efficiently, which can save them time and money.

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Section: Introductionmentioning

confidence: 99%

“…They found that nozzle diameter and operating pressure positively influence the CUC. Proportional increases in CUC are also expected when the nozzle diameter and operating pressure increase [9]. The Christiansen Uniformity Coefficient is affected by the distance separating nozzles [10].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks versus Multiple Linear Regressions to Predict the Christiansen Uniformity Coefficient in Sprinkler Irrigation

Skhiri,

Gabsi,

Dewidar

et al. 2023

Agronomy

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Intelligent Computing for Precision Agriculture

Gupta

Jhajharia

Mathur

2022

Handbook of Intelligent Computing and Optimization for Sustainable Development

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Demographic characteristics and prevalence of asymptomatic Leishmania donovani infection in migrant workers working in an endemic area in Northwest Ethiopia

Yimer,

Takele,

Yizengaw

et al. 2024

Front. Epidemiol.

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IntroductionVisceral leishmaniasis (VL), a neglected tropical disease that causes substantial morbidity and mortality, is a serious health problem in Ethiopia. Infections are caused by Leishmania (L.) donovani parasites. Most individuals remain asymptomatic, but some develop VL, which is generally fatal if not treated. We identified the area of Metema-Humera in Northwest Ethiopia as a setting in which we could follow migrant workers when they arrived in an endemic area. The demographic characteristics of this population and factors associated with their risk of asymptomatic infection are poorly characterised.MethodsWe divided our cohort into individuals who visited this area for the first time (first comers, FC) and those who had already been in this area (repeat comers, RC). We followed them from the beginning (Time 1, T1) to the end of the agricultural season (Time 2, T2), performing tests for sand fly bite exposure (anti-sand fly saliva antibody ELISA) and serology for Leishmania infection (rK39 rapid diagnostic test and the direct agglutination test) at each time point and collecting information on risk factors for infection.ResultsOur results show that most migrant workers come from non-endemic areas, are male, young (median age of 20 years) and are farmers or students. At T1, >80% of them had been already exposed to sand fly bites, as shown by the presence of anti-saliva antibodies. However, due to seasonality of sand flies there was no difference in exposure between FC and RC, or between T1 and T2. The serology data showed that at T1, but not at T2, a significantly higher proportion of RC were asymptomatic. Furthermore, 28.6% of FC became asymptomatic between T1 and T2. Over the duration of this study, one FC and one RC developed VL. In multivariable logistic regression of asymptomatic infection at T1, only age and the number of visits to Metema/Humera were significantly associated with asymptomatic infection.ConclusionA better understanding of the dynamics of parasite transmission and the risk factors associated with the development of asymptomatic infections and potentially VL will be essential for the development of new strategies to prevent leishmaniasis.

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Effects of Operating Pressure, Nozzle Diameter and Wind Speed on the Performance of Sprinkler in Irrigation System during Water Application

Cited by 3 publications

References 13 publications

Artificial Neural Networks versus Multiple Linear Regressions to Predict the Christiansen Uniformity Coefficient in Sprinkler Irrigation

Artificial Neural Networks versus Multiple Linear Regressions to Predict the Christiansen Uniformity Coefficient in Sprinkler Irrigation

Intelligent Computing for Precision Agriculture

Demographic characteristics and prevalence of asymptomatic Leishmania donovani infection in migrant workers working in an endemic area in Northwest Ethiopia

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