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Estimation of irrigation water quality index with development of an optimum model: a case study

机译:灌溉水质指数与最优模型的估算 - 案例研究

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Surface water quality parameters are important means for determination of water's suitability for irrigation. In this research, data from 32 irrigation stations were used to calculate the sodium adsorption rate (SAR), sodium percentage (Na%), Kelly index (KI), permeability index (PI) and irrigation water quality index (IWQI) for evaluation of surface water quality. The obtained SAR, KI and Na% values, respectively, varied between 0.10 and 9.43, 0.03-1.37 meq/l and 3.16-57.82%. The calculated PI values indicate that, 93.75% of the water samples is in "suitable" category, and 6.25% is in "non-suitable" category. The IWQI values obtained from the research area varied between 30.59 and 81.09. In terms of irrigation water quality, 12.5% of the samples is of "good" quality, 15.62% is of "poor" quality, 68.75% is of "very poor" quality, and 3.12% is of "non-suitable" quality. Accordingly, IWQI value was estimated on the basis of SAR, Na%, KI and PI values using multiple regression and artificial neural network (ANN) model. The regression coefficient (R~2) was determined as 0.6 in multiple regression analysis, and a moderately significant relationship (p < 0.05) was detected. As the calculated F value was higher than the tabulated F value, a real relationship between the dependent and independent variables is inferred. Four different models were built with ANN, and the statistical performance of the models was determined using statistical parameters such as average value (μ), standard error (SE), standard deviation (σ), R~2, root mean square error (RMSE) and mean absolute percentage error (MAPE). The training R~2 value belonging to the best model was found to be significantly high (0.99). The relation between the estimation results of ANN model and the experimental data (R~2 = 0.92) verifies the model's success. As a result, ANN proved to be a successful means for IWQI estimation using different water quality parameters.
机译:表面水质参数是测定水适合灌溉的适用性的重要手段。在本研究中,32个灌溉站的数据用于计算评估的钠吸附率(SAR),钠百分比(NA%),凯利指数(Ki),渗透率指数(IWQI)地表水质。所获得的SAR,Ki和Na%值分别在0.10和9.43之间变化,0.03-1.37meq / L和3.16-57.82%。计算出的PI值表明,93.75%的水样在“合适”类别中,6.25%处于“非合适”类别。从研究区域获得的IWQI值在30.59和81.09之间变化。在灌溉水质方面,12.5%的样品具有“良好”的品质,15.62%的质量为“差”质量,68.75%的质量“非常差”,3.12%是“非合适”质量。因此,使用多元回归和人工神经网络(ANN)模型的SAR,NA%,KI和PI值估计IWQI值。在多元回归分析中测定回归系数(R〜2)为0.6,检测到中等显着的关系(P <0.05)。随着计算出的f值高于标记的f值,所以将从所依赖和独立变量之间的实际关系推断出来。使用ANN构建了四种不同的模型,使用统计参数(例如平均值(μ),标准误差(σ),标准差(σ),R〜2,均方根误差(RMSE)确定模型的统计性能))和平均绝对百分比误差(mape)。发现属于最佳模型的训练R〜2值明显高(0.99)。 ANN模型的估计结果与实验数据(R〜2 = 0.92)之间的关系验证了模型的成功。因此,安证证明是使用不同水质参数的IWQI估计的成功手段。

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