Electrical conductivity (EC), one of the most widely used indices for water quality assessment, has been applied to predict the salinity of the Babol-Rood River, the greatest source of irrigation water in northern Iran. This study uses two individual—M5 Prime (M5P) and random forest (RF)—and eight novel hybrid algorithms—bagging-M5P, bagging-RF, random subspace (RS)-M5P, RS-RF, random committee (RC)-M5P, RC-RF, additive regression (AR)-M5P, and AR-RF—to predict EC. Thirty-six years of observations collected by the Mazandaran Regional Water Authority were randomly divided into two sets: 70% from the period 1980 to 2008 was used as model-training data and 30% from 2009 to 2016 was used as testing data to validate the models. Several water quality variables—pH, HCO3−, Cl−, SO42−, Na+, Mg2+, Ca2+, river discharge (Q), and total dissolved solids (TDS)—were modeling inputs. Using EC and the correlation coefficients (CC) of the water quality variables, a set of nine input combinations were established. TDS, the most effective input variable, had the highest EC-CC (r = 0.91), and it was also determined to be the most important input variable among the input combinations. All models were trained and each model’s prediction power was evaluated with the testing data. Several quantitative criteria and visual comparisons were used to evaluate modeling capabilities. Results indicate that, in most cases, hybrid algorithms enhance individual algorithms’ predictive powers. The AR algorithm enhanced both M5P and RF predictions better than bagging, RS, and RC. M5P performed better than RF. Further, AR-M5P outperformed all other algorithms (R2 = 0.995, RMSE = 8.90 μs/cm, MAE = 6.20 μs/cm, NSE = 0.994 and PBIAS = −0.042). The hybridization of machine learning methods has significantly improved model performance to capture maximum salinity values, which is essential in water resource management.

中文翻译：

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使用混合机器学习模型预测河流水盐度

电导率 (EC) 是最广泛使用的水质评估指标之一，已被用于预测伊朗北部最大的灌溉水源巴博鲁德河的盐度。本研究使用两个个体——M5 Prime（M5P）和随机森林（RF）——以及八种新颖的混合算法——bagging-M5P、bagging-RF、随机子空间（RS）-M5P、RS-RF、随机委员会（RC）—— M5P、RC-RF、加性回归 (AR)-M5P 和 AR-RF——预测 EC。Mazandaran 地区水务局收集的 36 年观测数据随机分为两组：1980 年至 2008 年期间的 70% 用作模型训练数据，2009 年至 2016 年期间的 30% 用作测试数据以验证楷模。几个水质变量——pH、HCO3−、Cl−、SO42−、Na+、Mg2+、Ca2+、河流排放量 (Q)、和总溶解固体 (TDS)——是建模输入。使用 EC 和水质变量的相关系数 (CC)，建立了一组九个输入组合。TDS 是最有效的输入变量，其 EC-CC 最高（r = 0.91），也被确定为输入组合中最重要的输入变量。训练所有模型，并使用测试数据评估每个模型的预测能力。几个定量标准和视觉比较被用来评估建模能力。结果表明，在大多数情况下，混合算法增强了单个算法的预测能力。AR 算法比 bagging、RS 和 RC 更好地增强了 M5P 和 RF 预测。M5P 的表现优于 RF。此外，AR-M5P 优于所有其他算法（R2 = 0.995，RMSE = 8.90 μs/cm，MAE = 6.20 μs/cm，NSE = 0.994 和 PBIAS = -0.042）。机器学习方法的混合显着提高了模型性能以捕获最大盐度值，这在水资源管理中至关重要。