Laboratory of Water and Environment Engineering in Sahara Milieu (GEEMS), Department of Civil Engineering and Hydraulics Faculty of Applied Sciences, Kasdi Merbah University Ouargla, Ouargla, Algeria.
CAAST-CSAWM, MPKV Rahuri, Rahuri, India.
Environ Sci Pollut Res Int. 2022 Mar;29(14):21067-21091. doi: 10.1007/s11356-021-17084-3. Epub 2021 Nov 8.
Forecasting the irrigation groundwater parameters helps plan irrigation water and crop, and it is commonly expensive because it needs various parameters, mainly in developing nations. Therefore, the present research's core objective is to create accurate and reliable machine learning models for irrigation parameters. To accomplish this determination, three machine learning (ML) models, viz. long short-term memory (LSTM), multi-linear regression (MLR), and artificial neural network (ANN), have been trained. It is validated with mean squared error (MSE) and correlation coefficients (r), root mean square error (RMSE), and mean absolute error (MAE). These machine learning models have been used and applied for predicating the six irrigation water quality parameters such as sodium absorption ratio (SAR), percentage of sodium (%Na), residual sodium carbonate (RSC), magnesium hazard (MH), Permeability Index (PI), and Kelly ratio (KR). Therefore, the two scenario performances of ANN, LSTM, and MLR have been developed for each model to predict irrigation water quality parameters. The first and second scenario performance was created based on all and second reduction input variables. The ANN, LSTM, and MLR models have discovered that excluding for ANN and MLR models shows high accuracy in first and second scenario models, respectively. These model's accuracy was checked based on the mean squared error (MSE), correlation coefficients (r), and root mean square error (RMSE) for training and testing processes serially. The RSC values are highly accurate predicated values using ANN and MLR models. As a result, machine learning models may improve irrigation water quality parameters, and such types of results are essential to farmers and crop planning in various irrigation processes.
预测灌溉地下水参数有助于规划灌溉用水和作物，由于需要各种参数，因此通常成本高昂，尤其是在发展中国家。因此，本研究的核心目标是创建准确可靠的机器学习模型来预测灌溉参数。为了实现这一目标，我们训练了三个机器学习 (ML) 模型，即长短期记忆 (LSTM)、多元线性回归 (MLR) 和人工神经网络 (ANN)。我们使用均方误差 (MSE) 和相关系数 (r)、均方根误差 (RMSE) 和平均绝对误差 (MAE) 对其进行了验证。这些机器学习模型已被用于预测包括钠吸收率 (SAR)、钠百分比 (%Na)、剩余碳酸钠 (RSC)、镁危害 (MH)、渗透率指数 (PI) 和凯利比 (KR) 在内的六个灌溉水质参数。因此，我们为每个模型开发了 ANN、LSTM 和 MLR 的两种情景性能，以预测灌溉水质参数。第一种和第二种情景性能是基于所有和第二减少输入变量创建的。ANN、LSTM 和 MLR 模型发现，除了 ANN 和 MLR 模型之外，其他模型在第一和第二情景模型中均表现出较高的准确性。我们根据均方误差 (MSE)、相关系数 (r) 和均方根误差 (RMSE) 对训练和测试过程进行了连续检查，以检查这些模型的准确性。ANN 和 MLR 模型可以准确预测 RSC 值。因此，机器学习模型可以改进灌溉水质参数，这种类型的结果对于农民和各种灌溉过程中的作物规划至关重要。
