Mahmoudi Maedeh, Mahdavi-Meymand Amin, AlDallal Ammar, Zounemat-Kermani Mohammad
Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
Institute of Hydro-Engineering, Polish Academy of Sciences, Gdańsk, Poland.
Environ Sci Pollut Res Int. 2025 Jan;32(4):1985-2006. doi: 10.1007/s11356-024-35874-3. Epub 2025 Jan 4.
Groundwater resources constitute one of the primary sources of freshwater in semi-arid and arid climates. Monitoring the groundwater quality is an essential component of environmental management. In this study, a comprehensive comparison was conducted to analyze the performance of nine ensembles and regular machine learning (ML) methods in predicting two water quality parameters including total dissolved solids (TDS) and pH, in an area with semi-arid climate conditions. The study area under consideration is an aquifer located in the Sirjan plain, Kerman, Iran. The developed models include standard multilayer perceptron neural network (MLPNN), classification and regression trees (CART), Chi-square automatic interaction detection (CHAID), and their ensemble versions in bagging (BG) and boosting (BT) ensemble structures. The analysis revealed that standard MLs yield comparable results in predicting TDS. The MLPNN, exhibiting a standard root mean square error (SRMSE) of 0.085, demonstrated superior accuracy in predicting TDS when contrasted with CART and CHAID models. Predicting pH poses a greater challenge for the models. Ensemble techniques significantly enhanced the accuracy of regular models. On average, the bagging and boosting techniques resulted in a 22.68% improvement in the accuracy of regular models, which represents a statistically significant enhancement. The boosting method, with an average SRMSE of 0.0602, is more accurate than bagging. Based on the results, the CHAID-BT with SRMSE of 0.0790 and CHAID-BG with SRMSE of 0.0330 are ranked the most accurate models for predicting TDS and pH, respectively. The performance of ensemble techniques in predicting TDS is more remarkable. In practical implementation, ensemble techniques can be considered an alternative method with high accuracy for sustainable water resources management in semi-arid regions, helping to address water shortages, climate change, water pollution, etc.
地下水资源是半干旱和干旱气候地区淡水的主要来源之一。监测地下水质量是环境管理的重要组成部分。在本研究中，进行了全面比较，以分析九种集成和常规机器学习（ML）方法在预测半干旱气候条件地区的两个水质参数（包括总溶解固体（TDS）和pH值）方面的性能。所考虑的研究区域是位于伊朗克尔曼省锡尔詹平原的一个含水层。所开发的模型包括标准多层感知器神经网络（MLPNN）、分类与回归树（CART）、卡方自动交互检测（CHAID），以及它们在装袋（BG）和提升（BT）集成结构中的集成版本。分析表明，标准机器学习方法在预测TDS方面产生了可比的结果。MLPNN的标准均方根误差（SRMSE）为0.085，与CART和CHAID模型相比，在预测TDS方面表现出更高的准确性。对模型来说，预测pH值是一个更大的挑战。集成技术显著提高了常规模型的准确性。平均而言，装袋和提升技术使常规模型的准确性提高了22.68%，这代表了统计学上的显著提高。提升方法的平均SRMSE为0.0602，比装袋更准确。根据结果，SRMSE为0.0790的CHAID-BT和SRMSE为0.0330的CHAID-BG分别被列为预测TDS和pH值最准确的模型。集成技术在预测TDS方面的性能更为显著。在实际应用中，集成技术可被视为半干旱地区可持续水资源管理的一种高精度替代方法，有助于应对水资源短缺、气候变化、水污染等问题。
