Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.
Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.
Sci Total Environ. 2023 Feb 1;858(Pt 2):159697. doi: 10.1016/j.scitotenv.2022.159697. Epub 2022 Nov 2.
The growing increase in groundwater (GW) salinization in the coastal aquifers has reached an alarming socio-economic menace in Saudi Arabia and various places globally due to several natural and anthropogenic activities. Hence, evaluating the GW salinization is paramount to safeguarding the water resources planning and management. This study presents three different scenarios viz.: real field investigation, experimental laboratory analysis (using ion chromatography (IC) and inductively coupled plasma mass spectrometry (ICP-MS), etc.), and artificial intelligence (AI) based metaheuristic optimization (MO) algorithms in Saudi Arabia. The main purpose of this study is to validate the obtained experimental-based analysis using hybrid MO techniques comprising of adaptive neuro-fuzzy inference system (ANFIS) hybridized with genetic algorithm (GA), particle swarm optimization (PSO), and biogeography-based optimization (BBO) for identification of GW salinization in the coastal region of eastern Saudi Arabia. Additionally, ArcGIS 10.3 software generates the prediction map based on ANFIS-GA, ANFIS-PSO, and ANFIS-BBO. Feature selection was assessed using the PSO algorithm, and four indices evaluated the estimated models, namely, root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and standard deviation (SD). The simulated results are based on three variable input combinations, which showed that the ANFIS-PSO (MAE = 0.00439) algorithm had the highest accuracy (99 %), followed by the ANFIS-GA (MAE = 0.00767) and ANFIS-BBO (MAE = 0.0132) algorithms. Besides, Ca, Na, Mg, and Cl were the most influential parameters. The accuracy also demonstrated the potential reliability of MO algorithms based on spatial distribution mapping. The employed approach proved to be merit and reliable tool for water resources decision-makers in the coastal aquifer of Saudi Arabia. This approach is believed to improve water scarcity as one of the essential targets for Goal 6 of Sustainable Development Vision 2030 and the Kingdom in general.
地下水（GW）盐度在沙特阿拉伯和全球各地的沿海含水层中不断增加，已经对社会经济构成了严重威胁，这主要是由于多种自然和人为活动造成的。因此，评估地下水盐度对于保障水资源规划和管理至关重要。本研究提出了三种不同的方案，即：实地调查、实验室内分析（使用离子色谱法（IC）和电感耦合等离子体质谱法（ICP-MS）等）和基于人工智能的元启发式优化（MO）算法。本研究的主要目的是使用包含遗传算法（GA）、粒子群优化算法（PSO）和生物地理学优化算法（BBO）的混合 MO 技术来验证基于实验的分析结果，以识别沙特阿拉伯东部沿海地区的地下水盐度。此外，ArcGIS 10.3 软件根据 ANFIS-GA、ANFIS-PSO 和 ANFIS-BBO 生成预测图。使用 PSO 算法评估特征选择，并用四个指标评估估计模型，即均方根误差（RMSE）、平均绝对误差（MAE）、平均绝对百分比误差（MAPE）和标准差（SD）。模拟结果基于三个变量输入组合，结果表明，ANFIS-PSO（MAE = 0.00439）算法的准确性最高（99%），其次是 ANFIS-GA（MAE = 0.00767）和 ANFIS-BBO（MAE = 0.0132）算法。此外，Ca、Na、Mg 和 Cl 是最具影响力的参数。该准确性也证明了基于空间分布映射的 MO 算法具有潜在的可靠性。该方法被证明是沙特阿拉伯沿海含水层水资源决策者的一种可靠工具。该方法有望改善水资源短缺问题，这是 2030 年可持续发展愿景目标 6 和王国的重要目标之一。
