Integrating an artificial intelligence approach with k-means clustering to model groundwater salinity: the case of Gaza coastal aquifer (Palestine)

Alagha, Jawad S.; Seyam, Mohammed; Md Said, Md Azlin; Mogheir, Yunes

Integrating an artificial intelligence approach with k-means clustering to model groundwater salinity: the case of Gaza coastal aquifer (Palestine) | Intégration d’une approche d’intelligence artificielle avec des moyennes de k par bouquet pour modéliser la salinité de l’eau souterraine: cas de l’aquifère côtier de Gaza (Palestine) Integración de un enfoque de inteligencia artificial con el agrupamiento de k-medios para modelar la salinidad del agua subterránea: el caso del acuífero costero de Gaza (Palestina) دمج تقنية الذكاء الصناعي مع وسيلة التصنيف "k-means" لنمذجة ملوحة المياه الجوفية : الحالة الدراسية، خزان قطاع غزة الجوفي (فلسطين) 人工智能方法与k-均值聚类结合在一起模拟地下水盐度:(巴勒斯坦)加沙沿海含水层的实例 Integrando uma abordagem de inteligência artificial com clusterização por k-means para modelar a salinidade das águas subterrâneas: o caso de um aquífero costeiro de Gaza (Palestina)

2017

Alagha, Jawad S. | Seyam, Mohammed | Md Said, Md Azlin | Mogheir, Yunes

Artificial intelligence (AI) techniques have increasingly become efficient alternative modeling tools in the water resources field, particularly when the modeled process is influenced by complex and interrelated variables. In this study, two AI techniques—artificial neural networks (ANNs) and support vector machine (SVM)—were employed to achieve deeper understanding of the salinization process (represented by chloride concentration) in complex coastal aquifers influenced by various salinity sources. Both models were trained using 11 years of groundwater quality data from 22 municipal wells in Khan Younis Governorate, Gaza, Palestine. Both techniques showed satisfactory prediction performance, where the mean absolute percentage error (MAPE) and correlation coefficient (R) for the test data set were, respectively, about 4.5 and 99.8% for the ANNs model, and 4.6 and 99.7% for SVM model. The performances of the developed models were further noticeably improved through preprocessing the wells data set using a k-means clustering method, then conducting AI techniques separately for each cluster. The developed models with clustered data were associated with higher performance, easiness and simplicity. They can be employed as an analytical tool to investigate the influence of input variables on coastal aquifer salinity, which is of great importance for understanding salinization processes, leading to more effective water-resources-related planning and decision making.

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