Assessment of groundwater nitrate contamination hazard in a semi-arid region by using integrated parametric IPNOA and data-driven logistic regression models Hossein Mojaddadi Rizeei & Omer Saud Azeez & Biswajeet Pradhan & Hayder Hassan Khamees Received: 7 June 2018 /Accepted: 26 September 2018 # Springer Nature Switzerland AG 2018 Abstract Groundwater hazard assessments involve many activities dealing with the impacts of pollution on groundwater, such as human health studies and en- vironment modelling. Nitrate contamination is consid- ered a hazard to human health, environment and eco- system. In groundwater management, the hazard should be assessed before any action can be taken, particularly for groundwater pollution and water quality. Thus, pol- lution due to the presence of nitrate poses considerable hazard to drinking water, and excessive nutrient loads deteriorate the ecosystem. The parametric IPNOA mod- el is one of the well-known methods used for evaluating nitrate content. However, it cannot predict the effect of soil and land use/land cover (LULC) types on calcula- tions relying on parametric well samples. Therefore, in this study, the parametric model was trained and inte- grated with the multivariate data-driven model with different levels of information to assess groundwater nitrate contamination in Saladin, Iraq. The IPNOA mod- el was developed with 185 different well samples and contributing parameters. Then, the IPNOA model was integrated with the logistic regression (LR) model to predict the nitrate contamination levels. Geographic in- formation system techniques were also used to assess the spatial prediction of nitrate contamination. High- resolution SPOT-5 satellite images with 5 m spatial resolution were processed by object-based image anal- ysis and support vector machine algorithm to extract LULC. Mapping of potential areas of nitrate contami- nation was examined using receiver operating charac- teristic assessment. Results indicated that the optimised LR-IPNOA model was more accurate in determining and analysing the nitrate hazard concentration than the standalone IPNOA model. This method can be easily replicated in other areas that have similar climatic con- dition. Therefore, stakeholders in planning and environ- mental decision makers could benefit immensely from the proposed method of this research, which can be potentially used for a sustainable management of urban, industrialised and agricultural sectors. Keywords Nitrate contamination . IPNOA . GIS . Logistic regression . Groundwater hazard assessment Introduction Groundwater sources are the most crucial, dependable and valuable sources of water in all climatic regions in the world (Sacco et al. 2006). The demand for ground- water continues to increase because of population growth, agricultural requirements, urbanisation (Ettazarini 2007) and rapid industrialisation (Pradhan 2009). Groundwater has more benefits than surface Environ Monit Assess (2018) 190:633 https://doi.org/10.1007/s10661-018-7013-8 H. M. Rizeei : B. Pradhan (*) Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, Australia e-mail: biswajeet24@gmail.com e-mail: Biswajeet.Pradhan@uts.edu.au O. S. Azeez : H. H. Khamees Department of Civil Engineering, Faculty of Engineering, University Putra Malaysia, Seri Kembangan, Selangor, Malaysia