Nitrate distribution in groundwater is a highly complex, spatially and temporally highly variable system with pronounced regional and vertical hydrogeochemical differentiation. In the research project "NiMo 4.0", we have developed three AI-controlled modules, which offer solutions for the resulting practical issues.
The first module is used for the intelligent regionalisation of the parameter nitrate. This enables a better assessment of nitrate pollution by authorities and water suppliers. In the second module, data-driven, AI-based algorithms were developed for the information-based optimisation of a groundwater monitoring network. In the third module, a dynamic
event detection system was set up for the automated plausibility check of analysis data. We developed, calibrated, and applied these intelligent modules using data from various pilot regions in Germany. In addition, the complex algorithms were integrated into a user-friendly interface. With that, data and analysis results can be presented in a clear structure, thus supporting decision-making processes.
We thank our pilot partners Zweckverband Landeswasserversorgung, Wasser- und Abwasser-Zweckverband Niedergrafschaft, LUBW (Baden-Württemberg), NLWKN (Lower Saxony) and other data suppliers (GWD-WV, LfU Brandenburg, LfU Rheinland-Pfalz, TLUBN Thüringen) for providing us with the data necessary to realise this project.
