Abstract: This paper reports the behavior of the dissolved U-isotopes 238U and 234U in groundwater providing from 15 cities in Amazonas State, Brazil. The isotope dilution technique accompanied by alpha spectrometry were utilized for acquiring the U content and 234U/238U activity ratio (AR) data, 0.01–1.4µgL−1 and 1.0–3.5, respectively. These results suggest that the water is circulating in a reducing environment and leaching strata containing minerals with low uranium concentration. A tendency to increasing ARs values following the groundwater flow direction is identified in Manaus city. The AR also increases according to the SW–NE directions: Uarini→Tefé; Manacapuru→Manaus; Presidente Figueiredo→São Sebastião do Uatumã; and Boa Vista do Ramos→Parintins. Such trends are possibly related to several factors, among them the increasing acid character of the waters. The waters analyzed are used for human consumption and the highest dissolved U content is much lower than the maximum established by the World Health Organization. Therefore, in view of this radiological parameter they can be used for drinking purposes.

Abstract: Predicting groundwater levels is pivotal in curbing overexploitation and ensuring effective water resource governance. However, groundwater level prediction is intricate, driven by dynamic nonlinear factors. To comprehend the dynamic interaction among these drivers, leveraging machine learning models can provide valuable insights. The drastic increase in computational capabilities has catalysed a substantial surge in the utilisation of machine learning-based solutions for effective groundwater management. The performance of these models highly depends on the selection of hyperparameters. The optimisation of hyperparameters is a complex process that often requires application-specific expertise for a skillful prediction. To mitigate the challenge posed by hyperparameter tuning’s problem-specific nature, we present an innovative approach by introducing the automated machine learning (AutoML-GWL) framework. This framework is specifically designed for precise groundwater level mapping. It seamlessly integrates the selection of best machine learning model and adeptly fine-tunes its hyperparameters by using Bayesian optimisation. We used long time series (1997-2018) data of precipitation, temperature, evaporation, soil type, relative humidity, and lag of groundwater level as input features to train the AutoML-GWL model while considering the influence of Land Use Land Cover (LULC) as a contextual factor. Among these input features, the lag of groundwater level emerged as the most relevant input feature. Once the model is trained, it performs well over the unseen data with a strong correlation of coefficient (R = 0.90), low root mean square error (RMSE = 1.22), and minimal bias = 0.23. Further, we compared the performance of the proposed AutoML-GWL with sixteen benchmark algorithms comprising baseline and novel algorithms. The AutoML-GWL outperforms all the benchmark algorithms. Furthermore, the proposed algorithm ranked first in Friedman’s statistical test, confirming its reliability. Moreover, we conducted a spatial distribution and uncertainty analysis for the proposed algorithm. The outcomes of this analysis affirmed that the AutoML-GWL can effectively manage data with spatial variations and demonstrates remarkable stability when faced with small uncertainties in the input parameters. This study holds significant promise in revolutionising groundwater management practices by establishing an automated framework for simulating groundwater levels for sustainable water resource management.