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Portable mid-infrared (MIR) technology is well suited for the provision of detailed and inexpensive information on key soil properties for optimum soil management. This technology requires prior complex multivariate modelling. In this manuscript, we propose an intelligent system approach based on portable MIR spectroscopy data modelled by machine learning techniques to predict total carbon (TC), nitrogen (TN), cation exchange capacity (CEC), clay, silt and exchangeable sodium (Na+) in 458 representative soil samples from Australia. To find the best performing algorithm to be incorporated into the intelligent system, we evaluated the performance of a number of machine learning techniques, including Gaussian process regression (GPR), Random Forest (RF), M5 rules, and Bagging and Decision Trees, which were compared with traditional Partial Least Squares Regression (PLSR). Generally, all the models showed a satisfactory performance. However, GPR stood out (according to statistical tests), especially for the predictions of clay, Na+ and CEC, obtaining root mean squared error differences between the GPR and PLSR techniques of −0.27%, −0.40% and −0.63 cmol+ kg−1, respectively. This study evidenced the feasibility of using the GPR technique to model data provided by a portable MIR instrument for the prediction of soil properties, providing the best, most stable and reliable results. Highlights • Different machine learning techniques have been tested to predict soil properties. • The predicted soil properties are TC, TN, CEC, clay, silt and Na+. • The best predictive machine learning technique has been the Gaussian Process. • The Gaussian process is better compared to the traditional PLSR technique. • The Gaussian Process is the candidate for the development of intelligent system. [ABSTRACT FROM AUTHOR]

The availability of water resources is limited, and rising consumption has increased pressure on natural resources. Therefore, reclaimed water represents an alternative option for use in urban areas, industry and, in particular, agriculture. Recent research has shown that some pharmaceutical compounds are not fully removed by wastewater treatment plants (WWTPs) and may eventually be released into agricultural systems through the application of wastewater-based resources (sludge and effluent). The present study develops an intelligent expert system (based on a feedforward neural network trained via an extreme learning machine algorithm) for predicting the carbamazepine (CBZ) and diclofenac (DCF) content in lettuce tissues irrigated with reclaimed water from WWTPs. This reduces laboratory costs, mitigates the negative impacts on the environment and leads to more effective, safer decisions on the use of reclaimed water in agriculture. The results obtained, which were validated through statistical testing, demonstrate that the intelligent expert system is well calibrated and reliable. Finally, this system was used to predict maps of CBZ and DCF accumulation in lettuce crops if they were watered with effluent from 10 WWTPs located in the Region of Murcia (Spain). In conclusion, our system provides highly accurate predictions of the amount of CBZ and DCF contained in different lettuce tissues (roots and leaves) and the predicted concentrations do not present any health risk. Highlights • Expert system predicts pharmaceutical absorption in lettuce by analysing effluent. • Predictions for accumulation in lettuces irrigated from 10 treatment plants in Murcia. • Predicted concentrations in lettuce do not constitute a potential risk to health. [ABSTRACT FROM AUTHOR]

A correct estimation of the instantaneous peak flow (IPF) is crucial to reducing the consequences of flash floods. An approach to estimate the IPF, obtained by combining Soil and Water Assessment Tool (SWAT) simulation and machine-learning models, was proposed and then verified by comparison with observation-based results in the Ladra river basin, northwest Spain. The SWAT model has been used to estimate the maximum mean daily flow (MMDF), and machine-learning models have been used to estimate the IPF based on MMDF. Four nonlinear time-series intelligence models, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and extreme learning machine (ELM) were applied, and their results were compared. The Modified Nash-Sutcliffe efficiency coefficient (MNSE) and the index of agreement (d) were used to evaluate SWAT performance while simulating MMDF, and the coefficient of determination (R2) and the root mean square error (RMSE) were employed to evaluate the performance of these intelligent systems. According to the results, the SWAT hydrological model is a useful tool to simulate MMDF. Validation analyses resulted in values of statistical indexes (MNSE = 0.64 and d = 0.95). Regarding intelligent systems, the results show that they can be successfully used in predicting IPF, but ELM has demonstrated a superior ability to estimate IPF from the MMDF (R2 = 0.86 and RMSE = 48.59). The results of this study can contribute to predicting IPF in areas where sub-daily observational data are scarce, thereby reducing uncertainties associated with IPF estimations. Graphical abstract Highlights • SWAT model has been used to estimate maximum mean daily flows. • Intelligent Systems estimate instantaneous peak flow based on the daily flows. • Extreme Learning and Support Vector Machine were optimal to estimate peak flow. • Predictions correlated with experimental data with coefficients of 0.86–0.88. • Results assist predicting peak flow with limited sub-daily observational data. [ABSTRACT FROM AUTHOR]