{'fraction_non_alphanumeric': 0.03897944720056697, 'fraction_numerical': 0.011339475549255847, 'mean_word_length': 5.060085836909871, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Estimating the amount of cadmium and lead in the polluted soil using artificial intelligence models

Abstract Contamination issues especially heavy metals such as cadmium (Cd) and lead (Pb) are currently considered as one of the most important and unsolved issues, which are directly connected with human and environmental health. Hence, its accurate estimation is of vital importance in the agricultural and environmental engineering. In this study, lead and cadmium were estimated from readily measurable soil data namely, clay, organic carbon (O.C.), pH, phosphorus (P), and total nitrogen (T.N.) using the multiple linear regression (MLR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. For this purpose, 250 soil samples collected in the Province of Gilan in Iran were used to train and test the above-mentioned models. For the assessment models, the statistical parameters such as the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) were used. The results showed that the ANN model with the RMSE of 1.04 and 0.23 outperforms the ANFIS model with the RMSE of 2.56 and 1.27 for the cadmium and lead, respectively. Finally, the results of the sensitivity analyses showed that the organic carbon and phosphorus have the most and least significant effects on the estimation of lead and cadmium parameters, respectively.