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• A complementary intelligent model predicts soil temperature using air temperature. • Modeling is conducted over 12 stations located in six different climate regions. • The proposed model shows superior soil-temperature prediction capability. Soil temperature (ST) is an essential catchment property strongly influenced by air temperature (Ta). ST is also the key factor in sustainable agricultural developments, so researchers are still motivated to develop robust machine learning (ML) models to predict ST more reliably. Four different ML models, utilizing the standalone algorithms (i.e., artificial neural networks: 'ANN' and co-active neuro-fuzzy inference systems: 'CANFIS') and complementary algorithms (i.e., wavelet transformation combined with ANN: 'WANN' and wavelet transformation combined with CANFIS: 'WCANFIS') were developed to predict the ST at six meteorological stations incorporating a wide range of climatic features to improve the overall performance. The study has utilized data over the period 2000–2010, collected at 12 locations in Iran. In the first phase of this research, the effects of climate variability on the changes in ST at different depths (i.e., 5, 10, 20, 30, 50 and 100 cm) were explored using air temperature as the exploratory and ST as the response variable. Assessing the performance of the predictive models used in ST prediction, the results indicated good predictive capability of the WCANFIS model, thus, advocating its potential utility in ST prediction problems, especially over diverse climatic regions. This study has also ascertained that the minimum and the maximum predictive errors were encountered at a depth of about 20 cm and 100 cm, respectively. The assessment of climatic features based on air temperature datasets on the performance of the models indicated the highest efficacy demonstrated by the ANN model for the case A–C–W climate type (i.e., a moist climate regime: Arid, temperature regime in winter: Cool, and temperature regime in summer: Warm), in comparison with the PH–C–W climate type (moist regime: Per-humid) for the other best ML models (i.e., WANN, WCANFIS and CANFIS). The order of the model accuracies based on the root mean square error (RMSE) can be ranked with error values of as: WCANFIS = 0.43 °C, ANN = 0.69 °C, CANFIS = 2.16 °C and WANN = 2.31 °C, demonstrating the wavelet-based CANFIS model to exceed the performance of the counterpart comparative models. The present study provides evidence of successfully developing new ML models, improved through wavelet transform for effective feature extraction, and the importance of such hybrid models that have practical implications in studying soil temperature based on air temperature feature inputs in diverse climatic conditions. The outcomes of this study are expected to support key decisions in sustainable agriculture and other related areas where soil health, based on air temperature changes, needs to be monitored or predicted. [ABSTRACT FROM AUTHOR]

Developing accurate soft computing methods for groundwater level (GWL) forecasting is essential for enhancing the planning and management of water resources. Over the past two decades, significant progress has been made in GWL prediction using machine learning (ML) models. Several review articles have been published, reporting the advances in this field up to 2018. However, the existing review articles do not cover several aspects of GWL simulations using ML, which are significant for scientists and practitioners working in hydrology and water resource management. The current review article aims to provide a clear understanding of the state-of-the-art ML models implemented for GWL modeling and the milestones achieved in this domain. The review includes all of the types of ML models employed for GWL modeling from 2008 to 2020 (138 articles) and summarizes the details of the reviewed papers, including the types of models, data span, time scale, input and output parameters, performance criteria used, and the best models identified. Furthermore, recommendations for possible future research directions to improve the accuracy of GWL prediction models and enhance the related knowledge are outlined. [ABSTRACT FROM AUTHOR]