Evapotranspiration (ET) has a significant role in various natural and human systems, such as water cycle balance, climate regulation, ecosystem health, agriculture, hydrological cycle, water resource management, and climate studies. Among various approaches that are employed for estimating ET, the Penman–Monteith equation is known as the widely accepted reference approach. However, the extensive data requirement of this method is a crucial challenge that limits its usage, particularly in data-scarce regions. Therefore, as an alternative approach, artificial intelligence (AI) models have gained prominence for estimating evapotranspiration because of their capacity to handle complicated relationships between meteorological variables and water loss processes. These models leverage large datasets and advanced algorithms to provide accurate and timely ET predictions. The current research aims to review previous studies addressing the application of the AI model in ET modeling under four main categories: neuron-based, tree-based, kernel-based, and hybrid models. The results of this study indicated that traditional models like the Penman–Monteith (PM) require extensive input data, while AI-based approaches offer promising alternatives due to their ability to model complex nonlinear relationships. Despite their potential, AI models face challenges such as overfitting, interpretability, inconsistent input variable selection, and lack of integration with physical ET processes, highlighting the need for standardized input configurations, better pre-processing techniques, and incorporation of hydrological and remote sensing data.

Ogee spillway is one of the most common types of the spillway. Researchers have attempted to investigate the hydraulics of ogee structure under hydraulic heads near the design head. Herein, an ogee-crested spillway is studied at heads significantly greater than the design head. The efforts are undertaken to study the hydrodynamic field under high head ratio conditions by conducting a numerical simulation using OpenFOAM with five turbulence closures including standard k–ε, realizable k–ε, RNG k–ε, k–ω SST and LRR. The comparisons of flow parameters under different head ratios with the experimental data demonstrated that the LRR model had the best performance, which shows its strength in cases dealing with flow separation or significant streamline curvature. It is found that with increasing hydraulic head, up to seven times that of the design head, the flow separation zone grows linearly. Discharge coefficients are studied for a wide range of head ratios. It is concluded that increasing head ratio up to five leads to an increase in the discharge coefficient due to decreasing pressure on the ogee crest. As head ratio increases to greater values, the discharge coefficient drops suddenly due to some changes in the pressure field.

Soil moisture (SM) is crucial for sustainable applications in agriculture, meteorology, and hydrology. While direct measurement provides superior accuracy, it is unfeasible when applied over extensive geographical areas because of its costly and time-intensive nature. On the other hand, parameterization, complexity, and assumptions used in empirical and physical models lead to challenging SM estimations using these models. By handling extensive datasets and identifying complex connections within the data, the machine-learning (ML) approach has become an attractive solution to address the aforementioned limitations. This approach can estimate SM by effectively capturing the complex relationships among environmental variables and soil moisture data. Although the ML approach is a powerful tool for estimating SM, it has several limitations, such as data dependency, scalability, and high dimensionality. This paper aims to present an overview of ML methods used for modeling SM while also discussing their challenges and notable achievements within this field. These models vary in suitability depending on data availability and context. DL models excel in capturing spatiotemporal complexity but require abundant data. SVMs are robust in noisy or sparse datasets, and hybrid models offer improved flexibility and predictive accuracy. Incorporating remote sensing, satellite data, and hybrid physical-AI frameworks can further enhance performance. However, the opaque “black-box” nature of ML remains a barrier to trust and operational use, emphasizing the need for explainable AI (XAI) to improve transparency. The findings underscored the importance of prioritizing the transferability of AI-based models across varied environmental conditions to ensure scalable and dependable soil moisture monitoring.

Soil temperature is an essential factor for agricultural, meteorological, and hydrological applications. Direct measurement, despite its high accuracy, is impractical on a large spatial scale due to the expensive and time-consuming process. On the other hand, the complex interaction between variables affecting soil temperature, such as topography and soil properties, leads to challenging estimation processes by empirical methods and physical models. Machine learning (ML) approaches gained considerable attention due to their ability to address the limitations of empirical and physical methods. These approaches are capable of estimating the variables of interest using complex nonlinear relationships with no assumptions about data distribution. However, their sensitivity to input data as well as the need for a large amount of training ground truth data limits the application of machine learning approaches. The current paper aimed to provide a review of ML techniques implemented for soil temperature modeling, their challenges, and milestones achieved in this domain.

Soil temperature is a critical parameter in soil science, agriculture, meteorology, hydrology, and water resources engineering, and its accurate and cost-effective determination and prediction are very important. Machine learning models are widely employed for surface, near-surface, and subsurface soil temperature predictions. The present study employed a properly designed one-dimensional convolutional neural network model to predict the hourly soil temperature at a subsurface depth of 0–7 cm. The annual input dataset for this model included eight hourly climatic features. The performance of this model was assessed using a wide range of evaluation metrics and compared to that of a multilayer perceptron model. A detailed sensitivity analysis was conducted on each feature to determine its importance in predicting the soil temperature. This analysis showed that air temperature had the greatest impact and surface thermal radiation had the least impact on soil temperature prediction. It was concluded that the one-dimensional convolutional model performed better than the multilayer perceptron model in predicting the soil temperature under both normal and hot weather conditions. The findings of this study demonstrated the capability of the model to predict the daily maximum soil temperature.

Accurate soil temperature prediction in cold climates is crucial for optimizing agricultural practices, hydrological processes, water resource management, minimizing frost damage, and mitigating flood risks. The capacity of deep learning methods to capture intricate patterns and relationships in climate data enhances the accuracy of soil temperature predictions and offers substantial benefits for reducing climate change impacts. In the present study, a comparative analysis of different deep learning techniques, including long short-term memory (LSTM), convolutional neural network (CNN), and multi-layer perceptron (MLP), for predicting the soil temperature is provided. The study examined cold climate areas across Canada, from snowy regions to Arctic conditions. Input datasets were considered both as time series and shuffled order. To comprehensively evaluate the predictive approaches for soil temperature, four machine learning (ML) models—CNN, LSTM, MLP in time series, and MLP on shuffled data—were employed. The results showed ML models using input data as time series have struggled with accurate soil temperature prediction, especially in very cold and polar climates, likely due to the presence of ice layers on the soil, limiting fluctuations near the freezing point. The normalized RMSE (NRMSE) for the CNN, LSTM, and MLP was calculated to be 8.6%. 7.4%, and 6.9%, respectively, and the scatter index (SI) for CNN, LSTM, and MLP was calculated to be 1.0%, 0.9%, and 0.9%, respectively. On the other hand, MLP-shuffled that employs shuffled input data outperformed others with an NRMSE of 5.4% and an SI of 0.7%, by creating a generalized data representation, free from presentation sequence bias. This study showed that predicting soil temperature in very cold climates poses a challenge for machine learning, yet the MLP-shuffled model excels, attaining superior accuracy through the creation of a generalized data representation independent of the sample sequence.