Predicting daily soil temperature at multiple depths using hybrid machine learning models for a semi-arid region in Punjab, India

Automatic Quality Assessment of Pork Belly via Deep Learning and Ultrasound Imaging

Pork belly, prized for its unique flavor and texture, is often overlooked in breeding programs that prioritize lean meat production. The quality of pork belly is determined by the number and distribution of muscle and fat layers. This study aimed to assess the number of pork belly layers using deep learning techniques. Initially, semantic segmentation was considered, but the intersection over union (IoU) scores for the segmented parts were below 70%, which is insufficient for practical application. Consequently, the focus shifted to image classification methods. Based on the number of fat and muscle layers, a dataset was categorized into three groups: three layers (n = 1811), five layers (n = 1294), and seven layers (n = 879). Drawing upon established model architectures, the initial model was refined for the task of learning and predicting layer traits from B-ultrasound images of pork belly. After a thorough evaluation of various performance metrics, the ResNet18 model emerged as the most effective, achieving a remarkable training set accuracy of 99.99% and a validation set accuracy of 96.22%, with corresponding loss values of 0.1478 and 0.1976. The robustness of the model was confirmed through three interpretable analysis methods, including grad-CAM, ensuring its reliability. Furthermore, the model was successfully deployed in a local setting to process B-ultrasound video frames in real time, consistently identifying the pork belly layer count with a confidence level exceeding 70%. By employing a scoring system with 100 points as the threshold, the number of pork belly layers in vivo was categorized into superior and inferior grades. This innovative system offers immediate decision-making support for breeding determinations and presents a highly efficient and precise method for assessment of pork belly layers.

Approximation in scour depth around spur dikes using novel hybrid ensemble data-driven model

The scouring process near spur dikes poses a threat to riverbank stability, making it crucial for river engineering to accurately calculate the maximum scour depth. However, determining the maximum scour depth has been challenging due to the intricacy of scour phenomena surrounding these structures. This research introduces a reliable ensemble data-driven model by hybridizing random tree (RT) using additive regression (AR), bagging (B), and random subspace (RSS) for predicting scour depths around spur dikes. A database of 154 experimental observations was collected from literature, with 103 and 51 observations used for training and testing subsets, respectively. A dimensionless analysis was performed on the collected dataset, selecting four variables as input variables (v/vs, y/l, l/d50, and Fd50) and ds/l as response variables. The performance comparison demonstrates that B_AR_RT has a better coefficient of determination (R2) of 0.9693, root mean square error (RMSE) of 0.1305, and Nash-Sutcliffe efficiency (NSE) of 0.9692. Finally, a comparison of the best hybrid model has been done with previous studies, and sensitivity analysis is performed to determine the most influential parameter for predicting the scour depth around spur dikes.

Development of a robust daily soil temperature estimation in semi-arid continental climate using meteorological predictors based on computational intelligent paradigms

Changes in soil temperature (ST) play an important role in the main mechanisms within the soil, including biological and chemical activities. For instance, they affect the microbial community composition, the speed at which soil organic matter breaks down and becomes minerals. Moreover, the growth and physiological activity of plants are directly influenced by the ST. Additionally, ST indirectly affects plant growth by influencing the accessibility of nutrients in the soil. Therefore, designing an efficient tool for ST estimating at different depths is useful for soil studies by considering meteorological parameters as input parameters, maximal air temperature, minimal air temperature, maximal air relative humidity, minimal air relative humidity, precipitation, and wind speed. This investigation employed various statistical metrics to evaluate the efficacy of the implemented models. These metrics encompassed the correlation coefficient (r), root mean square error (RMSE), Nash-Sutcliffe (NS) efficiency, and mean absolute error (MAE). Hence, this study presented several artificial intelligence-based models, MLPANN, SVR, RFR, and GPR for building robust predictive tools for daily scale ST estimation at 05, 10, 20, 30, 50, and 100cm soil depths. The suggested models are evaluated at two meteorological stations (i.e., Sulaimani and Dukan) located in Kurdistan region, Iraq. Based on assessment of outcomes of this study, the suggested models exhibited exceptional predictive capabilities and comparison of the results showed that among the proposed frameworks, GPR yielded the best results for 05, 10, 20, and 100cm soil depths, with RMSE values of 1.814°C, 1.652°C, 1.773°C, and 2.891°C, respectively. Also, for 50cm soil depth, MLPANN performed the best with an RMSE of 2.289°C at Sulaimani station using the RMSE during the validation phase. Furthermore, GPR produced the most superior outcomes for 10cm, 30cm, and 50cm soil depths, with RMSE values of 1.753°C, 2.270°C, and 2.631°C, respectively. In addition, for 05cm soil depth, SVR achieved the highest level of performance with an RMSE of 1.950°C at Dukan station. The results obtained in this research confirmed that the suggested models have the potential to be effectively used as daily predictive tools at different stations and various depths.

Flood discharge prediction using improved ANFIS model combined with hybrid particle swarm optimisation and slime mould algorithm

Due to the disastrous socio-economic impacts of flood hazards and estimated rise of its occurrences in the near future, there has been an increase in the importance of flood prediction worldwide. Artificial intelligence (AI) models have contributed significantly by giving cost-effective solutions for simulating physical processes of flood events and improving accuracy in prediction over the last few decades. This paper presents a novel conjoint model to forecast river flood discharge (QFD) considering data from four gauging stations of River Brahmani, Odisha India. The developed hybridised metaheuristic algorithm, i.e. ANFIS-PSOSMA, improves exploration capability of Slime mould algorithm (SMA) by integrating it with particle swarm optimisation (PSO). Performance of novel hybrid model is assessed by utilising quantitative statistical measures like the coefficient of correlation (R2), Nash-Sutcliffe Model Efficiency (NSE), root mean square error (RMSE), and mean absolute error (MAE). The proposed hybrid ANFIS model using optimisation algorithm provided the best performance values with NSE of 0.9952, R2 of 0.9946, RMSE of 0.0485, and MAE of 0.0265 during training and NSE of 0.9736, R2 of 0.9731, RMSE of 8.4236, and MAE of 4.3197 during testing at Jenapur gauging station, indicating the prospective of utilising the developed models in forecasting flood discharge. The present study's importance lies in integrating several input parameters, and AI algorithms have been utilised for developing flood prediction model. In addition, the attained results indicated that combining the optimisation algorithms with ANFIS enhanced its performance in modelling monthly flood discharge time series.

A Comprehensive Study of Artificial Intelligence Applications for Soil Temperature Prediction in Ordinary Climate Conditions and Extremely Hot Events

Soil temperature is a fundamental parameter in water resources and irrigation engineering. A cost-effective model that can accurately forecast soil temperature is urgently needed. Recently, many studies have applied artificial intelligence (AI) at both surface and underground levels for soil temperature predictions. In the present study, attempts are made to deliver a comprehensive and detailed assessment of the performance of a wide range of AI approaches in soil temperature prediction. In this regard, thirteen approaches, from classic regressions to well-established methods of random forest and gradient boosting to more advanced AI techniques, such as multi-layer perceptron and deep learning, are taken into account. Meanwhile, great varieties of land and atmospheric variables are applied as model inputs. A sensitivity analysis was conducted on input climate variables to determine the importance of each variable in predicting soil temperature. This examination reduced the number of input variables from 8 to 7, which decreased the simulation load. Additionally, this showed that air temperature and solar radiation play the most important roles in soil temperature prediction, while precipitation can be neglected in forecast AI models. The comparison of soil temperature predicted by different AI models showed that deep learning demonstrated the best performance with R-squared of 0.980 and NRMSE of 2.237%, followed by multi-layer perceptron with R-squared of 0.980 and NRMSE of 2.266%. In addition, the performance of developed AI models was evaluated in extremely hot events since heat warnings are essential to protect lives and properties. The assessment showed that deep learning and multi-layer perceptron methods still have the best prediction. However, their R-squared decreased to 0.862 and 0.859, and NRMSE increased to 6.519% and 6.601%, respectively.