Modeling unsaturated hydraulic conductivity by hybrid soft computing techniques

Performance analysis and modelling of circular jets aeration in an open channel using soft computing techniques

Dissolved oxygen (DO) is an important parameter in assessing water quality. The reduction in DO concentration is the result of eutrophication, which degrades the quality of water. Aeration is the best way to enhance the DO concentration. In the current study, the aeration efficiency (E20) of various numbers of circular jets in an open channel was experimentally investigated for different channel angle of inclination (θ), discharge (Q), number of jets (Jn), Froude number (Fr), and hydraulic radius of each jet (HRJn). The statistical results show that jets from 8 to 64 significantly provide aeration in the open channel. The aeration efficiency and input parameters are modelled into a linear relationship. Additionally, utilizing WEKA software, three soft computing models for predicting aeration efficiency were created with Artificial Neural Network (ANN), M5P, and Random Forest (RF). Performance evaluation results and box plot have shown that ANN is the outperforming model with correlation coefficient (CC) = 0.9823, mean absolute error (MAE) = 0.0098, and root mean square error (RMSE) = 0.0123 during the testing stage. In order to assess the influence of different input factors on the E20 of jets, a sensitivity analysis was conducted using the most effective model, i.e., ANN. The sensitivity analysis results indicate that the angle of inclination is the most influential input variable in predicting E20, followed by discharge and the number of jets.

Vertical and Horizontal Water Penetration Velocity Modeling in Nonhomogenous Soil Using Fast Multi-Output Relevance Vector Regression

A joint determination of horizontal and vertical movement of water through porous medium is addressed in this study through fast multi-output relevance vector regression (FMRVR). To do this, an experimental data set conducted in a sand box with 300 × 300 × 150 mm dimensions made of Plexiglas is used. A random mixture of sand having size of 0.5-1 mm is used to simulate the porous medium. Within the experiments, 2, 3, 7, and 12 cm walls are used together with different injection locations as 130.7, 91.3, and 51.8 mm measured from the cutoff wall at the upstream. Then, the Cartesian coordinated of the tracer, time interval, length of the wall in each setup, and two dummy variables for determination of the initial point are considered as independent variables for joint estimation of horizontal and vertical velocity of water movement in the porous medium. Alternatively, the multi-linear regression, random forest, and the support vector regression approaches are used to alternate the results obtained by the FMRVR method. It was concluded that the FMRVR outperforms the other models, while the uncertainty in estimation of horizontal penetration is larger than the vertical one.

Pre- and post-dam river water temperature alteration prediction using advanced machine learning models

Dams significantly impact river hydrology by changing the timing, size, and frequency of low and high flows, resulting in a hydrologic regime that differs significantly from the natural flow regime before the impoundment. For precise planning and judicious use of available water resources for agricultural operations and aquatic habitats, it is critical to assess the dam water's temperature accurately. The building of dams, particularly several dams in rivers, can significantly impact downstream water. In this study, we predict the daily water temperature of the Yangtze River at Cuntan. Thus, this work reveals the potential of machine learning models, namely, M5 Pruned (M5P), Random Forest (RF), Random Subspace (RSS), and Reduced Error Pruning Tree (REPTree). The best and effective input variables combinations were determined based on the correlation coefficient. The outputs of the various machine learning algorithm models were compared with recorded daily water temperature data using goodness-of-fit criteria and graphical analysis to arrive at a final comparison. Based on a number of criteria, numerical comparison between the models revealed that M5P model performed superior (R² = 0.9920, 0.9708; PCC = 0.9960, 0.9853; MAE = 0.2387, 0.4285; RMSE = 0.3449, 0.4285; RAE = 6.2573, 11.5439; RRSE = 8.0288, 13.8282) in pre-impact and post-impact spam, respectively. These findings suggest that a huge wave of dam construction in the previous century altered the hydrologic regimes of large and minor rivers. This study will be helpful for the ecologists and river experts in planning new reservoirs to maintain the flows and minimize the water temperature concerning spillway operation. Finally, our findings revealed that these algorithms could reliably estimate water temperature using a day lag time input in water level. They are cost-effective techniques for forecasting purposes.

Factor Analysis and Regression Prediction Model of Swimmers' Performance Structure Based on Mixed Genetic Neural Network

With the development of economy, people put forward higher demands on material life and spiritual life, and sports have become an indispensable part of daily life. Ten km freestyle, although it has been started late, is loved by people all over the world, and it is getting more and more attention in the world of competitive sports. Through the theoretical research of 10 km freestyle, it can be seen that most of the research on this project is focused on the training, selection of materials, and how to improve the technique of crossing obstacles or pools, while the research on the performance of 10 km freestyle is limited to the analysis of the development trend of the performance and no research on the prediction of the actual performance, which to some extent restricts the scientific development of 10 km freestyle. The efficiency of a suitable learning is a factor that influences whether the training and learning of a BP neural network is stable or not. It can also directly determine the choice of weights. When the learning rate is chosen to be large, it increases the speed of learning and makes the stability of the network change. This has limited the scientific development of 10 km freestyle to a certain extent, and it is also a good entry point for the research of 10 km freestyle. BP neural network is one of the most widely used neural network models, which can be used for classification, clustering, prediction, and other related problems. In this study, we propose a comprehensive evaluation method of 10 km freestyle performance based on BP neural network and try to establish a neural network evaluation model by combining the athletes' physiological and biochemical indexes and sports performance. In this study, the normalized index values are used as the network input, and the athletes' performance in 10 km freestyle is used as the network output to predict the athletes' performance in 10 km freestyle. This study provides a theoretical basis for adjusting the condition of 10 km freestyle athletes and improving their performance. Optimize the reasonable allocation of resources between coaches and athletes, and increase the opportunities for coaches and athletes to communicate and learn from outside.

Simulation of time-series groundwater parameters using a hybrid metaheuristic neuro-fuzzy model

The estimation of qualitative and quantitative groundwater parameters is an essential task. In this regard, artificial intelligence (AI) techniques are extensively utilized as accurate, trustworthy, and cost-effective tools. In the present paper, two hybrid neuro-fuzzy models are implemented for the prediction of groundwater level (GWL) fluctuations, as well as variations of Cl - and HCO3 - in the Karnachi well, Kermanshah, Iran in monthly intervals within a 13-year period from 2005 to 2018. In order to develop AI models, the adaptive neuro-fuzzy inference system (ANFIS), firefly algorithm (FA), and wavelet transform (WT) are used. In other words, two hybrid models including ANFIS-FA (adaptive neuro-fuzzy inference system-firefly algorithm) and WANFIS-FA (wavelet-adaptive neuro-fuzzy inference system-firefly algorithm) are utilized for the estimation of the quantitative and qualitative parameters. Firstly, influencing lags of the time-series of the qualitative and quantitative parameters are identified using the autocorrelation function. Then, four and eight separate models are developed for the approximation of GWLs and qualitative parameters (i.e. Cl - and HCO3 -), respectively. It is worth to mention that about 75% of observed values are assigned to train the hybrid AI models, while the rest (i.e. 25%) to test them. Sensitivity analysis results reveal that the WANFIS-FA models display more acceptable performance than the ANFIS-FA ones. Also, the estimations of MAE, NSC, and SI for the simulation of HCO3 - by the superior model of the WANFIS-FA are obtained to be 0.040, 0.988, and 0.022, respectively. In addition, the lags (t-1), (t-2), (t-3), and (t-4) are ascertained as the most effective time-series lags for the estimation of Cl - .

Assessment of Contamination Management Caused by Copper and Zinc Cations Leaching and Their Impact on the Hydraulic Properties of a Sandy and a Loamy Clay Soil

Soil hydraulic properties are crucial to agriculture and water management and depend on soil structure. The impact of Cu and Zn cations on the hydraulic properties of sandy and loamy clay soil samples of Central Greece, was investigated in the present study. Metal solutions with increased concentrations were used to contaminate the soil samples and the effect on hydraulic properties was evaluated, demonstrating the innovation of the current study. The soil samples were packed separately into transparent columns and the initial values of hydraulic conductivity, cumulative infiltration, infiltration rate and sorptivity were estimated. In order to evaluate soil adsorption, metal concentrations were measured at the water leachate. After the contamination of the soil samples, the hydraulic properties under investigation were determined again, using distilled water as the incoming fluid; the differences at the hydraulic parameters were observed. After doubling metal concentrations into the incoming solution of loamy clay soil, metal adsorption and the values of the hydraulic parameters increased significantly. Loamy clay soil showed interaction between the clay particles and the positive charge in the incoming fluid, which led to a possible increase in aggregation. Furthermore, aggregation may led to pore generation. Contamination of sandy soil exhibited no impact on aggregation and soil structure. In order to evaluate the differences on the hydraulic properties and soil structure, the experimental points were approximated with two infiltration models.

Daily Streamflow Forecasting Based on the Hybrid Particle Swarm Optimization and Long Short-Term Memory Model in the Orontes Basin

Water, a renewable but limited resource, is vital for all living creatures. Increasing demand makes the sustainability of water resources crucial. River flow management, one of the key drivers of sustainability, will be vital to protect communities from the worst impacts on the environment. Modelling and estimating river flow in the hydrological process is crucial in terms of effective planning, management, and sustainable use of water resources. Therefore, in this study, a hybrid approach integrating long short-term memory networks (LSTM) and particle swarm algorithm (PSO) was proposed. For this purpose, three hydrological stations were utilized in the study along the Orontes River basin, Karasu, Demirköprü, and Samandağ, respectively. The timespan of Demirköprü and Karasu stations in the study was between 2010 and 2019. Samandağ station data were from 2009–2018. The datasets consisted of daily flow values. In order to validate the performance of the model, the first 80% of the data were used for training, and the remaining 20% were used for the testing of the three FMSs. Statistical methods such as linear regression and the more classical model autoregressive integrated moving average (ARIMA) were used during the comparison process to assess the proposed method’s performance and demonstrate its superior predictive ability. The estimation results of the models were evaluated with RMSE, MAE, MAPE, SD, and R2 statistical metrics. The comparison of daily streamflow predictions results revealed that the PSO-LSTM model provided promising accuracy results and presented higher performance compared with the benchmark and linear regression models.