Prediction Of Infiltration Rate Research Articles

A Comparative Analysis of Data-Driven Empirical and Artificial Intelligence Models for Estimating Infiltration Rates

Infiltration is a vital phenomenon in the water cycle, and consequently, estimation of infiltration rate is important for many hydrologic studies. In the present paper, different data-driven models including Multiple Linear Regression (MLR), Generalized Reduced Gradient (GRG), two Artificial Intelligence (AI) techniques (Artificial Neural Network (ANN) and Multigene Genetic Programming (MGGP)), and the hybrid MGGP-GRG have been applied to estimate the infiltration rates. The estimated infiltration rates were compared with those obtained by empirical infiltration models (Horton’s model, Philip’s model, and modified Kostiakov’s model) for the published infiltration data. Among the conventional models considered, Philip’s model provided the best estimates of infiltration rate. It was observed that the application of the hybrid MGGP-GRG model and MGGP improved the estimates of infiltration rates as compared to conventional infiltration model, while ANN provided the best prediction of infiltration rates. To be more specific, the application of ANN and the hybrid MGGP-GRG reduced the sum of square of errors by 97.86% and 81.53%, respectively. Finally, based on the comparative analysis, implementation of AI-based models, as a more accurate alternative, is suggested for estimating infiltration rates in hydrological models.

Predicting micro-catchment infiltration dynamics

Rainfall and concentrated flow experiments were carried out on seven micro-catchments (MCs) that were designed to limit soil erosion and allow for water-harvesting. Prediction of infiltration rates within MCs is necessary to design effective hillslope-scale restoration projects. Continuous stage measurements and 3-D models of MC geometry were used to calculate infiltration rates from field experiments. Soil samples and Guelph permeameter (GP) measurements were collected to parameterize a predictive infiltration model in Hydrus 2D/3D. The model result of water velocity into the soil profile was averaged by depth intervals and multiplied by the corresponding MC surface area to calculate a volumetric flow rate. Four parameterizations of changes in conductivity with depth were evaluated within the model framework to determine which would best account for spatial heterogeneity. Use of the maximum field-measured conductivity provided the least biased results, with average error between simulated and measured values across all sites of less than 1%. Model results illustrate the limitations associated with particle-size distribution or GP measurements when used to predict infiltration rates in a numerical model. GP measurements with single ponded heights allowed convenient field measurement of conductivity that worked better than predictions from soil texture. The maximum of several GP samples was more representative of MC infiltration than the mean, so a higher percentile value from a distribution of MC measurements may help to account for complex infiltration processes that are not included in numerical models. This modeling approach will allow testing of process-based hypotheses about rangeland infiltration dynamics, and the development of optimal configurations of MCs at sites being considered for rangeland restoration.

Modified models for better prediction of infiltration rates in trapezoidal permeable stormwater channels

ABSTRACTIn stormwater management, it is important to accurately quantify the infiltration rates to solve urban runoff-related problems. This study proposes a method to improve estimates of the infiltration rate in permeable stormwater channels. As part of the analysis, five infiltration models were evaluated: the Kostiakov, Horton, modified Kostiakov, Philip and SCS (Soil Conservation Service) models. Infiltration tests with various initial water levels were performed on channel models with differing base width and side slopes. The results show that the addition of three parameters that describe the trapezoidal cross-sectional area, i.e. the depth, side slope and base width, in the infiltration models yielded better estimates of the infiltration rate. A comparison of the infiltration capacity values obtained from the models after the three parameters were added with those that were experimentally obtained, shows that the improved modified Kostiakov model is the most suitable model to predict infiltration rates in trapezoidal permeable stormwater channels.

Study on infiltration rate based on primary particle size distribution data in arid and semiarid region soils

Soil particle size distribution (PSD) is used to estimate some soil processes, soil moisture characteristics, and infiltration rate (IR). Prediction of infiltration rate from soil texture data requires an accurate characterization of PSD. The objective of this study was to determine more important primary particle diameters that control IR. The experiments were conducted using double-ring method with constant head of 5 cm in 15 different soils and three replications. The range of measured IR for studied soils varied from 1.6 to 30.66 cm h−1. The results indicated that the primary PSD had a significant influence on IR. In other words, most D n fractions had significant positive effect on the final IR. Among different fractions, D 30, D 40, and D 60 showed higher relationships with IR than the others. These diameters are attributed to particles with diameter of 0.05, 0.08, and 0.16 mm, respectively. The results also showed that increasing the percent of sand have intensified influence on increasing the final IR. Reversely, clay and silt contents showed negative effects on final IR. Furthermore, the CaCO3 had a meaningful effect on the IR that showed the importance of lime in arid and semiarid regions. Finally, it is revealed that the role of texture was important, especially in behavior of infiltration, runoff, and production capability.

Prediction of infiltration rate and the effect on energy use for ice rinks in hot and humid climates

Infiltration of hot humid air into an ice rink situated in subtropical region was investigated. A recreation ice rink in Taipei was used as the building model and measurements of temperature, humidity, and pressure difference were performed for different outdoor air conditions. Sherman infiltration model was used to investigate building stack and wind effects. Altogether eight cases including four points on IIHF (International Ice Hockey Federation) fog lines were studied for optimal energy operations. Cooling and dehumidification characteristics due to air infiltration were investigated. Annual analysis on air infiltration and building energy was performed for monthly average weather conditions. Stack effect was found to be the most important factor of infiltration. The effects of seasonal temperatures could result in five times difference of infiltration rates. Wind effects were found to affect the peak infiltration rate more than 50%. The air change rate was found to be as high as 0.6 ACH due to stack and wind effects. The moisture infiltration peaked in summer season and can be more than several times of that in winter. It was also found in the investigation that the total heat load due to infiltration can be as high as 13,540 GJ/year for the ice rink studied. Operating conditions were found to reduce the total annual heat load by more than 50% under the IIHF fog lines. The results of this study will contribute to the technical literature in the design of year round ice rinks in tropical and subtropical regions.

Infiltration of fibrous preforms by a pure metal: Part V. Influence of preform compressibility

Infiltration by a pure matrix in the presence of preform deformation and partial matrix solidification is analyzed using a bounding approach for the preform rheology where solid metal is present. It is found, using parameters for the infiltration of short alumina fiber preforms by aluminum, that the two bounds are close in comparison to other factors of uncertainty in the prediction of infiltration rate. Using this approach, preform compression is shown to exert a significant influence on the infiltration rate for the system explored; in particular, the analysis shows the existence of an optimal value of applied pressure. Simplifications in the analysis are also presented, which yield fairly accurate results while easing their computation significantly.

Temperature- and wind-induced air flow patterns in a staircase. Computer modelling and experimental verification

The typical infiltration load for a residential building has been found to range from one-third to one-half of the total space conditioning load. However, most infiltration measurements have been made on single-family houses. Information about the role of infiltration in the energy consumption of large buildings is limited. Furthermore, the prediction of infiltration rates in high-rise buildings is a complex problem. The forces that drive this flow result from the superposition of wind pressure on the faces of the building and the stack effect across the height of the building. Infiltration models have shown the latter effect to be significant in single-family residences, particular in colder climates and, consequently, the stack effect is even greater in high-rise buildings. For this work, we performed tracer gas and fan pressurization measurements on a 30 m tall University of California dormitory in order to determine the importance of both wind and stack effect upon infiltration. Measured pressure and tracer gas distributions were compared with those from a predictive infiltration computer model for high-rise buildings. To study the influence of the air flow pattern around the building, this model uses various wind velocity profiles characteristic of urban areas and different sets of surface pressure coefficients derived from wind tunnel experiments.

Pore size distribution — A factor to be considered in infiltration studies?

Pore size distribution (PSD) is often mentioned as an important factor controlling the rate of infiltration into a soil, but surprisingly when it comes to quantitative assessment or prediction of infiltration rates, PSD is rarely considered. Four possible reasons are suggested to explain this paradox: (1) the difficulties of isolating PSD from the entanglement of variables that control infiltration rates; (2) the variability of PSD through time; (3) measurement problems; and (4) the difficulty of expressing PSD as a single-term index which is suitable for any subsequent analysis. An attempt is made to construct a suitable index of pore size distribution, and then to include this in a path analysis study to examine the variable structure between infiltration, PSD, and a number of additional explanatory variables.