Predicting soil infiltration rate using Artificial Neural Network

Ekhmaj, Ahmed Ibrahim

doi:10.1109/iceea.2010.5596107

Cited by 10 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Infiltration rate is a critical component of the hydrological cycle. Along with precipitation, soil infiltration rate affects plant water availability, runoff, and reservoir and groundwater supply [16]. Data to estimate soil parameters using readily available data, such as textural soil qualities (i.e., particle-size distribution and porosity) are represented by the Pedotransfer functions (PTFs) method, which can be applied at the local scale with point textural qualities or at the watershed scale with aggregated textural information [82].…”

Section: To Predict Soil Hydraulic Conductivity and Soil Infiltration...mentioning

confidence: 99%

See 1 more Smart Citation

Application of Artificial Neural Networks in Soil Science Research

Keerthana

2024

ACRI

View full text Add to dashboard Cite

Artificial Neural Networks utilize high-performance computation and large data technology, allowing research to generate new prospects in agriculture. ANN is currently a preferred technique for crop yield prediction, forecasting, and classification in biological science domains. Among different agriculture fields, soil science research plays a vital role in understanding and managing the complex processes occurring within the soil environment as oil is a complex system with dynamic surface layers that differ from the other parts of the matrix. Due to the increasing accessibility of innovative computing techniques, Artificial Neural Networks (ANNs) have developed into useful tools for modeling and forecasting soil-related activities. The numerous applications of ANNs in soil science research, with a focus on how well they can classify soils, assess soil fertility, forecast soil erosion, and estimate soil moisture. They are vital tools for identifying soil types, evaluating fertility levels, predicting erosion, and soil moisture estimation. ANN models were effective at predicting soil characteristics like pH, organic carbon concentration, and clay content. By training on vast datasets that contain the chemical, biological, and physical properties of soil, ANNs are able to accurately predict different soil types and enable land-use planning, precision farming, and environmental management. This mini-review focuses on ANN approaches that possess the potential to increase our understanding of soil science and encourage informed decisions for soil management and conservation.

show abstract

Section: To Predict Soil Hydraulic Conductivity and Soil Infiltration...mentioning

confidence: 99%

“…Artificial neural networks (ANN) are a recent way to fitting PTFs [84]. ANN model is a more realistic and reliable since it does not predict negative steady infiltration rate values [16].…”

Section: To Predict Soil Hydraulic Conductivity and Soil Infiltration...mentioning

confidence: 99%

Application of Artificial Neural Networks in Soil Science Research

Keerthana

2024

ACRI

View full text Add to dashboard Cite

show abstract

“…Several types of research have been conducted using ANNs to estimate evapotranspiration as a function of climatic elements (Kumar et al, 2002), (Sudheer et al, 2003), (Odhiambo et al, 2001), (Trajkovic et al, 2003), (Achite et al, 2022), (Genaidy, 2020), (Heramb et al, 2023), (Rajput et al, 2023), (Abdel-Fattah et al, 2023), (Tunalı et al, 2023), (Ekhmaj, 2012) and (Ekhmaj et al, 2013). These researches found satisfactory results, compared with those obtained from the FPM method.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Artificial Neural Networks for Estimating Refer-ence Evapotranspiration in Shahat, Libya using limited climatic data

Momen,

Abdelatty

2024

mjbs

View full text Add to dashboard Cite

This study was conducted with the aim of evaluating the performance of artificial neural networks (ANNs) to estimate the reference evapotranspiration using limited climate data in Shahat region in Libya, compared to using the FAO Penman-Monteith equation (FPM), which requires temperature, wind speed, relative humidity and number of sunshine hours, which are rarely available in most meteorological stations in developing countries. In this study, we used the average temperature (Tmean) and the average relative humidity (RHmean) obtained from Shahat meteorological station for the period from 1963 to 1999, and the extraterrestrial radiation (Ra), which can be calculated given the location and time of the day. These data are divided into two groups, from 1963 to 1988 and from 1989 to 1999 for the training and validation phases of the neural networks, respectively. This study concluded that using (Tmean), (RHmean) and (Ra) gave the best agreement with the results calculated with the FAO Penman-Monteith equation, where the values of R2 and RMSE are equal to 0.98 and 0.26, respectively.

show abstract

“…They reported ANFIS as a powerful estimation tool relative to ANN and MLR. Ekhmaj (2010) developed MLR and ANN models in order to predict the steady infiltration rate, and the outcomes yielded better predictions with ANN model relative to MLR. Elbisy (2015) implemented genetic algorithm in order to determine the optimum SVM parameters and investigated the performance of three kernel functions (linear, radial basis and sigmoid) in determining field hydraulic conductivity of sandy soil having easily measurable soil parameters as input variables.…”

Section: Introductionmentioning

confidence: 99%

Random forest, M5P and regression analysis to estimate the field unsaturated hydraulic conductivity

2019

View full text Add to dashboard Cite

Hydraulic conductivity of soil reveals its influencing role in the studies related to management of surface and subsurface flow, e.g. irrigation and drainage projects, and solute mass transport models. Direct measurements of hydraulic conductivity have many difficulties due to spatial variation of the property in the field. Pertaining to this problem, in this study, estimation models have been developed using machine learning methods (M5 tree model and random forest model) in an attempt to estimate the accurate values of unsaturated hydraulic conductivity related to basic soil properties (clay, silt and sand content, bulk density and moisture content). Data set was collected from the experimental measurements of cumulative infiltration using mini disc infiltrometer at the study area (Kurukshetra, India). A multivariate nonlinear regression (MNLR) relationship was derived, and the performance of this model was compared with the machine learning-based models. The evaluation of the results, based on statistical criteria (R 2 , RMSE, MAE), suggested that random forest regression model is superior in accurate estimations of the unsaturated hydraulic conductivity of field data relative to M5 model tree and MNLR.

show abstract

Predicting soil infiltration rate using Artificial Neural Network

Cited by 10 publications

References 11 publications

Application of Artificial Neural Networks in Soil Science Research

Application of Artificial Neural Networks in Soil Science Research

Performance Evaluation of Artificial Neural Networks for Estimating Refer-ence Evapotranspiration in Shahat, Libya using limited climatic data

Random forest, M5P and regression analysis to estimate the field unsaturated hydraulic conductivity

Contact Info

Product

Resources

About