Optimizing soil water retention predictions using a subtractive clustering-based ANFIS model

The field capacity (FC) and permanent wilting point (PWP) are fundamental hydrological properties critical for assessing water availability within soils, rather than direct measures of soil health. Due to the challenges associated with their field measurement, alternative assessment methods are necessary. In this study, global-scale accessible soil data were retrieved from the world soil database called the World Soil Information Service (WoSIS), and artificial neural network (ANN) and gene-expression programming (GEP) algorithms were used to predict soil FC and PWP based on easily obtainable parameters from the database. The best-fit variable combination for FC (longitude, latitude, altitude, sand content, silt content, clay content, and electrical conductivity) and PWP (best-fit FC combination plus pH) modeling was determined. Both ANN and GEP showed greater accuracy than linear-based models in simulating the FC and PWP from the best-fit variables. The mean absolute error (MAE) was reduced by 51.54% for the FC and 56.38% for the PWP by the ANN model, compared with the linear model used in the previous literature. The normalized root mean square error (NRMSE) evaluation indicated that the ANN model performed best for PWP prediction (NRMSE of 19.9%), while the GEP model was superior for FC prediction (NRMSE of 29.9%). Between the ANN and GEP models, the ANN model showed a slightly higher model of interpretability; however, the GEP model exhibited a similar or better ability to avoid large error, based on the error distribution. Overall, our results demonstrated that machine learning is effective in predicting the FC and PWP from easily accessible data from WoSIS, and the GEP model is more preferable for FC and PWP modeling.

This study aimed to predict some soil water contents and soil erodibility indices with a multilayer perceptron (MLP) artificial neural network (ANN) using remote sensing data (Landsat 8 OLI TIRS) and topographic variables from a digital elevation model (DEM) in a semi-arid ecosystem. In models, the input variables were derived from remote sensing imaging and DEM. The output variables were field capacity, wilting point, aggregate stability index, structural stability index, dispersion ratio, and clay flocculation index. This study was realized in the watersheds of the Koruluk dam, the Kızlarkalesi, and the Telme ponds built for agricultural irrigation in Gümüşhane-Şiran. The soil samples were obtained from two depths (0-10cm and 10-20cm) from 59 soilprofiles. Besides field capacity, wilting point, and aggregate stability analysis, undispersed/dispersed sand, silt, clay contents, and organic matter analysis were performed due to their strong effect on soil moisture, soil water content, and erodibility indices. The correlation analysis results showed significant relationships between soil characteristics and soil water contents/soil erodibility indices. The remote sensing variables were derived from three Landsat images of 2015 (June, July, and September). The performance results of MLP ANN models predicted for soil water contents and erodibility indices ranged from 0.75 to 0.90 for R2, 0.046-4.115 for root mean square error (RMSE), 4.46-6.54 for normalized root mean square error (NRMSE), and 0.042-0.186 for mean absolute error (MAE). Topography was a more significant group of variables that affected soil water contents and soil erodibility indices and the feature importance of topography in the prediction was over 55%. The results showed that the use of topographic variables together with remote sensing variables in MLP ANN modeling increased the performance of the models.

Testing Some Pedo-Transfer Functions (PTFs) in Apulia Region. Evaluation on the Basis of Soil Particle Size Distribution and Organic Matter Content for Estimating Field Capacity and Wilting Point

The prediction of Field Capacity (FC) and Permanent Wilting Point (PWP) is essential for efficient soil management and agricultural sustainability, as in the Una River Watershed (URW), located in the state of São Paulo, Brazil, a region notable for its irrigated agriculture, which requires management tailored to its specific conditions. This study evaluates the efficiency of Pedotransfer Functions (PTFs) generated by Multiple Linear Regression (MLR) and Artificial Neural Networks (ANNs) in estimating FC and PWP from granulometric fractions and Organic Matter (OM) content. We analyzed 35 soil samples (0–20 cm), and the models were implemented in a Python environment. MLR stood out for its simplicity and efficiency, achieving R² values of up to 0.59 for FC and 0.87 for PWP, requiring fewer variables and lower computational costs. Meanwhile, ANNs reached R² values of up to 0.73 for FC but demanded greater computational complexity and more larger datasets. The results demonstrate that both methodologies provide adaptable solutions for estimating soil hydraulic parameters, and their integration into agricultural management systems is strategic for improving water-use efficiency and promoting sustainable practices.

ABSTRACTUsing pedotransfer functions (PTF) is a useful way for field capacity (FC) and permanent wilting point (PWP) prediction. The aim of this study was to model PTF to estimate FC and PWP using regression tree (RT) and stepwise multiple linear regressions (SMLR). For this purpose, 165 and 45 soil samples from UNSODA and HYPRES datasets were used for development and validation of new PTFs, respectively. %Clay, geometric mean diameter (dg), and bulk density (BD) were selected as predictor variables due to the highest correlation and lowest multicollinearity. The results showed that clay percentage with W* = 0.89 and dg with W* = −0.57 were the most effective variables to predict PWP and FC, respectively. The RT method had a better performance (R2 = 0.80, ME = −0.002 cm3cm−3, RMSE = 0.05 cm3cm−3 for FC and R2 = 0.85, ME = 0.003 cm3cm−3, RMSE = 0.03 cm3 cm−3 for PWP) than SMLR in estimation of FC and PWP.

Permanent wilting point plays an important role in simulating winter wheat growth under water deficit conditions

Nowadays, the load monitoring system is an important element in smart buildings to reduce energy consumption. Nonintrusive load monitoring (NILM) is utilized to determine the power consumption of each appliance in smart homes. The main problem of NILM is how to separate each appliance's power from the signal of aggregated consumption. In this regard, this paper presents a combination between particle swarm optimization (PSO) and artificial neural networks (ANNs) to identify electrical appliances for demand‐side management. ANN is applied in NILM as a load identification task, and PSO is used to train the ANN algorithm. This combination enhances the NILM technique's accuracy, which is further verified by experiments on different datasets like Reference Energy Disaggregation Dataset, UK Domestic Appliance‐Level ElectricityUK‐DALE, and Indian data for Ambient Water and electricity Sensing. The high accuracy of the proposed algorithm is verified by comparisons with state of the art methods. Compared with other approaches, the total mean absolute error has decreased from 39.3566 to 18.607. Also, the normalized root mean square error (NRMSE) method has been used to compare the measured and predicted results. The NRMSE is in the range of 1.719%–16.514%, which means perfect consistency. This demonstrates the effectiveness of the proposed approach for home energy management. Furthermore, customer behavior has been studied, considering energy costs during day hours.

Functional electrical stimulation (FES) is widely used in neurorehabilitation to improve patients’ motion ability. It has been verified to promote neural remodeling and relearning, during which FES has to produce an accurate movement to obtain a good efficacy. Therefore, many studies have focused on the relationship between FES parameters and the generated movements. However, most of the relationships have been established in static contractions, which leads to an unsatisfactory result when applied to dynamic conditions. Therefore, this study proposed a FES control strategy based on the surface electromyography (sEMG) and kinematic information during dynamic contractions. The pulse width (PW) of FES was determined by a direct transfer function (DTF) with sEMG features and joint angles as the input. The DTF was established by combing the polynomial transfer functions of sEMG and joint torque and the polynomial transfer functions of joint torque and FES. Moreover, the PW of two FES channels was set based on the muscle synergy ratio obtained through sEMG. A total of six healthy right-handed subjects were recruited in this experiment to verify the validity of the strategy. The PW of FES applied to the left arm was evaluated based on the sEMG of the right extensor carpi radialis (ECR) and the right wrist angle. The coefficient of determination (R2) and the normalized root mean square error (NRMSE) of FES-included and voluntary wrist angles and torques were used to verify the performance of the strategy. The result showed that this study achieved a high accuracy (R2 = 0.965 and NRMSE = 0.047) of joint angle and a good accuracy (R2 = 0.701 and NRMSE = 0.241) of joint torque reproduction during dynamic movements. Moreover, the DTF in real-time FES system also had a nice performance of joint angle fitting (R2 = 0.940 and NRMSE = 0.071) and joint torque fitting (R2 = 0.607 and NRMSE = 0.303). It is concluded that the proposed strategy is able to generate proper FES parameters based on sEMG and kinematic information for dynamic movement reproduction and can be used in a real-time FES system combined with bilateral movements for better rehabilitation.

Remote sensing datasets offer a unique opportunity to observe spatial and temporal trends in water quality indicators (WQIs), such as chlorophyll-a, salinity, and turbidity, across various aquatic ecosystems. In this study, we used available in situ WQI measurements (chlorophyll-a: 17, salinity: 478, and turbidity: 173) along with Landsat-8 surface reflectance data to examine the capability of empirical and machine learning (ML) models in retrieving these indicators over Matagorda Bay, Texas, between 2014 and 2023. We employed 36 empirical models to retrieve chlorophyll-a (12 models), salinity (2 models), and turbidity (22 models) and 4 ML families—deep neural network (DNN), distributed random forest, gradient boosting machine, and generalized linear model—to retrieve salinity and turbidity. We used the Nash–Sutcliffe efficiency coefficient (NSE), correlation coefficient (r), and normalized root mean square error (NRMSE) to assess the performance of empirical and ML models. The results indicate that (1) the empirical models displayed minimal effectiveness when applied over Matagorda Bay without calibration; (2) once calibrated over Matagorda Bay, the performance of the empirical models experienced significant improvements (chlorophyll-a—NRMSE: 0.91 ± 0.03, r: 0.94 ± 0.04, NSE: 0.89 ± 0.06; salinity—NRMSE: 0.24 ± 0, r: 0.24 ± 0, NSE: 0.06 ± 0; turbidity—NRMSE: 0.15 ± 0.10, r: 0.13 ± 0.09, NSE: 0.03 ± 0.03); (3) ML models outperformed calibrated empirical models when used to retrieve turbidity and salinity, and (4) the DNN family outperformed all other ML families when used to retrieve salinity (NRMSE: 0.87 ± 0.09, r: 0.49 ± 0.09, NSE: 0.23 ± 0.12) and turbidity (NRMSE: 0.63± 0.11, r: 0.79 ± 0.11, NSE: 0.60 ± 0.20). The developed approach provides a reference context, a structured framework, and valuable insights for using empirical and ML models and Landsat-8 data to retrieve WQIs over aquatic ecosystems. The modeled WQI data could be used to expand the footprint of in situ observations and improve current efforts to conserve, enhance, and restore important habitats in aquatic ecosystems.

A robust model for diagnosing water stress of winter wheat by combining UAV multispectral and thermal remote sensing

We estimate parameters from the Katz and Thompson permeability model using laboratory complex electrical conductivity (CC) and nuclear magnetic resonance (NMR) data to build permeability models parameterized with geophysical measurements. We use the Katz and Thompson model based on the characteristic hydraulic length scale, determined from mercury injection capillary pressure estimates of pore throat size, and the intrinsic formation factor, determined from multisalinity conductivity measurements, for this purpose. Two new permeability models are tested, one based on CC data and another that incorporates CC and NMR data. From measurements made on forty‐five sandstone cores collected from fifteen different formations, we evaluate how well the CC relaxation time and the NMR transverse relaxation times compare to the characteristic hydraulic length scale and how well the formation factor estimated from CC parameters compares to the intrinsic formation factor. We find: (1) the NMR transverse relaxation time models the characteristic hydraulic length scale more accurately than the CC relaxation time ( of 0.69 and 0.33 and normalized root mean square errors (NRMSE) of 0.16 and 0.21, respectively); (2) the CC estimated formation factor is well correlated with the intrinsic formation factor (NRMSE=0.23). We demonstrate that that permeability estimates from the joint‐NMR‐CC model (NRMSE=0.13) compare favorably to estimates from the Katz and Thompson model (NRMSE=0.074). This model advances the capability of the Katz and Thompson model by employing parameters measureable in the field giving it the potential to more accurately estimate permeability using geophysical measurements than are currently possible.

Para a determinação do volume de água retido no solo e disponível às plantas, é imprescindível quantificar o teor de água do solo (TAS) na capacidade de campo (CC) e no ponto de murcha permanente (PMP). O objetivo deste trabalho foi comparar os valores do TAS na CC e no PMP determinados por distintos métodos. Utilizaram-se amostras de um Latossolo Vermelho. A CC foi obtida no campo com o método do perfil instantâneo e, no laboratório, com funis de placa porosa. O PMP foi determinado, em laboratório, como sendo o teor de água residual retida no potencial matricial de -1,5 MPa, em câmara de Richards e com medidor de potencial de ponto de orvalho WP4 PotentiaMeter. Para murcha das plantas, o TAS foi significativamente menor no método fisiológico que o determinado nas câmaras de Richards; entretanto, o teor de água do solo no PMP foi estatisticamente igual para a cultura do girassol e superior para o milho, quando comparado com aquele obtido pelo WP4. A água disponível diminuiu com o aumento na tensão para a estimativa da CC e com o método WP4, mas não diferiu do método fisiológico. Assim, o WP4 permitiu adequada estimativa do PMP e disponibilidade hídrica às plantas, em solo argiloso.

In this study, the artificial neural networks (ANNs) and regression models were used to downscale the simulated outputs of the general circulation models (GCMs). The simulated precipitation for 25.18 º N to 34.51 º N and 45 º E to 60 º E, geopotential height at 850 mb and zonal wind at 200 mb for 12.56° N to 43.25° N and 19.68° E to 61.87° E data sets as the predictors were extracted from ECHAM5 GCM for the period 1960-2005. The observed monthly precipitation data of Abadan, Abadeh, Ahwaz, Bandar Abbas, Bushehr, Shiraz and Fasa stations as the predictand were extracted for the period 1960-2005. The principal components (PCs) of the simulated data sets were extracted and then six PCs were considered as the input file of the ANN and multiple regression models. Also the combinations of the simulated data sets were used as the input file of these models. The periods 1960-2000 and 2001-2005 were considered as the train and test data in the ANN, respectively. The Pearson correlation coefficient and normalized root mean square error results indicated that ANN predicts precipitation more accurate than multiple regression. For the monthly time scale, the geopotential height is the best predictor and for the seasonal time scale (winter) the simulated precipitation is the best predictor in ANN based standardized precipitation principal components.

The Cerrado biome is a strategic region for Brazilian agriculture, and obtaining physical hydraulic properties is fundamental to understanding the dynamics of soil water and its impact on productivity. However, the lack and difficulty of obtaining such properties opens an opportunity to use pedotransfer functions (PTFs). In the context, the objective of the present study was to develop PTFs using multiple linear regression to estimate hydraulic conductivity of the saturated soil (Ks) and soil moisture at tensions of 0, 6, 10, 33, 100, and 1,500 kPa for the Cerrado biome. For this, two different predictor datasets were used. Dataset 1 consists of sand, silt, clay, bulk density, particle density, total porosity, microporosity, and macroporosity, and dataset 2 consists of the variables in dataset 1 plus soil moisture at field capacity (FC) and soil moisture at the permanent wilting point (PMP). Dataset 2 presented the best performance compared to Dataset 1 for estimating Ks and soil moisture, highlighting the importance of moisture in FC and PWP as predictors in the development of PTFs. However, the PTFs developed for Ks presented a low predictive capacity in all predictor datasets, unlike the soil moistures that presented high precision, with R² greater than 0.8 and errors close to zero.

In this research the effect of industrial sugar beet wastewater has been assessed on the soil water content properties in summer 2005. The evaluated parameters were the soil water content points such as Saturation Percent (SP), Field Capacity (FC), Permanent Wilting Point (PWP), gravitational water and Total Available Water (TAW). The pilot design was fully randomized with three replications and three treatments. The three treatments were: 1-normal water, 2-industrial sugar beet wastewater (50%) and normal water (50%) and 3-sugar beet wastewater (100%). The experiments have been carried out in the field, in 21 columns with the diameter 110 mm and the height of 400 mm. The soil was irrigated using surface irrigation method for 12 events with a constant volume and period. Based on the result, the SP, FC and PWP initial value were 46.5, 35 and 15%, respectively for all the treatments. At the end of the period, the values changed to 47, 36.6 and 17.5% for T2. They are also increased significantly to 48.5, 37 and 18.7% for T3 at the end of the period. The increasing of soil Organic Matter (OM) during the period is expected to be the main factor for this change. The result shows that although the FC and PWP parameters are increased during the period but TAW decreased significantly from the 20 to 18.5%. The other effects of wastewater on soil and leached water quality should be evaluated too.