Evaluation and development of empirical models for wetted soil fronts under drip irrigation in high-density apple crop from a point source

Vishwakarma, Dinesh Kumar; Kumar, Rohitashw; Kumar, Amit; Kushwaha, Nand Lal; Kushwaha, Kuldeep Singh; Elbeltagi, Ahmed

doi:10.1007/s00271-022-00826-7

Cited by 14 publications

(9 citation statements)

References 45 publications

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“…For PBIAS, between 0 to 10 is considered to be very good, 10 to 15 is considered to be good, 15 to 25 is considered to be fair and more than 25 is regarded as unsatisfactory or inadequate. A better and excellence model is one that has the minimum value of root mean square error (RMSE) and percent bias (PBIAS) and higher values of coefficients of correlation (CC) [41][42][43][44][45][46].…”

Section: Model Performance Evaluationmentioning

confidence: 99%

Daily Sediment Yield Prediction Using Hybrid Machine Learning Approach

Yadav

Alam²,

Suryavanshi³

2023

IJECC

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In this study, four different soft computing AI techniques were tested for the prediction of sediment yield based on hydro-meteorological variables at Jondhara station, Seonath stream in Rajnandgaon district, India. In order to fulfill this purpose, the models namely, multilayer perceptron (MLP), support vector machine (SVM), multilayer perceptron coupled with genetic algorithm (MLP-GA), and support vector machine coupled with genetic algorithm (SVM-GA) models were employed. To select the optimal input variables, a statistical method such as the Gamma test was considered among several methods. Based on the results of the analysis, all models were evaluated by using the following statistical indices: Coefficient of Correlation (CC), room mean square error (RMSE) and percent bias (PBAIS). Overall, the performance of the studied models indicates that all of them are capable of simulation sediments yield at Jondhara station, Seonath river basin in a satisfactory manner. Comparison of results showed that the MLP-GA with CC = 0.988, RMSE = 0.006 and PBIAS = 0.000 in training period and CC= 0.990, RMSE = 0.007 and PBIAS = 0.000 in testing period for S-6 model and CC = 0.986, RMSE = 0.025 and PBIAS = -0.001 in training period and CC = 0.988, RMSE = 0.029 and PBIAS = -0.001 in testing period for S-13 model were able to yield better results than the other models considered. Furthermore, an SVM model is also observed to have some advantages over MLP models and SVM-GA models since it can represent the output data in a continuous manner by fitting a linear regression function to the output data, which has the advantage of making the model more precise than MLP and SVM-GA models.

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Section: Model Performance Evaluationmentioning

confidence: 99%

Daily Sediment Yield Prediction Using Hybrid Machine Learning Approach

Yadav

Alam²,

Suryavanshi³

2023

IJECC

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“…According to previous results, the soil wetting pattern are affected by several factors including soil hydraulic properties and irrigation factors, such as soil saturated hydraulic conductivity, initial soil water content, soil textures, and emitter discharge rate (Kanda et al 2020;Vishwakarma et al 2022). Therefore, the objectives of this study were…”

Section: Introductionmentioning

confidence: 97%

Development of a soil wetting pattern estimation model for drip irrigation

Nie

Feng

2022

Water Supply

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Accurately estimating the soil wetting pattern that closely reflects the measured value can improve the water use efficiency for drip irrigation. Ignoring the effect of the initial soil water content on the soil wetting pattern affects the accuracy of the estimated results to a certain extent. This research aimed to develop a soil wetting pattern estimation model for drip irrigation that included four easily measurable parameters (i.e., initial soil water content, saturated hydraulic conductivity, total volume of applied water, and emitter discharge rate) based on dimensional analysis theory. In this study, the wetting front advance data of 12 typical soil textures were obtained in Hydras-2D/3D. The estimated values were then compared with measured or simulated wetting front advance values. For different experiments, the mean absolute error, root mean square error, and mean relative error varied from 2.77 to 4.69 cm, 6.20 to 10.61 cm, and 5.61% to 10.51%, respectively. Compared with the existing models, the proposed model was more consistent between the measured and simulated values. Therefore, the proposed model of this study is efficient and simple, which can help accurately estimate the soil wetting pattern of drip irrigation with a variety of soil textures.

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“…To improve irrigation efficiency, proper design and planning are crucial. Key design elements, such as lateral spacing, operating pressure, emitter spacing, emitter point position, and emitter discharge rates, must be established and maintained, including proper irrigation scheduling and a sound fertigation strategy 3 , 9 . The supply of water and fertilizer in the root zone must align with crop requirements to ensure optimal effectiveness 4 , 17 – 21 .…”

Section: Introductionmentioning

confidence: 99%

“…However, proper design and planning of the system can further enhance irrigation efficiency. In order to build an efficient system, various design features must be properly built (lateral spacing, operating pressure, emitter spacing, proper position of emitter point, emitter discharge rates) and maintained (e.g., irrigation scheduling, fertigation strategy) 3 , 9 , 10 . The rates and locations of water and fertilizer supplies in the root zone must match crop requirements 3 , 22 – 24 .…”

Section: Introductionmentioning

confidence: 99%

“…The wetting dimension of soil single under point source drip irrigation should be well-defined to ensure appropriate matching between these wetted soil pattern dimensions and the distance between emitters and laterals, as well as the rooting depth 32 . Several researchers emphasized the wetted dimension/shape of wetted soil, as well as the wetted bulb volume and the spatial soil moisture distribution under point source drip irrigation, which are heavily influenced by soil hydraulic characteristics (soil texture, saturated hydraulic conductivity, bulk density, and initial moisture content), emitter discharge rates, number of emitters, emitter spacing, and their interactions 8 , 9 , 33 – 46 . Field planners will benefit more from accurate data on the horizontal and vertical wetting front advance of the soil in order to design better row and plant emitter discharge and emitter spacing, as well as a design operating pressure, in order to reduce costs and increase water use efficiency, effectiveness, and productivity 35 – 37 , 45 , 47 – 49 .…”

Section: Introductionmentioning

confidence: 99%

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Modeling of soil moisture movement and wetting behavior under point-source trickle irrigation

Vishwakarma,

Kumar,

Abed

et al. 2023

Sci Rep

Self Cite

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The design and selection of ideal emitter discharge rates can be aided by accurate information regarding the wetted soil pattern under surface drip irrigation. The current field investigation was conducted in an apple orchard in SKUAST- Kashmir, Jammu and Kashmir, a Union Territory of India, during 2017–2019. The objective of the experiment was to examine the movement of moisture over time and assess the extent of wetting in both horizontal and vertical directions under point source drip irrigation with discharge rates of 2, 4, and 8 L h−1. At 30, 60, and 120 min since the beginning of irrigation, a soil pit was dug across the length of the wetted area on the surface in order to measure the wetting pattern. For measuring the soil moisture movement and wetted soil width and depth, three replicas of soil samples were collected according to the treatment and the average value were considered. As a result, 54 different experiments were conducted, resulting in the digging of pits [3 emitter discharge rates × 3 application times × 3 replications × 2 (after application and 24 after application)]. This study utilized the Drip-Irriwater model to evaluate and validate the accuracy of predictions of wetting fronts and soil moisture dynamics in both orientations. Results showed that the modeled values were very close to the actual field values, with a mean absolute error of 0.018, a mean bias error of 0.0005, a mean absolute percentage error of 7.3, a root mean square error of 0.023, a Pearson coefficient of 0.951, a coefficient of correlation of 0.918, and a Nash–Sutcliffe model efficiency coefficient of 0.887. The wetted width just after irrigation was measured at 14.65, 16.65, and 20.62 cm; 16.20, 20.25, and 23.90 cm; and 20.00, 24.50, and 28.81 cm in 2, 4, and 8 L h−1, at 30, 60, and 120 min, respectively, while the wetted depth was observed 13.10, 16.20, and 20.44 cm; 15.10, 21.50, and 26.00 cm; 19.40, 25.00, and 31.00 cm, respectively. As the flow rate from the emitter increased, the amount of moisture dissemination grew (both immediately and 24 h after irrigation). The soil moisture contents were observed 0.4300, 0.3808, 0.2298, 0.1604, and 0.1600 cm3 cm−3 just after irrigation in 2 L h−1 while 0.4300, 0.3841, 0.2385, 0.1607, and 0.1600 cm3 cm−3 were in 4 L h−1 and 0.4300, 0.3852, 0.2417, 0.1608, and 0.1600 cm3 cm−3 were in 8 L h−1 at 5, 10, 15, 20, and 25 cm soil depth in 30 min of application time. Similar distinct increments were found in 60, and 120 min of irrigation. The findings suggest that this simple model, which only requires soil, irrigation, and simulation parameters, is a valuable and practical tool for irrigation design. It provides information on soil wetting patterns and soil moisture distribution under a single emitter, which is important for effectively planning and designing a drip irrigation system. Investigating soil wetting patterns and moisture redistribution in the soil profile under point source drip irrigation helps promote efficient planning and design of a drip irrigation system.

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Evaluation and development of empirical models for wetted soil fronts under drip irrigation in high-density apple crop from a point source

Cited by 14 publications

References 45 publications

Daily Sediment Yield Prediction Using Hybrid Machine Learning Approach

Daily Sediment Yield Prediction Using Hybrid Machine Learning Approach

Development of a soil wetting pattern estimation model for drip irrigation

Modeling of soil moisture movement and wetting behavior under point-source trickle irrigation

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