Applicability of temporal stability analysis in predicting field mean of soil moisture in multiple soil depths and different seasons in an irrigated vineyard

Hao, Xinmei; Qiu, Yuan; Fan, Yaqiong; Li, Tao; Leng, Dong; Li, Sien; Kang, Shaozhong

doi:10.1016/j.jhydrol.2020.125059

Cited by 8 publications

(4 citation statements)

References 29 publications

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“…This coefficient is between 1 and −1 and if there is no relationship between the two variables, it is equal to zero [60]. These indicators are calculated based on the following formulas [61][62][63][64]:…”

Section: Validation Indicatorsmentioning

confidence: 99%

Hybridization of Differential Evolution and Adaptive-NetworkBased Fuzzy Inference System in Estimation of Compression Coefficient of Plastic Clay Soil

Nguyen¹,

Hai²,

Al‐Ansari³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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One of the important geotechnical parameters required for designing of the civil engineering structure is the compressibility of the soil. In this study, the main purpose is to develop a novel hybrid Machine Learning (ML) model (ANFIS-DE), which used Differential Evolution (DE) algorithm to optimize the predictive capability of Adaptive-Network-based Fuzzy Inference System (ANFIS), for estimating soil Compression coefficient (Cc) from other geotechnical parameters namely Water Content, Void Ratio, Specific Gravity, Liquid Limit, Plastic Limit, Clay content and Depth of Soil Samples. Validation of the predictive capability of the novel model was carried out using statistical indices: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R). In addition, two popular ML models namely Reduced Error Pruning Trees (REPTree) and Decision Stump (Dstump) were used for comparison. Results showed that the performance of the novel model ANFIS-DE is the best (R = 0.825, MAE = 0.064 and RMSE = 0.094) in comparison to other models such as REPTree (R = 0.7802, MAE = 0.068 and RMSE = 0.0988) and Dstump (R = 0.7325, MAE = 0.0785 and RMSE = 0.1036). Therefore, the ANFIS-DE model can be used as a promising tool for the correct and quick estimation of the soil Cc, which can be employed in the design and construction of civil engineering structures.

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Section: Validation Indicatorsmentioning

confidence: 99%

Hybridization of Differential Evolution and Adaptive-NetworkBased Fuzzy Inference System in Estimation of Compression Coefficient of Plastic Clay Soil

Nguyen¹,

Hai²,

Al‐Ansari³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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“…The method for estimating the error rate is the average difference between the predicted value and the actual value in all test cases [68,69]. This error is the average prediction error [70]. The formulas of the methods described below are listed as equations [71][72][73]:…”

Section: Validation Indicatorsmentioning

confidence: 99%

Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam

Hoan¹,

Dũng²,

Thu³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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Water level predictions in the river, lake and delta play an important role in flood management. Every year Mekong River delta of Vietnam is experiencing flood due to heavy monsoon rains and high tides. Land subsidence may also aggravate flooding problems in this area. Therefore, accurate predictions of water levels in this region are very important to forewarn the people and authorities for taking timely adequate remedial measures to prevent losses of life and property. There are so many methods available to predict the water levels based on historical data but nowadays Machine Learning (ML) methods are considered the best tool for accurate prediction. In this study, we have used surface water level data of 18 water level measurement stations of the Mekong River delta from 2000 to 2018 to build novel time-series Bagging based hybrid ML models namely: Bagging (RF), Bagging (SOM) and Bagging (M5P) to predict historical water levels in the study area. Performances of the Bagging-based hybrid models were compared with Reduced Error Pruning Trees (REPT), which is a benchmark ML model. The data of 19 years period was divided into 70:30 ratio for the modeling. The data of the period 1/2000 to 5/2013 (which is about 70% of total data) was used for the training and for the period 5/2013 to 12/2018 (which is about 30% of total data) was used for testing (validating) the models. Performance of the models was evaluated using standard statistical measures: Coefficient of Determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Results show that the performance of all the developed models is good (R2 > 0.9) for the prediction of water levels in the study area. However, the Bagging-based hybrid models are slightly better than another model such as REPT. Thus, these Bagging-based hybrid time series models can be used for predicting water levels at Mekong data.

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“…The (temporal) variability of RD referred by Gao et al (2015) as a precision term reflects the degree of (in)stability of the MRD. Its application has been conducted across a range of scales and yielded promising results in terms of understanding such variabilities and their controls (soil properties, vegetation, topography, and rainfall) and finding the most representative location of the spatial mean soil moisture in an area of interest (Dari et al, 2019; Hao et al, 2020; Vanderlinden et al, 2012; Zhao et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Role of time variant and invariant terms on soil moisture spatio‐temporal variability in a subhumid coastal wetland

Kohfahl,

David,

Aguilera

et al. 2024

Hydrological Processes

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The knowledge of soil moisture spatio‐temporal variability is highly relevant for water resources management. This paper reports an analysis of the spatial–temporal variability of soil moisture data for a small to medium‐scale soil‐sensors network in a coastal wetland of southwestern Spain. Measurements were taken from five sites located in the Doñana National Park over the time‐period of one hydrological year from September 2017 to September 2018. The total area of the soil‐sensors network shows an extension about 25 × 3 km. Soil moisture data was separated into time invariant (the temporal mean of the whole period at each site) and time‐variant terms (the deviations of soil moisture from the mean, or anomalies). The time‐invariant component was generally the main contributor to the total spatial variance of soil moisture and it was mostly controlled by the groundwater levels in the area. Nevertheless, the time variant terms have a huge effect on soil moisture variability in very dry states. Characteristic convex time‐dependent patterns for this field site were found between spatially averaged soil moisture and its variability. This information could be used for the up and downscaling of soil moisture from satellite data. Those patterns of relation between spatial mean and variability of soil moisture were only affected by heavy rainfalls giving rise to hysteretic behaviour. This study shows that even though groundwater level is a time‐variant variable, it significantly affects soil moisture's time‐variant but also time‐invariant terms due to the different average groundwater level depths at the different sites.

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Applicability of temporal stability analysis in predicting field mean of soil moisture in multiple soil depths and different seasons in an irrigated vineyard

Cited by 8 publications

References 29 publications

Hybridization of Differential Evolution and Adaptive-NetworkBased Fuzzy Inference System in Estimation of Compression Coefficient of Plastic Clay Soil

Hybridization of Differential Evolution and Adaptive-NetworkBased Fuzzy Inference System in Estimation of Compression Coefficient of Plastic Clay Soil

Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam

Role of time variant and invariant terms on soil moisture spatio‐temporal variability in a subhumid coastal wetland

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