Water table depth forecasting in cranberry fields using two decision-tree-modeling approaches

Brédy, Jhemson; Gallichand, Jacques; Celicourt, Paul; Gumière, Silvio José

doi:10.1016/j.agwat.2020.106090

Cited by 48 publications

(21 citation statements)

References 51 publications

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“…Bagging (bootstrap aggregating) is an ensemble technique that predicts results by generating multiple decision trees independently of each other [20]. In boosting, multiple decision trees are grown sequentially using information from existing trees [21].…”

Section: Introductionmentioning

confidence: 99%

The Predictive Capability of a Novel Ensemble Tree-Based Algorithm for Assessing Groundwater Potential

Park

2021

Sustainability

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Understanding the potential groundwater resource distribution is critical for sustainable groundwater development, conservation, and management strategies. This study analyzes and maps the groundwater potential in Busan Metropolitan City, South Korea, using random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGB) methods. Fourteen groundwater conditioning factors were evaluated for their contribution to groundwater potential assessment using an elastic net. Curvature, the stream power index, the distance from drainage, lineament density, and fault density were excluded from the subsequent analysis, while nine other factors were used to create groundwater potential maps (GMPs) using the RF, GBM, and XGB models. The accuracy of the resultant GPMs was tested using receiver operating characteristic curves and the seed cell area index, and the results were compared. The analysis showed that the three models used in this study satisfactorily predicted the spatial distribution of groundwater in the study area. In particular, the XGB model showed the highest prediction accuracy (0.818), followed by the GBM (0.802) and the RF models (0.794). The XGB model, which is the most recently developed technique, was found to best contribute to improving the accuracy of the GPMs. These results contribute to the establishment of a sustainable management plan for groundwater resources in the study area.

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Section: Introductionmentioning

confidence: 99%

The Predictive Capability of a Novel Ensemble Tree-Based Algorithm for Assessing Groundwater Potential

Park

2021

Sustainability

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“…Three observations wells were located within the Surface type (Wells B-b, B-c, and B-d at Site B, Figure 1), and three within the Natural type (Wells A-e and A-g at Site A; Well B-g at Site B, Figure 1). Over the 4 years, a total of 24 time series of WTDs met the characteristics of water table management required to be classified Bottom (8), Surface (8), or Natural (8). On an annual basis, 10 time series were kept for 2017, 7 for 2018, 3 for 2019, and 4 for 2020.…”

Section: Soils Sites and Datamentioning

confidence: 99%

“…The use of such tools actively relies on developing and incorporating real-time predictive and adaptive models of distribution patterns of water table depth (WTD). This kind of real-time water management has been studied widely through monitoring or modelling of soil water content or status [2,3], soil matric potential [4][5][6], crop parameters [7], and water table depth (WTD) [8,9].…”

Section: Introductionmentioning

confidence: 99%

Temporal and Local Heterogeneities of Water Table Depth under Different Agricultural Water Management Conditions

Lafond

Vanlandeghem

Gallichand

et al. 2021

Water

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Integrated water management has become a priority for cropping systems where subirrigation is possible. Compared to conventional sprinkler irrigation, the controlling water table can lead to a substantial increase in yield and water use efficiency with less pumping energy requirements. Knowing the spatiotemporal distribution of water table depth (WTD) and soil properties should help perform intelligent, integrated water management. Observation wells were installed in cranberry fields with different water management systems: Bottom, with good drainage and controlled WTD management; Surface, with good drainage and sprinkler irrigation management; Natural, without drainage, or with imperfectly drained and conventional sprinkler irrigation. During the 2017–2020 growing seasons, WTD was monitored on an hourly basis, while precipitation was measured at each site. Multi-frequential periodogram analysis revealed a dominant periodic component of 40 days each year in WTD fluctuations for the Bottom and Surface systems; for the Natural system, periodicity was heterogeneous and ranged from 2 to 6 weeks. Temporal cross correlations with precipitation show that for almost all the sites, there is a 3 to 9 h lag before WTD rises; one exception is a subirrigation site. These results indicate that automatic water table management based on continuously updated knowledge could contribute to integrated water management systems, by using precipitation-based models to predict WTD.

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“…The model was built with separate combinations of input variables. Lag times of input variables were generated by means of the sliding window method (Brédy et al, 2020), which allowed us to restructure the time series of P, ET 0 , and SMP as a supervised learning problem by using a size of the lag (d) equal to 168 h. This lag size was chosen to consider the influence of the SMP values at position T1 over the previous 168-h time period on the predictions. Therefore, we constructed the ML-based model maps an input window of width d into an individual output value y.…”

Section: Ml-based Model Development Input Selectionmentioning

confidence: 99%

“…The extraterrestrial radiation for the daily and hourly periods were estimated using the equations of Allen et al (1998), which for each day of the year and for different latitudes, can be estimated from the solar constant, G sc = 4.92 MJm −2 d −1 . Because of computational time constraints, the number of decision trees (ntree) was set to 200; as reported by Rodriguez-Galiano et al (2014) and (Brédy et al, 2020), the gain in accuracy is negligible for ntree > 200.…”

Section: Ml-based Model Development Input Selectionmentioning

confidence: 99%

Machine Learning vs. Physics-Based Modeling for Real-Time Irrigation Management

et al. 2020

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Real-time monitoring of soil matric potential has now become a common practice for precision irrigation management. Some crops, such as cranberries, are susceptible to both water and anoxic stresses. Excessive variations in soil matric potential in the root zone may reduce plant transpiration, due to either saturated or dry soil conditions, thereby reducing productivity. A timely supply of the right amount of water is, therefore, fundamental for efficient irrigation management. In this paper, we compare the capabilities of a machine learning-based model and a physics-based model to predict soil matric potential in the root zone. The machine learning model is a random forest algorithm, while the physics-based model is a two-dimensional solver of Richards equation (HYDRUS 2D). After training and calibration on a dataset collected in a cranberry field located in Québec (Canada), the performance of the two models is evaluated for 30 different time frames of 72-h soil matric potential forecasts. The results highlight that both models can accurately forecast the soil matric potential in the root zone. The machine learning-based model can achieve better performance when compared to the physics-based model, but forecasting accuracy decreases rapidly toward the end of the 72-h lead time, while the error for the Richards equation-based model does not increase with time and remain small compared to the typical measurement error.

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Water table depth forecasting in cranberry fields using two decision-tree-modeling approaches

Cited by 48 publications

References 51 publications

The Predictive Capability of a Novel Ensemble Tree-Based Algorithm for Assessing Groundwater Potential

The Predictive Capability of a Novel Ensemble Tree-Based Algorithm for Assessing Groundwater Potential

Temporal and Local Heterogeneities of Water Table Depth under Different Agricultural Water Management Conditions

Machine Learning vs. Physics-Based Modeling for Real-Time Irrigation Management

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