Forecasting Pesticide Use on Golf Courses by Integration of Deep Learning and Decision Tree Techniques

Grégoire, Guillaume; Fortin, J. A.; Ebtehaj, Isa; Bonakdari, Hossein

doi:10.3390/agriculture13061163

Cited by 5 publications

(1 citation statement)

References 35 publications

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“…where Q o and Q m represent the observed and modeled values of the target variable (respectively), H denotes the number of samples, Q o and Q m correspond to the average of the observed and modeled values of the target variable, respectively. The model efficiency characterization based on R 2 , NSE, and NRMSE intervals is provided in Table 2 [62].…”

Section: Goodness Of Fitmentioning

confidence: 99%

Modeling Hydrodynamic Behavior of the Ottawa River: Harnessing the Power of Numerical Simulation and Machine Learning for Enhanced Predictability

Cardi,

Dussel,

Letessier

et al. 2023

Hydrology

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The Ottawa River Watershed is a vast area that stretches across Ontario and Quebec and holds great importance for Canada’s people, economy, and collective history, both in the present and the future. The river has faced numerous floods in recent years due to climate change. The most significant flood occurred in 2019, surpassing a 100-year flood event, and serves as a stark reminder of how climate change impacts our environment. Considering the limitations of machine learning (ML) models, which heavily rely on historical data used during training, they may struggle to accurately predict such “non-experienced” or “unseen” floods that were not encountered during the training process. To tackle this challenge, our study has utilized a combination of numerical modeling and ML to create an integrated methodology. Indeed, a comprehensive dataset of river flow discharge was generated using a numerical model, encompassing a wide range of potential future floods. This significantly improved the ML training process to generalize the accuracy of results. Utilizing this dataset, a novel ML model called the Expanded Framework of Group Method of Data Handling (EFGMDH) has been developed. Its purpose is to provide decision-makers with explicit equations for estimating three crucial hydrodynamic characteristics of the Ottawa River: floodplain width, flow velocity, and river flow depth. These predictions rely on various inputs, including the location of the desired cross-section, river slope, Manning roughness coefficient at different river sections (right, left, and middle), and river flow discharge. To establish practical models for each of the aforementioned hydrodynamic characteristics of the Ottawa River, different input combinations were tested to identify the most optimal ones. The EFGMDH model demonstrated high accuracy throughout the training and testing stages, achieving an R2 value exceeding 0.99. The proposed model’s exceptional performance demonstrates its reliability and practical applications for the study area.

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Section: Goodness Of Fitmentioning

confidence: 99%

Modeling Hydrodynamic Behavior of the Ottawa River: Harnessing the Power of Numerical Simulation and Machine Learning for Enhanced Predictability

Cardi,

Dussel,

Letessier

et al. 2023

Hydrology

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Generalized Structure of Group Method of Data Handling: Novel Technique for Flash Flood Forecasting

Ebtehaj,

Bonakdari

2024

Water Resour Manage

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Advanced Forecasting of Drought Zones in Canada Using Deep Learning and CMIP6 Projections

Soltani,

Amiri,

Ebtehaj

et al. 2024

Climate

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This study addresses the critical issue of drought zoning in Canada using advanced deep learning techniques. Drought, exacerbated by climate change, significantly affects ecosystems, agriculture, and water resources. Canadian Drought Monitor (CDM) data provided by the Canadian government and ERA5-Land daily data were utilized to generate a comprehensive time series of mean monthly precipitation and air temperature for 199 sample locations in Canada from 1979 to 2023. These data were processed in the Google Earth Engine (GEE) environment and used to develop a Convolutional Neural Network (CNN) model to estimate CDM values, thereby filling gaps in historical drought data. The CanESM5 climate model, as assessed in the IPCC Sixth Assessment Report, was employed under four climate change scenarios to predict future drought conditions. Our CNN model forecasts CDM values up to 2100, enabling accurate drought zoning. The results reveal significant trends in temperature changes, indicating areas most vulnerable to future droughts, while precipitation shows a slow increasing trend. Our analysis indicates that under extreme climate scenarios, certain regions may experience a significant increase in the frequency and severity of droughts, necessitating proactive planning and mitigation strategies. These findings are critical for policymakers and stakeholders in designing effective drought management and adaptation programs.

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Forecasting Pesticide Use on Golf Courses by Integration of Deep Learning and Decision Tree Techniques

Cited by 5 publications

References 35 publications

Modeling Hydrodynamic Behavior of the Ottawa River: Harnessing the Power of Numerical Simulation and Machine Learning for Enhanced Predictability

Modeling Hydrodynamic Behavior of the Ottawa River: Harnessing the Power of Numerical Simulation and Machine Learning for Enhanced Predictability

Generalized Structure of Group Method of Data Handling: Novel Technique for Flash Flood Forecasting

Advanced Forecasting of Drought Zones in Canada Using Deep Learning and CMIP6 Projections

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