Predictive Models for Optimal Irrigation Scheduling and Water Management: A Review of AI and ML Approaches

International Journal of Management Technology and Social Sciences, Journal Year: 2024, Volume and Issue: unknown, P. 94 - 110

Published: May 20, 2024

Purpose: Maintaining agricultural output, protecting water supplies, and lessening environmental effects all depend on effective management. Through a comprehensive review of the literature an in-depth analysis various AI ML techniques, this paper aims to put light cutting-edge approaches used in irrigation scheduling predictive modeling. The goal research is determine advantages, disadvantages, future directions ML-based management systems by means methodical algorithms, data sources, applications. Additionally, study seeks demonstrate how data-driven methods can enhance systems' sustainability, accuracy, precision. Stakeholders agriculture, resource management, conservation make well-informed decisions maximize techniques having thorough understanding theoretical underpinnings practical applications models. also attempts tackle issues like scalability, model interpretability, lack when implementing solutions for In final form, review's conclusions advance our use improve resilience efficiency, supporting adaptive sustainable strategies face rising scarcity concerns climate change. Design/Methodology/Approach: order gather information study, several articles from reliable sources were analyzed compared. Objective: To provide current gaps prediction models best suggest using fill these gaps. Results/ Findings: response growing challenges change, paper's findings highlight transformative potential optimizing scheduling, enhancing resilience, increasing strategies. Originality/Value: This uniqueness significance come its modeling ideal scheduling. It provides insights into new their possible optimization sustainability. Type Paper: Literature Review.

Language: Английский

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Machine Learning for Peatland Ground Water Level (GWL) Prediction via IoT System

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 89585 - 89598

Published: Jan. 1, 2024

Peatland poses a severe environmental threat due to its potential for massive carbon emission during fires. Conventional Ground Water Level (GWL) monitoring in peatlands is labor-intensive and lacks real-time data, hindering effective management. To address this, this paper proposed an IoT system with neural network-based GWL prediction monitoring. By using atmospheric parameters, the network predicts GWL, allowing extra time responsible party take appropriate action reduce fire risk peatland. The demonstrates promising results, Root Mean Square Error (RMSE) between 3.554 4.920, ensuring 99% accuracy within 14.760 mm range of actual GWL. This finding underscores novel approach integrating networks peatland prediction, offering significant advancement mitigation strategies. novelty lies capability predict even areas lacking resources conventional monitoring, simple meteorological parameters.

Language: Английский

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Development of Functional Quantile Autoregressive Model for River Flow Curve Forecasting

Earth and Space Science, Journal Year: 2024, Volume and Issue: 11(8)

Published: Aug. 1, 2024

Abstract Among several hydrological processes, river flow is an essential parameter that vital for different water resources engineering activities. Although methodologies have been adopted over the literature modeling flow, limitation still exists in time series curve. In this research, a functional quantile autoregressive of order one model was developed to characterize entire conditional distribution Based on principal component analysis, regression function estimated using multivariate framework. For purpose, hourly scale collected from three rivers Australia (Mary River, Lockyer Valley, and Albert River) were used evaluate finite‐sample performance proposed methodology. A Monte‐Carlo experiments historical data sets examined at stations. Further, uncertainty analysis methodology evaluation. Compared with existing methods, provides more robust forecasts outlying observations, non‐Gaussian heavy‐tailed error distribution, heteroskedasticity. Also, has merit predicting intervals future realizations central non‐central locations. The results confirmed potential curve high level accuracy comparison benchmark methods.

Language: Английский

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Fluoride contamination in African groundwater: Predictive modeling using stacking ensemble techniques

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 957, P. 177693 - 177693

Published: Nov. 25, 2024

Language: Английский

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