A Review of Precision Irrigation Water-Saving Technology under Changing Climate for Enhancing Water Use Efficiency, Crop Yield, and Environmental Footprints

Agriculture, Journal Year: 2024, Volume and Issue: 14(7), P. 1141 - 1141

Published: July 14, 2024

Water is considered one of the vital natural resources and factors for performing short- long-term agricultural practices on Earth. Meanwhile, globally, most available freshwater are utilized irrigation purposes in agriculture. Currently, many world regions facing extreme water shortage problems, which can worsen if not managed properly. In literature, numerous methods remedies used to cope with increasing global crises. The use precision water-saving systems (PISs) efficient management under climate change them a highly recommended approach by researchers. It mitigate adverse effects changing help enhance efficiency, crop yield, environmental footprints. Thus, present study aimed comprehensively examine review PISs, focusing their development, implementation, positive impacts sustainable management. addition, we searched literature using different online search engines reviewed summarized main results previously published papers PISs. We discussed traditional method its modernization enhancing PIS monitoring controlling, architecture, data sharing communication technologies, role artificial intelligence water-saving, future prospects PIS. Based brief review, concluded that PISs seems bright, driven need systems, technological advancements, awareness. As scarcity problem intensifies due population growth, poised play critical optimizing modernizing usage, reducing footprints, thus ensuring agriculture development.

Language: Английский

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Enhancing Water Level Prediction Using Ensemble Machine Learning Models: A Comparative Analysis

Water Resources Management, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 11, 2025

Language: Английский

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A New Benchmark on Machine Learning Methodologies for Hydrological Processes Modelling: A Comprehensive Review for Limitations and Future Research Directions

Knowledge-Based Engineering and Sciences, Journal Year: 2023, Volume and Issue: 4(3), P. 65 - 103

Published: Dec. 31, 2023

The best practice of watershed management is through the understanding hydrological processes. As a matter fact, processes are highly associated with stochastic, non-linear, and non-stationary phenomena. Hydrological simulation modeling challenging issues in domains hydrology, climate environment. Hence, development machine learning (ML) models for solving those complex problems took essential place over past couple decades. It can be observed, data availability has increased remarkably, thus computational resources led to resurgence ML models’ development. been witnessed huge efforts on using facility several review researches have conducted. Literature studies approved capacity field hydrology classical “traditional models” based their forecastability, flexibility, precision, generalization, execution convergence speed. However, although potential merits were observed model’s development, limitations allied such as interpretability black-box models, practicality management, difficulty explain physical In this survey, an exhibition all published articles recognize research gaps direction. ultimate aim current survey establish new milestone interested environment researchers applications models.

Language: Английский

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Climate Impact on Evapotranspiration in the Yellow River Basin: Interpretable Forecasting with Advanced Time Series Models and Explainable AI

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(1), P. 115 - 115

Published: Jan. 1, 2025

Evapotranspiration (ET) plays a crucial role in the hydrological cycle, significantly impacting agricultural productivity and water resource management, particularly water-scarce areas. This study explores effects of key climate variables temperature, precipitation, solar radiation, wind speed, humidity on ET from 2000 to 2020, with forecasts extended 2030. Advanced data preprocessing techniques, including Yeo-Johnson Box-Cox transformations, Savitzky–Golay smoothing, outlier elimination, were applied improve quality. Datasets MODIS, TRMM, GLDAS, ERA5 utilized enhance model accuracy. The predictive performance various time series forecasting models, Prophet, SARIMA, STL + ARIMA, TBATS, ARIMAX, ETS, was systematically evaluated. also introduces novel algorithms for Explainable AI (XAI) SHAP (SHapley Additive exPlanations), enhancing interpretability predictions improving understanding how affect ET. comprehensive methodology not only accurately but offers transparent approach climatic results indicate that Prophet ETS models demonstrate superior prediction accuracy compared other models. achieved lowest Mean Absolute Error (MAE) values 0.60 0.51 0.48 radiation. excelled Root Squared (RMSE) 0.62 0.67 0.74 precipitation. analysis indicates temperature has strongest impact predictions, ranging −1.5 1.0, followed by speed (−0.75 0.75) radiation (−0.5 0.5).

Language: Английский

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Precipitation recycling impacts on runoff in arid regions of China and Mongolia: a machine learning approach

Hydrological Sciences Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 20, 2025

Language: Английский

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Forecasting energy consumption and enhancing sustainability in microbreweries: Integrating ANN-based models with thermal storage solutions

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 112, P. 115508 - 115508

Published: Jan. 24, 2025

Language: Английский

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Water, Resources, and Resilience: Insights from Diverse Environmental Studies

Water, Journal Year: 2023, Volume and Issue: 15(22), P. 3965 - 3965

Published: Nov. 15, 2023

Water is our most precious resource, and its responsible management utilization are paramount in the face of ever-growing environmental challenges [...]

Language: Английский

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Harnessing Deep Learning for Real-Time Water Quality Assessment: A Sustainable Solution

Water, Journal Year: 2024, Volume and Issue: 16(23), P. 3380 - 3380

Published: Nov. 24, 2024

This study presents an innovative approach utilizing artificial intelligence (AI) for the prediction and classification of water quality parameters based on physico-chemical measurements. The primary objective was to enhance accuracy, speed, accessibility monitoring. Data collected from various samples in Algeria were analyzed determine key such as conductivity, turbidity, pH, total dissolved solids (TDS). These measurements integrated into deep neural networks (DNNs) predict indices sodium adsorption ratio (SAR), magnesium hazard (MH), percentage (SP), Kelley’s (KR), potential salinity (PS), exchangeable (ESP), well Water Quality Index (WQI) Irrigation (IWQI). DNNs model, optimized through selection activation functions hidden layers, demonstrated high precision, with a correlation coefficient (R) 0.9994 low root mean square error (RMSE) 0.0020. AI-driven methodology significantly reduces reliance traditional laboratory analyses, offering real-time assessments that are adaptable local conditions environmentally sustainable. provides practical solution resource managers, particularly resource-limited regions, efficiently monitor make informed decisions public health agricultural applications.

Language: Английский

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Modeling Dynamic Processes in the Black Sea Pelagic Habitat—Causal Connections between Abiotic and Biotic Factors in Two Climate Change Scenarios

Sustainability, Journal Year: 2024, Volume and Issue: 16(5), P. 1849 - 1849

Published: Feb. 23, 2024

The paper contributes to the Sustainable Development Goals (SDGs) targeting Life Below Water by introducing user-friendly modeling approaches. It delves into impact of abiotic factors on first two trophic levels within marine ecosystem, both naturally and due human influence. Specifically, study examines connections between environmental parameters (e.g., temperature, salinity, nutrients) plankton along Romanian Black Sea coast during warm season over a decade. research develops models forecast zooplankton proliferation using machine learning (ML) algorithms gathered data. temperature significantly affects copepods “other groups” densities season. Conversely, no discernible is observed dinoflagellate Noctiluca scintillans blooms. Salinity fluctuations notably influence typical phytoplankton proliferation, with phosphate concentrations primarily driving widespread explores scenarios for forecasting growth: Business as Usual, predicting modest increases in constant nutrient levels, Mild scenario, projecting substantial salinity alongside significant decrease 2042. findings underscore high under scenarios, particularly pronounced second surpassing around 70%. These findings, indicative eutrophic potential implications altered ecosystem health, aligning SDGs focused Water.

Language: Английский

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Artificial Intelligence for Water Consumption Assessment: State of the Art Review

Water Resources Management, Journal Year: 2024, Volume and Issue: 38(9), P. 3113 - 3134

Published: April 25, 2024

Abstract In recent decades, demand for freshwater resources has increased the risk of severe water stress. With growing prevalence artificial intelligence (AI), many researchers have turned to it as an alternative linear methods assess consumption (WC). Using PRISMA (Preferred Reporting Items Systematic Reviews and Meta-Analyses) framework, this study utilized 229 screened publications identified through database searches snowball sampling. This introduces novel aspects AI's role in assessment by focusing on innovation, application sectors, sustainability, machine learning applications. It also categorizes existing models, such standalone hybrid, based input, output variables, time horizons. Additionally, classifies learnable parameters performance indexes while discussing AI models' advantages, disadvantages, challenges. The translates information into a guide selecting models WC assessment. As no one-size-fits-all model exists, suggests utilizing hybrid alternatives. These offer flexibility regarding efficiency, accuracy, interpretability, adaptability, data requirements. They can address limitations individual leverage strengths different approaches, provide better understanding relationships between variables. Several knowledge gaps were identified, resulting suggestions future research.

Language: Английский

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