Search Results (25)

The canopy temperature of winter jujube serves as a direct indicator of plant water status and transpiration efficiency, making its accurate prediction a critical prerequisite for effective water management and optimized growth conditions in greenhouse environments. This study developed a data-driven model to forecast canopy temperature. The model serially integrates a Long Short-Term Memory (LSTM) network and a Random Forest (RF) algorithm, leveraging their complementary strengths in capturing temporal dependencies and robust nonlinear fitting. A three-stage framework comprising temporal feature extraction, multi-source feature fusion, and direct prediction was implemented to enable reliable nowcasting. Data acquisition and preprocessing were tailored to the greenhouse environment, involving multi-sensor data and thermal imagery processed with Robust Principal Component Analysis (RPCA) for dimensionality reduction. Key environmental variables were selected through Spearman correlation analysis. Experimental results demonstrated that the proposed LSTM–RF model achieved superior performance, with a determination coefficient (R²) of 0.974, mean absolute error (MAE) of 0.844 °C, and root mean square error (RMSE) of 1.155 °C, outperforming benchmark models including standalone LSTM, RF, Transformer, and TimesNet. SHAP (SHapley Additive exPlanations)-based interpretability analysis further quantified the influence of key factors, including the “thermodynamic state of air” driver group and latent temporal features, offering actionable insights for irrigation management. The model establishes a reliable, interpretable foundation for real-time water stress monitoring and precision irrigation control in protected winter jujube production systems. Full article

Accurate microclimate forecasting is essential for optimizing agricultural decision-making and resource management within Internet of Things (IoT)-enabled farming systems. This study proposes an Attention-Enhanced Dual-Branch Spatio-Temporal Deep Neural Network with Station Embeddings model designed for robust spatio-temporal multisensor temperature forecasting across heterogeneous environmental stations. The model integrates multisensor data parameters within a sliding-window temporal framework to capture both short-term fluctuations and long-term dependencies. Comprehensive experiments were conducted using data from two meteorological stations to evaluate model accuracy, generalization, and robustness against sensor noise. Results show that the proposed model outperforms both classical and persistence-based baselines, achieving an average RMSE of 1.65 °C and R² of 0.94 on test datasets. Feature correlation and importance analyses confirmed that the model learns physically meaningful relationships—particularly the influence of soil temperature and humidity on air temperature dynamics—while residual and convergence analyses verified its stability and unbiased learning behavior. Beyond algorithmic validation, this study highlights how the proposed model can be integrated into precision-agriculture systems for adaptive irrigation control, crop-growth forecasting, and microclimate-based disease-risk assessment. The model provides a scalable foundation for real-time IoT deployment on edge devices, enabling continuous environmental monitoring and intelligent actuation. These results demonstrate that data-driven deep learning models can bridge algorithmic forecasting and operational decision-making, contributing to sustainable and efficient agricultural management. Full article

Efficient water management is a critical challenge for modern agriculture, particularly in the context of increasing climate variability and limited freshwater resources. This study presents a comparative field-based evaluation of two fuzzy-logic-based irrigation decision support systems for vineyard management: a Mamdani-type controller with expert-defined rules and a Takagi–Sugeno system designed to enable automated learning from ultra-local historical field data. Both systems integrate soil moisture sensing, short-term forecasting, and weather predictions to provide optimized irrigation recommendations. The evaluation combines counterfactual simulations with a bootstrap-based statistical analysis to assess water use efficiency, soil moisture control, and robustness to environmental variability. The comparison highlights distinct strengths of the two approaches, revealing trade-offs between water conservation and crop stress mitigation, and offering practical insights for the design and deployment of intelligent irrigation management solutions. Full article

Understanding water and heat transport through soils is vital for managing soil and groundwater resources, agricultural irrigation, and ecosystem protection. This paper aims to explore the potential application of deep learning methods in simulating water and heat transport processes within soils. It also examines the interactions between soil hydrological processes and environmental factors, including meteorological conditions and groundwater levels. To achieve these, we develop a hybrid model Informer–LSTM by combining two powerful architectures: Informer, a Transformer-based model essentially designed for long-sequence time-series forecasting, and Long Short-Term Memory (LSTM), a neural network that is great at learning short-term patterns in sequential data. The model is applied to field measurements from Henan Township in Ordos, Inner Mongolia, China, for training and testing, to simulate three key variables: soil water content, temperature, and heat flux at different depths in two soil columns with different groundwater levels. Our results confirm that Informer–LSTM is highly effective at simulating the soil water and heat transport. Simultaneously, we evaluate its performance by incorporating various combinations of input data including meteorological data, soil hydrothermal dynamics, and groundwater level. This reveals the relationship between soil hydrothermal processes and meteorological data, as well as coupled processes of soil water and heat transport. Moreover, employing SHapley Additive exPlanations (SHAP) analysis, we identify the most influential factors for predicting heat flux in shallow soils. This research demonstrates that deep learning models are a viable and valuable tool for simulating soil hydrothermal processes in arid and semi-arid regions. Full article

This study addresses the critical challenge of optimizing water resource allocation in fragmented citrus cultivation zones, particularly in Anfusi Town, a key citrus production area in China’s middle-lower Yangtze River region. To overcome the limitations of traditional deterministic models and spatially heterogeneous water supply–demand dynamics, an innovative framework integrating interval two-stage stochastic programming (ITSP) with long short-term memory (LSTM) neural networks is proposed. The LSTM component forecasts irrigation demand and supply under climate variability, while ITSP optimizes dynamic allocation strategies by quantifying uncertainties through interval analysis and balancing economic returns with hydrological risks. Key results demonstrate an 8.67% increase in system-wide benefits compared to baseline practices in the current year scenario. For the planning year (2025), the model identifies optimal water distribution thresholds: an upper limit of 3.85 × 10⁶ m³ for high-availability zone A and lower limits of 1.62 × 10⁶ m³ for moderate-to-low-availability zones B and C. These allocations minimize water scarcity penalties while maximizing net benefits, prioritizing local over external water sources to reduce costs. The study innovates by integrating stochastic-economic analysis with spatial prioritization of high-marginal-benefit zones and uncertainty robustness via interval analysis and two-stage decision making. By bridging a research gap in citrus irrigation optimization, this approach advances sustainable water management in complex agricultural systems, offering a scalable solution for regions facing fragmented landscapes and climate-driven water scarcity. Full article

Accurate irrigation volume prediction is crucial for sustainable agriculture. This study enhances precision irrigation by integrating diverse datasets, including historical irrigation records, soil moisture, and climatic factors, collected from a small-scale commercial estate vineyard in southwestern Idaho, the United States of America (USA), over a period of three years (2017–2019). Focusing on long-term irrigation forecasting, addressing a critical gap in sustainable water management, we use machine learning (ML) methods to predict future irrigation needs, with improved accuracy. We designed, developed, and tested a Long Short-Term Memory (LSTM) model, which achieved a Mean Squared Error (MSE) of 0.37, and evaluated its performance against a simpler baseline linear regression (LinReg) model, which yielded a higher MSE of 1.29. We validate the results of the LSTM model using a cross-validation technique, wherein a mean MSE of 0.18 was achieved. The low value of the statistical analysis (p-value = 0.0009) of a paired t-test confirmed that the improvement is significant. This research shows the potential of Artificial Intelligence (AI) to optimize irrigation planning and advance sustainable precision agriculture (PA), by providing a practical tool for long-term forecasting and that supports data-driven decisions. Full article

Surface water systems face unprecedented stress due to climate variability, urbanization, land-use change, and growing water demand—prompting a shift from traditional hydrological modeling to intelligent, adaptive systems. This review critically explores the integration of Artificial Intelligence (AI) in surface flow management, encompassing applications in streamflow forecasting, sediment transport, flood prediction, water quality monitoring, and infrastructure operations such as dam and irrigation control. Drawing from over two decades of interdisciplinary literature, this study synthesizes recent advances in machine learning (ML), deep learning (DL), the Internet of Things (IoT), remote sensing, and hybrid AI–physics models. Unlike earlier reviews focusing on single aspects, this paper presents a systems-level perspective that links AI technologies to their operational, ethical, and governance dimensions. It highlights key AI techniques—including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), Transformer models, and Reinforcement Learning—and discusses their strengths, limitations, and implementation challenges, particularly in data-scarce and climate-uncertain regions. Novel insights are provided on Explainable AI (XAI), algorithmic bias, cybersecurity risks, and institutional readiness, positioning this paper as a roadmap for equitable and resilient AI adoption. By combining methodological analysis, conceptual frameworks, and future directions, this review offers a comprehensive guide for researchers, engineers, and policy-makers navigating the next generation of intelligent surface flow management. Full article

Vegetation productivity, as an essential global carbon sink, directly influences the variety and stability of ecosystems. Precise vegetation productivity monitoring and forecasting are crucial for the global carbon cycle. Traditional machine learning algorithms frequently experience overfitting when processing high-dimensional time-series data or substantial numbers of outliers, impeding the accurate prediction of various vegetation metrics. We propose a multimodal regression prediction model utilizing the TCLA framework—comprising the Transient Trigonometric Harris Hawks Optimizer (TTHHO), Convolutional Neural Networks (CNN), Least Squares Support Vector Machine (LSSVM), and Adaptive Bandwidth Kernel Density Estimation (ABKDE)—with the Hetao Irrigation District, a vast irrigation basin in China, serving as the study area. This model employs TTHHO to effectively navigate the search space and adaptively optimize network node positions, integrates CNN-LSSVM for feature extraction and regression analysis, and incorporates ABKDE for probability density function estimation and outlier detection, resulting in accurate interval probability prediction and enhanced model resilience to interference. Experimental data indicate that the TCLA model improves prediction accuracy by 10.57–26.47% compared to conventional models (Long Short-Term Memory (LSTM), Transformer). In the presence of 5–15% outliers, the fusion of multimodal data results in a substantial drop in RMSE (p < 0.05), with a reduction of 45.18–69.66%, yielding values between 0.079 and 0.137, thereby demonstrating the model’s high robustness and resistance to interference in predicting the next three years. This work introduces a scientific approach for precisely forecasting alterations in regional vegetation productivity using the proposed multimodal TCLA model, significantly enhancing global vegetation resource management and ecological conservation techniques. Full article

Daily reference crop evapotranspiration (ET₀) is crucial for precision irrigation management, yet traditional prediction methods struggle to capture its dynamic variations due to the complexity and nonlinearity of meteorological conditions. To address this, we propose an Improved Informer model to enhance ET₀ prediction accuracy, providing a scientific basis for agricultural water management. Using meteorological and soil data from the Yingde region, we employed the Maximal Information Coefficient (MIC) to identify key influencing factors and integrated Residual Cycle Forecasting (RCF), Star Aggregate Redistribute (STAR), and Fully Adaptive Normalization (FAN) techniques into the Informer model. MIC analysis identified total shortwave radiation, sunshine duration, maximum temperature at 2 m, soil temperature at 28–100 cm depth, and surface pressure as optimal features. Under the five-feature scenario (S3), the improved model achieved superior performance compared to Long Short-Term Memory (LSTM) and the original Informer models, with MAE reduced to 0.065 (LSTM: 0.637, Informer: 0.171) and MSE to 0.007 (LSTM: 0.678, Informer: 0.060). The inference time was also reduced by 31%, highlighting the enhanced computational efficiency. The Improved Informer model effectively captures the periodic and nonlinear characteristics of ET₀, offering a novel solution for precision irrigation management with significant practical implications. Full article

Renewable sources of energy production are some of the main targets today to protect the environment through reduced fossil fuel consumption and CO₂ emissions. Alongside wind, solar, marine, biomass and nuclear sources, hydropower is among the oldest but still not fully explored renewable energy sources. Compared with other sources like wind and solar, hydropower is more stable and consistent, offering increased predictability. Even so, it should be analyzed considering water flow, dams capacity, climate change, irrigation, navigation, and so on. The aim of this study is to propose a forecast model of hydropower production capacity and identify long-term trends. The curve fit forecast parabolic model was applied to 33 European countries for time series data from 1990 to 2021. Space-time cube ArcGIS representation in 2D and 3D offers visualization of the prediction and model confidence rate. The quadratic trajectory fit the raw data for 14 countries, validated by visual check, and in 20 countries, validated by FMRSE 10% threshold from the maximal value. The quadratic model choice is good for forecasting future values of hydropower electric capacity in 22 countries, with accuracy confirmed by the VMRSE 10% threshold from the maximal value. Seven local outliers were identified, with only one validated as a global outlier based on the Generalized Extreme Studentized Deviate (GESD) test at a 5% maximal number of outliers and a 90% confidence level. This result achieves our objective of estimating a level with a high degree of occurrence and offering a reliable forecast of hydropower production capacity. All European countries show a growing trend in the short term, but the trends show a stagnation or decrease if policies do not consider intensive growth through new technology integration and digital adoption. Unfortunately, Europe does not have extensive growth potential compared with Asia–Pacific. Public policies must boost hybrid hydro–wind or hydro–solar systems and intensive technical solutions. Full article

The reference evapotranspiration (ET₀) information is crucial for irrigation planning and water resource management. While the Penman-Monteith (PM) equation is widely recognized for ET₀ calculation, its reliance on numerous meteorological parameters constrains its practical application. This study used 28 years of meteorological data from 18 stations in four geographic regions of Taiwan to evaluate the effectiveness of an artificial intelligence (AI) model for estimating PM-calculated ET₀ using limited meteorological variables as input and compared it with traditional methods. The AI models were also employed for short-term ET₀ forecasting with limited meteorological variables. The findings suggested that AI models performed better than their counterpart methods for ET₀ estimation. The artificial neural network using temperature, solar radiation, and relative humidity as input variables performed best, with the correlation coefficient (r) ranging from 0.992 to 0.998, mean absolute error (MAE) ranging from 0.07 to 0.16 mm/day, and root mean square error (RMSE) ranging from 0.12 to 0.25 mm/day. For short-term ET₀ forecasting, the long short-term memory model using temperature, solar radiation, and relative humidity as input variables was the best structure to forecast four-day-ahead ET₀, with the r ranging from 0.608 to 0.756, MAE ranging from 1.05 to 1.28 mm/day, and RMSE ranging from 1.35 to 1.62 mm/day. The percentage error of this structure was within $\pm$5% for most meteorological stations over the one-year test period, underscoring the potential of the proposed models to deliver daily ET₀ forecasts with acceptable accuracy. Finally, the proposed estimating and forecasting models were developed in regional and variable-limited scenarios, making them highly advantageous for practical applications. Full article

The accurate measurement of soil moisture content emerges as a critical parameter within the ambit of agricultural irrigation management, wherein the precise prediction of this variable plays an instrumental role in enhancing the efficiency and conservation of agricultural water resources. This study introduces an innovative, cutting-edge hybrid model that ingeniously integrates Gated Recirculation Unit (GRU) and Transformer technologies, meticulously crafted to amplify the precision and reliability of soil moisture content forecasts. Leveraging meteorological and soil moisture datasets amassed from eight monitoring stations in Hebei Province, China, over the period from 2011 to 2018, this investigation thoroughly assesses the model’s efficacy against a diverse array of input variables and forecast durations. This assessment is concurrently contrasted with a range of conventional machine learning and deep learning frameworks. The results demonstrate that (1) the GRU–Transformer model exhibits remarkable superiority across various aspects, particularly in short-term projections (1- to 2-day latency). The model’s mean square error (MSE) for a 1-day forecast is notably low at 5.22%, reducing further to a significant 2.71%, while the mean coefficient of determination (R²) reaches a high of 89.92%. Despite a gradual increase in predictive error over extended forecast periods, the model consistently maintains robust performance. Moreover, the model shows exceptional versatility in managing different soil depths, notably excelling in predicting moisture levels at greater depths, thereby surpassing its performance in shallower soils. (2) The model’s predictive error inversely correlates with the reduction in parameters. Remarkably, with a streamlined set of just six soil moisture content parameters, the model predicts an average MSE of 0.59% and an R² of 98.86% for a three-day forecast, highlighting its resilience to varied parameter configurations. (3) In juxtaposition with prevalent models such as Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Gradient Boosting Decision Tree (GBDT), XGBoost, Random Forest, and deep learning models like Deep Neural Network (DNN), Convolutional Neural Network (CNN), and standalone GRU-branch and Transformer-branch models, the GRU–Transformer framework demonstrates a significant advantage in predicting soil moisture content with enhanced precision for a five-day forecast. This underscores its exceptional capacity to navigate the intricacies of soil moisture data. This research not only provides a potent decision-support tool for agricultural irrigation planning but also makes a substantial contribution to the field of water resource conservation and optimization in agriculture, while concurrently imparting novel insights into the application of deep learning techniques in the spheres of agricultural and environmental sciences. Full article

Agriculture is a vulnerable sector to climate change due to its sensitivity to weather conditions. Changes in climatic parameters such as temperature and precipitation significantly affect productivity as well as the consumption of natural resources like water to meet irrigation water needs. There has been a large amount of research on regional climate change. However, this study placed specific crops at first place and considered their irrigation water needs that will arise due to evapotranspiration increase. The aim of this study was to estimate the future irrigation water needs of wheat, cotton, and alfalfa in the east part of Thessaly Plain in central Greece, where Lake Karla, a recently restored lake, is located. The Weather Research and Forecasting (WRF) model was applied as a high-resolution regional climate model to simulate temperature and precipitation for two 5-year periods, namely 2046–2050 (future period) and 2006–2010 (reference period). Simulations refer to the RCP8.5 emission scenario (worst-case). A methodology proposed by the Food and Agriculture Organization (FAO) of the United Nations was followed to estimate the reference crop evapotranspiration, the crop evapotranspiration based on each crop factor, which was determined for each crop, the effective rainfall, and finally, the irrigation water needs for each crop, for the two 5-year periods. Based on WRF simulations, temperature was projected to be 1.1 °C higher in the future period compared to the reference period, while precipitation and effective precipitation were projected to decrease by 32% and 45%, respectively. Based on the WRF projections, by 2025, the irrigation water needs of wheat and alfalfa are expected to increase by more than 16% and more than 11%, respectively, while irrigation water needs of cotton are expected to increase by 7%. An extension of wheat’s irrigation period for one month (i.e., December) was also identified. Good practices that could be applied in the frame of precision agriculture principles in order to save irrigation water were suggested. The results of this study could be exploited by water resources and land use managers when planning short and long-term strategies to adapt to climate change impacts. Full article

The water supply in Saudi Arabia is already depleted. Climate change will exacerbate the demand for these resources. This paper examines how climate change affects the water demands of Saudi Arabia’s most important food crops: wheat, clover, vegetables, and dates. To reduce the adverse climate change impacts on these crops’ productivity, as well as their irrigation water requirements (IWR), a number of adaptation techniques were investigated. The study was carried out for the Ar Riyadh region, Saudi Arabia, with a cultivated area of 179,730 ha. In this study, five climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for two Shared Socio-economic Pathways (SSPs), SSP2-4.5 and SSP5-8.5, were used to forecast and investigate the potential impacts of climate change on agricultural water resources in the Al-Riyadh Region of Saudi Arabia. To simulate IWRs under the present and projected climate change scenarios, CROPWAT8.0 was used. The results showed that the maximum increase ratio in 2100 under SSP2-4.5 and SSP5-8.5, respectively, will be 4.46% and 12.11% higher than in the current case (2020). The results showed that the projected maximum temperatures in 2100 will be increased by 4.46% and 12.11%, respectively, under SSP2-4.5 and SSP5-8.5, compared to the current case (2020), supporting past research on the Arabian Peninsula that revealed that both short- and long-term temperature increases are anticipated to be considerable. Under SSP2-4.5 and SSP5-8.5, the projected ET_o was found to be increased by 2.18% and 6.35% in 2100, respectively. Given that evapotranspiration closely mirrors the temperature behavior in the study region from June to August, our data suggest that crop and irrigation demand may increase in the mid to long term. The findings indicate that Riyadh, Saudi Arabia’s capital and commercial hub, will require more water to irrigate agricultural land because of the expanding ETo trend. Under SSP2-4.5 and SSP5-8.5, the projected growth irrigation water requirement (GIWR) will be increased by 3.1% and 6.7% in 2100, respectively. Under SSP5-8.5, crop areas of wheat, clover, dates, maize, citrus, tomato, potato, and other vegetables in Ar Riyadh will decrease by 6.56%, 7.17%, 5.90%, 6.43%, 5.47%, 6.99%, 5.21%, and 5.5%, respectively, in 2100. Conversely, under SSP2-4.5, the crop areas will decrease by 3.10%, 3.67%, 2.35%, 3.83%, 2.32%, 4.18%, 1.72%, and 2.38% in 2100, respectively. This research could aid in clarifying the adverse climate change impacts on GIWR in Ar Riyad, as well as improving water resource management planning. Full article

Irrigation systems are a crucial research area because it is essential to conserve fresh water and utilize it wisely. As a part of this study, the reliability of predicting the usage of water in the present and future is investigated in order to develop an effective prediction model to communicate demand. In order to improve prediction, we develop a prediction model and share the updated model with nearby farmers. In order to forecast the irrigation requirements, the recommended model utilizes the Internet of Things (IoT), k-nearest neighbours (KNN), cloud storage, long short-term memory (LSTM), and adaptive network fuzzy inference system (ANFIS) techniques. By collecting real-time environmental data, KNN identifies the closest water requirement from the roots and its surrounding. In order to predict short-term requirements, ANFIS is used. To transfer the new requirements for better prediction, transfer learning is used. Time-series-data updates are predicted using LSTM for future forecasting, and the integrated model is shared with other farmers using cloud environments to enhance forecasting and analysis. For implementation, a period of nine to ten months of data was collected from February to December 2021, and banana tree was used to implement the planned strategy. Four farms, with measurements, were considered at varying intervals to determine the minimum and maximum irrigation needs. The requirements of farms were collected over time and compared to the predictions. Future requirements at 8, 16, 24, 32, and 48 h were also anticipated. The results indicated were compared to manual water pouring, and, thus, the entire crop used less water, making our prediction model a real-world option for irrigation. The prediction model was evaluated using R², MSLE and the average initial prediction value of R² was 0.945. After using transfer learning, the prediction of the model of Farm-2, 3 and 4 were 0.951, 0.958 and 0.967, respectively. Full article