Forecasting vapor pressure deficit for agricultural water management using machine learning in semi-arid environments

Elbeltagi, Ahmed; Srivastava, Aman; Deng, Jinsong; Li, Zhibin; Raza, Ali; Khadke, Leena; Yu, Zhoulu; El-Rawy, Mustafa

doi:10.1016/j.agwat.2023.108302

Cited by 19 publications

(7 citation statements)

References 84 publications

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“…It provides insights into variable importance, aiding in understanding significant factors. The algorithm is robust to overfitting, outliers, and high-dimensional data common in nitrate leaching modelling 106 – 108 .…”

Section: Methodsmentioning

confidence: 99%

Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique

Swain,

Khura,

Sahoo

et al. 2024

Sci Rep

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An accurate assessment of nitrate leaching is important for efficient fertiliser utilisation and groundwater pollution reduction. However, past studies could not efficiently model nitrate leaching due to utilisation of conventional algorithms. To address the issue, the current research employed advanced machine learning algorithms, viz., Support Vector Machine, Artificial Neural Network, Random Forest, M5 Tree (M5P), Reduced Error Pruning Tree (REPTree) and Response Surface Methodology (RSM) to predict and optimize nitrate leaching. In this study, Urea Super Granules (USG) with three different coatings were used for the experiment in the soil columns, containing 1 kg soil with fertiliser placed in between. Statistical parameters, namely correlation coefficient, Mean Absolute Error, Willmott index, Root Mean Square Error and Nash–Sutcliffe efficiency were used to evaluate the performance of the ML techniques. In addition, a comparison was made in the test set among the machine learning models in which, RSM outperformed the rest of the models irrespective of coating type. Neem oil/ Acacia oil(ml): clay/sulfer (g): age (days) for minimum nitrate leaching was found to be 2.61: 1.67: 2.4 for coating of USG with bentonite clay and neem oil without heating, 2.18: 2: 1 for bentonite clay and neem oil with heating and 1.69: 1.64: 2.18 for coating USG with sulfer and acacia oil. The research would provide guidelines to researchers and policymakers to select the appropriate tool for precise prediction of nitrate leaching, which would optimise the yield and the benefit–cost ratio.

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Section: Methodsmentioning

confidence: 99%

Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique

Swain,

Khura,

Sahoo

et al. 2024

Sci Rep

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“…In the context of climate change adaptation in Africa, one compelling case study from Egypt will be assessed here to illuminate the transformative potential of AI-ML technologies. Elbeltagi et al [9] offer valuable insights into the precise estimation of evapotranspiration (ET), a critical factor for effective agricultural water management in water-stressed developing countries amidst climate change (see Figure 3). Specifically, the case study focuses on forecasting vapor pressure deficit (VPD), a key parameter influencing ET calculation.…”

Section: Africamentioning

confidence: 99%

“…This assessment delves into a compelling case study from Egypt, underscoring the broader implications for urban climate change adaptation and sustainable development across Africa. More specifically, the case study by Elbeltagi et al [9] focuses on precise ET estimation, a critical factor for effective agricultural water management, particularly in water-stressed developing countries (see Table 2). The significance of this case study extends beyond Egypt and resonates with urban environments throughout Africa.…”

Section: Africamentioning

confidence: 99%

“…The exploration of AI-ML techniques is essential, more specifically for urban climate change adaptation and sustainable development, due to several compelling reasons that set it apart from traditional methods, as highlighted by Alanzi [6], Taghikhah et al [7], Leal Filho et al [8], Elbeltagi et al [9], and Kumar et al [10]. Firstly, urban environments present intricate and multifaceted climate challenges that demand the capability to handle complex and diverse datasets.…”

Section: Introductionmentioning

confidence: 99%

“…Lastly, the continuous learning ability of AI-ML is vital in the context of climate change, where conditions evolve over time. AI-ML models can adapt and enhance their accuracy as they encounter new data, ensuring that adaptation strategies remain up-to-date and effective [6][7][8][9][10][11][12]. These advantages collectively position AI-ML as a pivotal tool in addressing the challenges posed by climate change in urban areas, making it the method of choice for this study.…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Potential of AI–ML in Urban Climate Change Adaptation and Sustainable Development

Srivastava,

Maity

2023

Sustainability

Self Cite

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This study addresses a notable gap in the climate change literature by examining the potential of artificial intelligence and machine learning (AI–ML) in urban climate change adaptation and sustainable development across major global continents. While much attention has been given to mitigation strategies, this study uniquely delves into the AI–ML’s underexplored role in catalyzing climate change adaptation in contemporary and future urban centers. The research thoroughly explores diverse case studies from Africa, Asia, Australasia, Europe, North America, and South America, utilizing a methodological framework involving six-step and five-step models for systematic literature reviews. The findings underscore AI–ML achievements, illuminate challenges, and emphasize the need for context-specific and collaborative approaches. The findings imply that a one-size-fits-all approach is insufficient. Instead, successful adaptation strategies must be intricately linked to the particular characteristics, vulnerabilities, and intricacies of each region. Furthermore, the research underscores the importance of international collaboration, knowledge sharing, and technology transfer to expedite the integration of AI–ML into climate adaptation strategies globally. The study envisions a promising trajectory for AI–ML in the climate adaptation domain, emphasizing the necessity for ongoing research, innovation, and practical AI–ML applications. As climate change remains a defining challenge, this research predicts an increasingly pivotal role for AI–ML in constructing climate-resilient urban centers and promoting sustainable development. Continuous efforts to advance AI–ML technologies, establish robust policy frameworks, and ensure universal access are crucial for harnessing AI–ML’s transformative capabilities to combat climate change consequences.

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Future of Desalination and Wastewater Treatment Technologies

2024

Materials and Methods for Industrial Wastewater and Groundwater Treatment

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Forecasting vapor pressure deficit for agricultural water management using machine learning in semi-arid environments

Cited by 19 publications

References 84 publications

Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique

Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique

Assessing the Potential of AI–ML in Urban Climate Change Adaptation and Sustainable Development

Future of Desalination and Wastewater Treatment Technologies

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