Water-Quality Prediction Based on H2O AutoML and Explainable AI Techniques

Madni, Hamza Ahmad; Umer, Muhammad; Ishaq, Abid; Abuzinadah, Nihal; Saidani, Oumaima; Alsubai, Shtwai; Hamdi, Monia; Ashraf, Imran

doi:10.3390/w15030475

Cited by 20 publications

(5 citation statements)

References 27 publications

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“…H2O AutoML supports training regression, binary classification, and multi-class classification models on a single machine or a cluster of machines. It trains a Random Forest, an Extremely Randomized Forest, Gradient Boosting Machines (GBMs), Deep Neural Nets, and a grid of GLMs (Madni et al, 2023). Finally, a stacked ensemble model is trained.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Using feature engineering and machine learning in FAO reference evapotranspiration estimation

Považanová,

Čistý,

Bajtek

2023

Journal of Hydrology and Hydromechanics

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The authors of this study investigated the use of machine learning (ML) and feature engineering (FE) techniques to accurately determine FAO reference evapotranspiration (ETo) with a minimal number of climate variables being measured. The recommended techniques for areas with insufficient measurements are based solely on daily temperature readings. Various ML methods were tested to evaluate how sophisticated an ML algorithm is for this task necessary. The main emphasis was on feature engineering, which involves converting raw variables into inputs better suited for ML algorithms, resulting in improved results. FE methods for estimating evapotranspiration include approximations of clear-sky solar radiation based on altitude and Julian day, approximate relative humidity and wind velocity, a categorical month variable, and variables interactions. The authors confirmed that the ability of ML in such tasks is not solely dependent on choosing the suitable algorithm but also on this frequently ignored step. The results of computational experiments are presented, accompanied by a comparison of the proposed method against standard ETo empiric equations. Machine learning methods, mainly due to the transformation of raw variables using FE, provided better results than traditional empirical methods and sophisticated ML algorithms without FE. In addition, the authors tested the applicability of the developed models in the broader area to evaluate the possibility of their generalizability. The potential of this approach to deliver improved predictions, reduced input requirements, and increased efficiency holds interesting promise for optimizing water management strategies, irrigation planning, and decision-making within the agricultural sector.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Using feature engineering and machine learning in FAO reference evapotranspiration estimation

Považanová,

Čistý,

Bajtek

2023

Journal of Hydrology and Hydromechanics

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“…We have used the same input data for both traditional and AutoML models. The AutoML frameworks include tools such as H 2 O-AutoML, DataRobot, Cloud AutoML, the Tree-based Pipeline Optimization Tool (TPOT), Auto-Keras, Auto-Weka, ML BOX, AutoSklearn, and Auto-Pytorch [38][39][40][41]. In this study, we have used the TPOT, which uses a Genetic Algorithm (GA) to automatically find which AI methods present the best performance for the dataset [42].…”

Section: Advancements In Machine Learningmentioning

confidence: 99%

Artificial Intelligence Approach in Aerospace for Error Mitigation

Bautista-Hernández,

Martín-Prats

2024

Aerospace

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Many of the reports created at assembly lines, where all components of an aircraft are installed, frequently indicate that errors threaten safety. The proposed methodology in this study evaluates error prediction and risk mitigation to prevent failures and their consequences. The results linked to a typical electrical harness manufacture of a military aircraft estimated reductions of 93% in time and 90% in error during the creation of engineering manufacturing processes using AI techniques. However, traditional risk assessments methods struggle to identify and mitigate errors effectively. Thus, developing an advanced methodology to ensure systems safety is needed. This paper addresses how innovative AI technology solutions can overcome these challenges, mitigate error risks, and enhance safety in aerospace. Technologies, such as artificial intelligence, predictive algorithms, machine learning, and automation, can play a key role in enhancing safety. The aim of this study is to develop a model that considers the factors that can potentially contribute to error creation, through an artificial intelligence (AI) approach. The specific AI techniques used such as support vector machine, random forest, logistic regression, K-nearest neighbor, and XGBoost (Python 3.8.5) show good performance for use in error mitigation. We have compared the modeled values obtained in this study with the experimental ones. The results confirm that the best metrics are obtained by using support vector machine and logistic regression. The smallest deviation between the measured and modeled values for these AI methods do not exceed 5%. Furthermore, using advancements in machine learning methods can enhance error mitigation in aerospace. The use of AutoML can play a key role in automatically finding an appropriate model which provides the best performance metrics and therefore the most reliable forecast for data prediction and error mitigation.

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“…Water availability in the twenty-rst century is a signi cant issue. Water is indispensable for sustaining life and supporting various ecosystems and human activities (Alsubai et al, 2023; Al-Saedi & Saeed, 2021; Appiah & Asomani-Boateng, 2020). Surface and groundwater are vital resources for human existence and health (Nartey et al, 2009).…”

Section: 0: Introductionmentioning

confidence: 99%

GIS-Based Water Budget Estimation in the Pra Basin using Remote Sensing Data

Ankomahene,

Osah,

Asiedu

2024

Preprint

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Monitoring water demand and water supply is critical for effective and long-term water resource management. In this study, the monthly water budget components for the 2019 and 2022 wet and dry seasons were determined using satellite measurements in the Pra River Basin. The water budget components that were considered include evapotranspiration, rainfall, and water stored on land (terrestrial water storage). Different satellite images, including GRACE, CHIRPS, and AETI from the WaPOR portal, were employed for the study. The result of the study shows that the basin water budget for the 2019 and 2022 dry seasons experienced an increase of 4.973%, while the basin experienced a 39.4522% fall in the 2019 and 2022 wet seasons. The high ET resulted in a negative water budget for the 2019 and 2022 dry and wet seasons. Runoff was calculated as a residual from the water balance equation because it could not be directly determined from remote sensing measurements. Remote sensing datasets can be extremely helpful, especially in areas with limited data, for assessing seasonal and inter-annual changes in water components and river basin management.

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Water-Quality Prediction Based on H2O AutoML and Explainable AI Techniques

Cited by 20 publications

References 27 publications

Using feature engineering and machine learning in FAO reference evapotranspiration estimation

Using feature engineering and machine learning in FAO reference evapotranspiration estimation

Artificial Intelligence Approach in Aerospace for Error Mitigation

GIS-Based Water Budget Estimation in the Pra Basin using Remote Sensing Data

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