Common Machine Learning Techniques Research Articles

Cancer Diagnosis by Gene-Environment Interactions via Combination of SMOTE-Tomek and Overlapped Group Screening Approaches with Application to Imbalanced TCGA Clinical and Genomic Data

The complexity of cancer development involves intricate interactions among multiple biomarkers, such as gene-environment interactions. Utilizing microarray gene expression profile data for cancer classification is anticipated to be effective, thus drawing considerable interest in the fields of bioinformatics and computational biology. Due to the characteristics of genomic data, problems of high-dimensional interactions and noise interference do exist during the analysis process. When building cancer diagnosis models, we often face the dilemma of model adaptation errors due to an imbalance of data types. To mitigate the issues, we apply the SMOTE-Tomek procedure to rectify the imbalance problem. Following this, we utilize the overlapping group screening method alongside a binary logistic regression model to integrate gene pathway information, facilitating the identification of significant biomarkers associated with clinically imbalanced cancer or normal outcomes. Simulation studies across different imbalanced rates and gene structures validate our proposed method’s effectiveness, surpassing common machine learning techniques in terms of classification prediction accuracy. We also demonstrate that prediction performance improves with SMOTE-Tomek treatment compared to no imbalance treatment and SMOTE treatment across various imbalance rates. In the real-world application, we integrate clinical and gene expression data with prior pathway information. We employ SMOTE-Tomek and our proposed methods to identify critical biomarkers and gene-environment interactions linked to the imbalanced binary outcomes (cancer or normal) in patients from the Cancer Genome Atlas datasets of lung adenocarcinoma and breast invasive carcinoma. Our proposed method consistently achieves satisfactory classification accuracy. Additionally, we have identified biomarkers indicative of gene-environment interactions relevant to cancer and have provided corresponding estimates of odds ratios. Moreover, in high-dimensional imbalanced data, for achieving good prediction results, we recommend considering the order of balancing processing and feature screening.

Assessing forest cover changes in Dak Lak province (Central Highlands of Vietnam) from multi-temporal Landsat data and machine learning techniques

Dak Lak is a province in the Central Highlands region of Vietnam, with a large area of forests and forestry land. However, the forest cover has changed dramatically in recent times due to the influence of human activities and climate change. This article presents the results of assessing forest cover changes in Dak Lak province from Landsat satellite image data for the period 2000 – 2020. Three Landsat satellite image scenes, including Landsat 5 TM images taken in March 2000 and February 2010 and Landsat 8 OLI image taken in February 2020 are used to classify forest cover. Three common machine learning techniques, including Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Tree (CART) and the traditional maximum likelihood classification algorithm are used to classify forest cover in the study area, thereby choosing the method with the highest accuracy. The results show that the RF algorithm has the highest accuracy in classifying forest cover from multi-temporal Landsat images by comparing the overall accuracy value and the Kappa coefficient. The obtained results are used to build forest cover change maps in the period 2000 - 2010, 2010 - 2020 and 2000 - 2020. The results received in the study provide information to help managers in monitoring and protecting forest resources.

Dealing with Drift of Adaptation Spaces in Learning-based Self-Adaptive Systems Using Lifelong Self-Adaptation

Recently, machine learning (ML) has become a popular approach to support self-adaptation. ML has been used to deal with several problems in self-adaptation, such as maintaining an up-to-date runtime model under uncertainty and scalable decision-making. Yet, exploiting ML comes with inherent challenges. In this article, we focus on a particularly important challenge for learning-based self-adaptive systems: drift in adaptation spaces. With adaptation space, we refer to the set of adaptation options a self-adaptive system can select from to adapt at a given time based on the estimated quality properties of the adaptation options. A drift of adaptation spaces originates from uncertainties, affecting the quality properties of the adaptation options. Such drift may imply that the quality of the system may deteriorate, eventually, no adaptation option may satisfy the initial set of adaptation goals, or adaptation options may emerge that allow enhancing the adaptation goals. In ML, such a shift corresponds to a novel class appearance, a type of concept drift in target data that common ML techniques have problems dealing with. To tackle this problem, we present a novel approach to self-adaptation that enhances learning-based self-adaptive systems with a lifelong ML layer. We refer to this approach as lifelong self-adaptation . The lifelong ML layer tracks the system and its environment, associates this knowledge with the current learning tasks, identifies new tasks based on differences, and updates the learning models of the self-adaptive system accordingly. A human stakeholder may be involved to support the learning process and adjust the learning and goal models. We present a general architecture for lifelong self-adaptation and apply it to the case of drift of adaptation spaces that affects the decision-making in self-adaptation. We validate the approach for a series of scenarios with a drift of adaptation spaces using the DeltaIoT exemplar.

ESTSS—energy system time series suite: a declustered, application-independent, semi-artificial load profile benchmark set

This paper introduces an univariate application-independent set of load profiles or time series derived from real-world energy system data. The generation involved a two-step process: manifolding the initial dataset through signal processors to increase diversity and heterogeneity, followed by a declustering process that removes data redundancy. The study employed common feature engineering and machine learning techniques: the time series are transformed into a normalized feature space, followed by a dimensionality reduction via hierarchical clustering, and optimization. The resulting dataset is uniformly distributed across multiple feature space dimensions while retaining typical time and frequency domain characteristics inherent in energy system time series. This data serves various purposes, including algorithm testing, uncovering functional relationships between time series features and system performance, and training machine learning models. Two case studies demonstrate the claims: one focused on the suitability of hybrid energy storage systems and the other on quantifying the onsite hydrogen supply cost in green hydrogen production sites. The declustering algorithm, although a bys study, shows promise for further scientific exploration. The data and source code are openly accessible, providing a robust platform for future comparative studies. This work also offers smaller subsets for computationally intensive research. Data and source code can be found at https://github.com/s-guenther/estss and https://zenodo.org/records/10213145.

The design and implementation of a deep reinforcement learning and quantum finance theory-inspired portfolio investment management system

Deep Learning (DL) and Reinforcement Learning (RL) are common machine learning techniques used in automatic trading, notwithstanding, RL is deficient in portfolio investment in terms of funds distribution, potential loss control, profit maximization, and examine undetected environment. This paper proposed an intelligent Quantum Finance-based portfolio investment system (QFPIS), which is a combination of Deep Reinforcement Learning (DRL) with Quantum Finance Theory (QFT) to improve these conditions. There are two major agents embodied in the system: 1) a trading agent based on Deep Deterministic Policy Gradient algorithm to determine investment weighting for different financial products by generating continuous actions; 2) an intelligent agent based on Policy Gradient (PG) algorithm to enact risk control and determine whether to hold current orders by producing discrete actions depend on daily Quantum Price Levels (QPLs). The advantages of incorporating a two-agents system design are to devise stable and realistic fund allocation for different products in portfolio. Experiment results had shown robustness, flexibility, and profitability on a series of forex products and the U.S. stocks in the back-testing phase as compared to other RL trading systems.

Machine learning for leaf disease classification: data, techniques and applications

The growing demand for sustainable development brings a series of information technologies to help agriculture production. Especially, the emergence of machine learning applications, a branch of artificial intelligence, has shown multiple breakthroughs which can enhance and revolutionize plant pathology approaches. In recent years, machine learning has been adopted for leaf disease classification in both academic research and industrial applications. Therefore, it is enormously beneficial for researchers, engineers, managers, and entrepreneurs to have a comprehensive view about the recent development of machine learning technologies and applications for leaf disease detection. This study will provide a survey in different aspects of the topic including data, techniques, and applications. The paper will start with publicly available datasets. After that, we summarize common machine learning techniques, including traditional (shallow) learning, deep learning, and augmented learning. Finally, we discuss related applications. This paper would provide useful resources for future study and application of machine learning for smart agriculture in general and leaf disease classification in particular.

Performance of advanced machine learning algorithms overlogistic regression in predicting hospital readmissions: A meta-analysis

ObjectivesMachine learning algorithms are being increasingly used for predicting hospital readmissions. This meta-analysis evaluated the performance of logistic regression (LR) and machine learning (ML) models for the prediction of 30-day hospital readmission among patients in the US. MethodsElectronic databases (i.e., Medline, PubMed, and Embase) were searched from January 2015 to December 2019. Only studies in the English language were included. Two reviewers performed studies screening, quality appraisal, and data collection. The quality of the studies was assessed using the Quality in Prognosis Studies (QUIPS) tool. Model performance was evaluated using the Area Under the Curve (AUC). A random-effects meta-analysis was performed using STATA 16. ResultsNine studies were included based on the selection criteria. The most common ML techniques were tree-based methods such as boosting and random forest. Most of the studies had a low risk of bias (8/9). The AUC was greater with ML to predict 30-day all-cause hospital readmission compared with LR [Mean Difference (MD): 0.03; 95% Confidence Interval (CI) 0.01–0.05]. Subgroup analyses found that deep-learning methods had a better performance compared with LR (MD 0.06; 95% CI, 0.04–0.09), followed by neural networks (MD: 0.03; 95% CI, 0.03–0.03), while the AUCs of the tree-based (MD: 0.02; 95% CI -0.00-0.04) and kernel-based (MD: 0.02; 95% CI 0.02 (−0.13–0.16) methods were no different compared to LR. More than half of the studies evaluated heart failure-related rehospitalization (N = 5). For the readmission prediction among heart failure patients, ML performed better compared with LR, with a mean difference in AUC of 0.04 (95% CI, 0.01–0.07). The leave-one-out sensitivity analysis confirmed the robustness of the findings. ConclusionMultiple ML methods were used to predict 30-day all-cause hospital readmission. Performance varied across the ML methods, with deep-learning methods showing the best performance over the LR.

Combinatorial testing and machine learning for automated test generation

In this issue, we are pleased to present two papers that showcase innovative techniques in software testing in two different directions (memory-aware combinatorial test generation and survey about the use of machine learning for automated test generation). The first paper, ‘An investigation of distributed computing for combinatorial testing’ by Edmond La Chance and Sylvain Hallé, proposes the use of distributed computing to reduce the time and memory required for t-way test generation. The authors present a distributed graph colouring method and a distributed hypergraph vertex covering method for generating high-quality test suites. They also demonstrate how to build a distributed IPOG algorithm using these methods. (Recommended by Arnaud Gotlieb) The second paper, ‘The Integration of Machine Learning into Automated Test Generation: A Systematic Mapping Study’ by Afonso Fontes and Gregory Gay, investigates the integration of machine learning (ML) into automated test generation. Through a systematic study of 124 papers, the authors characterize the emerging research in this area and identify the testing practices, researcher goals, ML techniques applied, evaluation metrics, and challenges in integrating ML into testing. The results show that ML is used to generate inputs for different types of testing, improve the performance of existing generation methods, and generate test verdicts and oracles. The authors also identify common ML techniques used in this area, such as supervised and reinforcement learning, and the evaluation metrics used to assess the effectiveness of these techniques. (Recommended by Phil McMinn) The first paper contributes to the literature on combinatorial testing by demonstrating the effectiveness of distributed computing for test generation. The second paper highlights the potential of ML in automated test generation and provides insights into the challenges that researchers face when integrating ML into testing. We hope that these papers will inspire further research in these directions of software testing.

Machine-Learning Approaches for the Discovery of Electrolyte Materials for Solid-State Lithium Batteries

Solid-state lithium batteries have attracted considerable research attention for their potential advantages over conventional liquid electrolyte lithium batteries. The discovery of lithium solid-state electrolytes (SSEs) is still undergoing to solve the remaining challenges, and machine learning (ML) approaches could potentially accelerate the process significantly. This review introduces common ML techniques employed in materials discovery and an overview of ML applications in lithium SSE discovery, with perspectives on the key issues and future outlooks.

Privacy Preserving and Time Series Analysis of Medical Dataset using Deep Feature Selection

A significant category of medical data that includes rich temporal and spatial information is time-series medical imaging. Since then, experts in a variety of domains, including clinical picture analysis, have been actively participating in the rapidly emerging subject of profound learning. This paper discusses profound learning processes and their applicability to clinical picture examination and mainly focused common machine learning techniques in the field of computer vision and how deep learning has transformed ML, ML models for deep learning and applications of deep learning to clinical image analysis. In fact, even before the term "deep learning" was coined, a variety of clinical picture investigation concerns, including harm and non-harm grouping, harm type characterisation, harm or organ division, and injury recognition, were addressed using picture input machine learning (PIML). Deep learning is predicted to be the key innovation for clinical picture examination in the upcoming few years. Picture input ML, including profound learning, is an exceptionally powerful, flexible, higher-throughput innovation that can raise the current level of execution in clinical picture examination. "Profound learning" or picture input ML, in clinical picture examination is a quickly developing, promising field. Picture input ML is supposed to turn into a significant field in clinical picture examination in the following couple of many years.

Machine learning-based prediction of intraoperative hypoxemia for pediatric patients.

Reducing the duration of intraoperative hypoxemia in pediatric patients by means of rapid detection and early intervention is considered crucial by clinicians. We aimed to develop and validate a machine learning model that can predict intraoperative hypoxemia events 1 min ahead in children undergoing general anesthesia. This retrospective study used prospectively collected intraoperative vital signs and parameters from the anesthesia ventilator machine extracted every 2 s in pediatric patients undergoing surgery under general anesthesia between January 2019 and October 2020 in a tertiary academic hospital. Intraoperative hypoxemia was defined as oxygen saturation <95% at any point during surgery. Three common machine learning techniques were employed to develop models using the training dataset: gradient-boosting machine (GBM), long short-term memory (LSTM), and transformer. The performances of the models were compared using the area under the receiver operating characteristics curve using randomly assigned internal testing dataset. We also validated the developed models using temporal holdout dataset. Pediatric patient surgery cases between November 2020 and January 2021 were used. The performances of the models were compared using the area under the receiver operating characteristic curve (AUROC). In total, 1,540 (11.73%) patients with intraoperative hypoxemia out of 13,130 patients' records with 2,367 episodes were included for developing the model dataset. After model development, 200 (13.25%) of the 1,510 patients' records with 289 episodes were used for holdout validation. Among the models developed, the GBM had the highest AUROC of 0.904 (95% confidence interval [CI] 0.902 to 0.906), which was significantly higher than that of the LSTM (0.843, 95% CI 0.840 to 0.846 P < .001) and the transformer model (0.885, 95% CI, 0.882-0.887, P < .001). In holdout validation, GBM also demonstrated best performance with an AUROC of 0.939 (95% CI 0.936 to 0.941) which was better than LSTM (0.904, 95% CI 0.900 to 0.907, P < .001) and the transformer model (0.929, 95% CI 0.926 to 0.932, P < .001). Machine learning models can be used to predict upcoming intraoperative hypoxemia in real-time based on the biosignals acquired by patient monitors, which can be useful for clinicians for prediction and proactive treatment of hypoxemia in an intraoperative setting.

A sampling and learning framework to prove motion planning infeasibility

We present a learning-based approach to prove infeasibility of kinematic motion planning problems. Sampling-based motion planners are effective in high-dimensional spaces but are only probabilistically complete. Consequently, these planners cannot provide a definite answer if no plan exists, which is important for high-level scenarios, such as task-motion planning. We apply data generated during multi-directional sampling-based planning (such as PRM) to a machine learning approach to construct an infeasibility proof. An infeasibility proof is a closed manifold in the obstacle region of the configuration space that separates the start and goal into disconnected components of the free configuration space. We train the manifold using common machine learning techniques and then triangulate the manifold into a polytope to prove containment in the obstacle region. Under assumptions about the hyper-parameters and robustness of configuration space optimization, the output is either an infeasibility proof or a motion plan in the limit. We demonstrate proof construction for up to 4-DOF configuration spaces. A large part of the algorithm is parallelizable, which offers potential to address higher dimensional configuration spaces.

Materials informatics of woven fabric composites: Effect of different dimensionality reduction and learning methods

A microscale data-driven framework, under the emerging Materials Informatics paradigm, is developed to build structure-property linkages of woven composites. To show the approach, the microstructure of a set of glass fiber-reinforced polymer samples are imaged using micro-computed tomography, with the property of interest being the elastic modulus obtained from tensile tests. After data collection, fingerprints of microstructural images are calculated by means of 2-point spatial correlations. Different dimensionality reduction algorithms are next employed and compared to build reduced-order representations of the ensuing heatmaps and subsequently identify the dominant features for each composite sample through the Least Absolute Shrinkage and Selection Operator (LASSO) regularization. The tested dimensionality reduction algorithms include Principal Component Analysis (PCA), Non-negative Matrix Factorization (NMF), and Partial Least Squares (PLS). Using the dominant features, different structure-property surrogate models are built and compared via a set of common machine learning techniques: Multiple Linear Regression (MLR), Random Forest Regression (RFR), Support Vector Regression (SVR), and Gaussian Process Regression (GPR). In addition, a Linear Mixed-effect Model (LMM) is developed to account for potential random effects taking place during the fabrication of samples. In this case study, the best performing models were RFR and PLS, with the latter containing components well correlated with the material geometrical attributes such as fiber orientation and fiber volume fraction. This preliminary Composites Informatics case study would furnish the path for a non-destructive property-microstructure prediction of woven fabrics, as well as data-driven discovery and design of new reinforcements.

Face detection and grimace scale prediction of white furred mice

Studying the facial expressions of humans has been one of the major applications of computer vision. An open question is whether common machine learning techniques can also be used to track behaviors of animals, which is a less explored research problem. Since animals are not capable of verbal communication, computer vision solutions can provide valuable information to track the animal’s state. We are particularly interested in pain neurobiology research, where rodent models are extensively used to investigate pain interventions. A grimace scale is used to understand the suffering of a mouse in the presence of interventions, which is inferred from various facial features such as the shape of the eyes and ears. In this work, we automate the prediction of the grimace scale on white furred mice using a machine learning approach, following the same principles used for human facial expression recognition: face detection, landmark region extraction, and expression recognition. We demonstrate the use of the you only look once (YOLO) framework for face detection of the mice with outstanding results. For eye region extraction and grimace pain prediction, we propose a novel structure based on a dilated convolutional network. The experimental results are promising, showing that it is possible to differentiate among the pain scale of the mice.

Parkinson's Disease Detection from Resting-State EEG Signals Using Common Spatial Pattern, Entropy, and Machine Learning Techniques.

Parkinson’s disease (PD) is a very common brain abnormality that affects people all over the world. Early detection of such abnormality is critical in clinical diagnosis in order to prevent disease progression. Electroencephalography (EEG) is one of the most important PD diagnostic tools since this disease is linked to the brain. In this study, novel efficient common spatial pattern-based approaches for detecting Parkinson’s disease in two cases, off–medication and on–medication, are proposed. First, the EEG signals are preprocessed to remove major artifacts before spatial filtering using a common spatial pattern. Several features are extracted from spatially filtered signals using different metrics, namely, variance, band power, energy, and several types of entropy. Machine learning techniques, namely, random forest, linear/quadratic discriminant analysis, support vector machine, and k-nearest neighbor, are investigated to classify the extracted features. The impacts of frequency bands, segment length, and reduction number on the results are also investigated in this work. The proposed methods are tested using two EEG datasets: the SanDiego dataset (31 participants, 93 min) and the UNM dataset (54 participants, 54 min). The results show that the proposed methods, particularly the combination of common spatial patterns and log energy entropy, provide competitive results when compared to methods in the literature. The achieved results in terms of classification accuracy, sensitivity, and specificity in the case of off-medication PD detection are around 99%. In the case of on-medication PD, the results range from 95% to 98%. The results also reveal that features extracted from the alpha and beta bands have the highest classification accuracy.

Applications of Machine Learning in Aircraft Maintenance

Aircraft maintenance is an expansive multidisciplinary field which entails robust design and optimization of extensive maintenance operations and procedures; encompassing the fault identification, detection and rectification, and overhauling, repair or modification of aircraft systems, subsystems, and components, as well as the scheduling for various maintenance operations, in compliance with the aviation standards; in order to predict, pre-empt and prevent failures and thus ensure the continual reliability of aircraft. Advances in Big Data Analytics (BDA) and artificial intelligence techniques have revolutionized predictive maintenance operations. Predictive maintenance is making big strides in the aerospace sector accompanied by a variety of prognostic health management options. Artificial intelligence algorithms have recently been extensively applied to optimize aircraft maintenance systems and operations. Several researchers have proposed, analysed, and investigated the applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) based data analytics for predictive maintenance of aircraft systems, subsystems, and components. This paper provides a comprehensive review of the ML techniques like Multilayer Perceptron (MLP), Logic Regression (LR), Random Forest (RF), Artificial Neural Network (ANN), Support Vector Regression (SVR), Linear Regression (LR), and other common ML techniques for their present implementation and potential future applications in aircraft maintenance.

Advanced Machine Learning Methods for Learning from Sparse Data in High-Dimensional Spaces: A Perspective on Uses in the Upstream of Development of Novel Energy Technologies

Machine learning (ML) has found increasing use in physical sciences, including research on energy conversion and storage technologies, in particular, so-called sustainable technologies. While often ML is used to directly optimize the parameters or phenomena of interest in the space of features, in this perspective, we focus on using ML to construct objects and methods that help in or enable the modeling of the underlying phenomena. We highlight the need for machine learning from very sparse and unevenly distributed numeric data in multidimensional spaces in these applications. After a brief introduction of some common regression-type machine learning techniques, we focus on more advanced ML techniques which use these known methods as building blocks of more complex schemes and thereby allow working with extremely sparse data and also allow generating insight. Specifically, we will highlight the utility of using representations with subdimensional functions by combining the high-dimensional model representation ansatz with machine learning methods such as neural networks or Gaussian process regressions in applications ranging from heterogeneous catalysis to nuclear energy.

Comparison Analysis of Traditional Machine Learning and Deep Learning Techniques for Data and Image Classification

The purpose of the study is to analyse and compare the most common machine learning and deep learning techniques used for computer vision 2D object classification tasks. Firstly, we will present the theoretical background of the Bag of Visual words model and Deep Convolutional Neural Networks (DCNN). Secondly, we will implement a Bag of Visual Words model, the VGG16 CNN Architecture. Thirdly, we will present our custom and novice DCNN in which we test the aforementioned implementations on a modified version of the Belgium Traffic Sign dataset. Our results showcase the effects of hyperparameters on traditional machine learning and the advantage in terms of accuracy of DCNNs compared to classical machine learning methods. As our tests indicate, our proposed solution can achieve similar - and in some cases better - results than existing DCNNs architectures. Finally, the technical merit of this article lies in the presented computationally simpler DCNN architecture, which we believe can pave the way towards using more efficient architectures for basic tasks.

Correlating Time-Resolved Pressure Measurements With Rim Sealing Effectiveness for Real-Time Turbine Health Monitoring

Abstract Purge flow is bled from the upstream compressor and supplied to the under-platform region to prevent hot main gas path ingress that damages vulnerable under-platform hardware components. A majority of turbine rim seal research has sought to identify methods of improving sealing technologies and understanding the physical mechanisms that drive ingress. While these studies directly support the design and analysis of advanced rim seal geometries and purge flow systems, the studies are limited in their applicability to real-time monitoring required for condition-based operation and maintenance. As operational hours increase for in-service engines, this lack of rim seal performance feedback results in progressive degradation of sealing effectiveness, thereby leading to reduced hardware life. To address this need for rim seal performance monitoring, this study utilizes measurements from a one-stage turbine research facility operating with true-scale engine hardware at engine-relevant conditions. Time-resolved pressure measurements collected from the rim seal region are regressed with sealing effectiveness through the use of common machine learning techniques to provide real-time feedback of sealing effectiveness. Two modeling approaches are presented that use a single sensor to predict sealing effectiveness accurately over a range of two turbine operating conditions. Results show that an initial purely data-driven model can be further improved using domain knowledge of relevant turbine operations, which yields sealing effectiveness predictions within 3% of measured values.

Flood Early Warning Systems Using Machine Learning Techniques: The Case of the Tomebamba Catchment at the Southern Andes of Ecuador

Worldwide, machine learning (ML) is increasingly being used for developing flood early warning systems (FEWSs). However, previous studies have not focused on establishing a methodology for determining the most efficient ML technique. We assessed FEWSs with three river states, No-alert, Pre-alert and Alert for flooding, for lead times between 1 to 12 h using the most common ML techniques, such as multi-layer perceptron (MLP), logistic regression (LR), K-nearest neighbors (KNN), naive Bayes (NB), and random forest (RF). The Tomebamba catchment in the tropical Andes of Ecuador was selected as a case study. For all lead times, MLP models achieve the highest performance followed by LR, with f1-macro (log-loss) scores of 0.82 (0.09) and 0.46 (0.20) for the 1 h and 12 h cases, respectively. The ranking was highly variable for the remaining ML techniques. According to the g-mean, LR models correctly forecast and show more stability at all states, while the MLP models perform better in the Pre-alert and Alert states. The proposed methodology for selecting the optimal ML technique for a FEWS can be extrapolated to other case studies. Future efforts are recommended to enhance the input data representation and develop communication applications to boost the awareness of society of floods.