Hybrid Machine Learning Model Research Articles

Enhancing Arabic Dialect Detection on Social Media: A Hybrid Model with an Attention Mechanism

Recently, the widespread use of social media and easy access to the Internet have brought about a significant transformation in the type of textual data available on the Web. This change is particularly evident in Arabic language usage, as the growing number of users from diverse domains has led to a considerable influx of Arabic text in various dialects, each characterized by differences in morphology, syntax, vocabulary, and pronunciation. Consequently, researchers in language recognition and natural language processing have become increasingly interested in identifying Arabic dialects. Numerous methods have been proposed to recognize this informal data, owing to its crucial implications for several applications, such as sentiment analysis, topic modeling, text summarization, and machine translation. However, Arabic dialect identification is a significant challenge due to the vast diversity of the Arabic language in its dialects. This study introduces a novel hybrid machine and deep learning model, incorporating an attention mechanism for detecting and classifying Arabic dialects. Several experiments were conducted using a novel dataset that collected information from user-generated comments from Twitter of Arabic dialects, namely, Egyptian, Gulf, Jordanian, and Yemeni, to evaluate the effectiveness of the proposed model. The dataset comprises 34,905 rows extracted from Twitter, representing an unbalanced data distribution. The data annotation was performed by native speakers proficient in each dialect. The results demonstrate that the proposed model outperforms the performance of long short-term memory, bidirectional long short-term memory, and logistic regression models in dialect classification using different word representations as follows: term frequency-inverse document frequency, Word2Vec, and global vector for word representation.

Performance Evaluation of Hybrid Machine Learning Algorithms for Online Lending Credit Risk Prediction

ABSTRACT Peer-to-Peer systems are still in the early stages of development when it comes to the processing of credit and the appraisal of the risk associated with it. In this study, we used a hybrid convolutional neural network with logistic regression, a gradient-boosting decision tree, and a k-nearest neighbor to predict the credit risk in a P2P lending club. The lending clubs publicly available P2P loan data was used to train the model. In order to address the issue of data imbalance within the dataset, specifically between the non-defaulter and defaulter classes, the synthetic minority oversampling technique sampling approach is utilized. We developed the architecture of our hybrid model by removing the fully connected layer with the soft-max, which is the final layer of the fully connected CNN model and replaced by LR, GBDT, and k-NN algorithms. The experimental results show that the hybrid CNN-kNN model outperforms the CNN-GBDT and CNN-LR models based on the performance metrics accuracy, recall, F1-score, and area under the curve for both all input and important features. This shows that hybrid machine learning models effectively identify and categorize credit risk in peer-to-peer lending clubs, hence assisting in financial loss prevention.

Hybrid machine learning model based predictions for properties of poly(2-hydroxyethyl methacrylate)-poly(vinyl alcohol) composite cryogels embedded with bacterial cellulose

Supermacroporous composite cryogels with enhanced adjustable functionality have received extensive interest in bioseparation, tissue engineering, and drug delivery. However, the variations in their components significantly impactfinal properties. This study presents a two-step hybrid machine learning approach for predicting the properties of innovative poly(2-hydroxyethyl methacrylate)-poly(vinyl alcohol) composite cryogels embedded with bacterial cellulose (pHEMA-PVA-BC) based on their compositions. By considering the ratios of HEMA (1.0–22.0 wt%), PVA (0.2–4.0 wt%), poly(ethylene glycol) diacrylate (1.0–4.5 wt%), BC (0.1–1.5 wt%), and water (68.0–96.0 wt%) as investigational variables, overlay sampling uniform design (OSUD) was employed to construct a high-quality dataset for model development. The random forest (RF) model was used to classify the preparation conditions. Then four models of artificial neural network, RF, gradient boosted regression trees (GBRT), and XGBoost were developed to predict the basic properties of the composite cryogels. The results showed that the RF model achieved an accurate three-class classification of preparation conditions. Among the four models, the GBRT model exhibited the best predictive performance of the basic properties, with the mean absolute percentage error of 16.04 %, 0.85 %, and 2.44 % for permeability, effective porosity, and height of theoretical plate (1.0 cm/min), respectively. Characterization results of the representative pHEMA-PVA-BC composite cryogel showed an effective porosity of 81.01 %, a permeability of 1.20 × 10−12 m2, and a range of height of theoretical plate between 0.40–0.49 cm at flow velocities of 0.5–3.0 cm/min. These indicate that the pHEMA-PVA-BC cryogel was an excellent material with supermacropores, low flow resistance and high mass transfer efficiency. Furthermore, the model output demonstrates that the alteration of the proportions of PVA (0.2–3.5 wt%) and BC (0.1–1.5 wt%) components in composite cryogels resulted in significant changes in the material basic properties. This work represents an attempt to efficiently design and prepare target composite cryogels using machine learning and providing valuable insights for the efficient development of polymers.

Novel neural network-based metaheuristic models for the stability prediction of rectangular trapdoors in anisotropic and non-homogeneous clay

The problem of trapdoor stability is a crucial problem in geotechnical engineering. This study is the first to introduce novel neural network-based metaheuristic models for the stability prediction of 3D rectangular trapdoors in anisotropic and nonhomogeneous clays. However, no researcher has considered such trapdoor problems in the past. In this study, the dataset is obtained by using finite element limit analysis (FELA). The proposed hybrid machine learning models based on artificial neural networks (ANNs) and various types of optimization algorithms (OAs), namely, ant colony optimization (ACO), artificial lion optimization (ALO), the imperialist competition algorithm (ICA), and shuffled complex evolution (SCE), are also proposed in this paper by undergoing rigorous optimization to ensure accuracy and efficiency in capturing the intricate dynamics of the stability investigations from the models. The performance of the proposed ANN-based models is assessed using several performance metrics, regression plots, and rank analysis. The proposed ANN-SCE model outperforms the other proposed models in predicting trapdoor stability, where the ANN-SCE model achieved the highest rank, with a score of 58, followed by the ANN-ALO (47), ANN-ICA (33), and ANN-ACO (22) models. The proposed neural network-based metaheuristic models deliver precise and effective forecasts of trapdoor stability to make informed decisions concerning road design and mitigation tactics, ultimately improving the robustness of infrastructure facing geotechnical challenges.

A Novel Approaches to Crop Variety Development for Sustainable Agriculture using Hybrid Machine Learning Model

The growing global population and changing climate need sustainable agriculture practices and new crop varieties. This study proposes a hybrid machine learning technique that combines linear regression and logistic regression to identify and extract unique traits from a broad range of crop species. The system integrates these qualities to create new crop types with a variety of benefits. The system begins by collecting detailed data on multiple crop species, including production potential, drought resilience, disease tolerance, nutritional content, and adaptation to different climates. Preprocessing ensures data quality. The hybrid machine learning algorithm uses the revised dataset. Linear regression identifies variables with strong connections with the required attributes, making it easier to find good candidates for the new crop variety. Logistic regression analyzes categorical data, including disease resistance genes and nutritional variables that affect crop quality. The method efficiently identifies and ranks the best characteristics across varied crop species by combining regression model findings. The traits have been strategically combined to create a pioneering cultivar that optimizes crop productivity, robustness, nutritional composition, and versatility while reducing vulnerability to pathogenic diseases and ecological challenges. The innovative crop variety is tested in various agricultural situations to determine the system's effectiveness. The topic under inquiry is compared to current kinds in terms of yield, quality, and sustainability. This unique hybrid machine learning technology may combine the best qualities from multiple sources to develop cutting-edge agricultural cultivars. The system's speed and accuracy in identifying beneficial traits promotes sustainable agriculture by reducing monospecific breeding and increasing crop resilience to environmental challenges. This work advances global food security and resource-efficient crop varieties.

Stacked hybridization to enhance the performance of artificial neural networks (ANN) for prediction of water quality index in the Bagh river basin, India

Water quality assessment is paramount for environmental monitoring and resource management, particularly in regions experiencing rapid urbanization and industrialization. This study introduces Artificial Neural Networks (ANN) and its hybrid machine learning models, namely ANN-RF (Random Forest), ANN-SVM (Support Vector Machine), ANN-RSS (Random Subspace), ANN-M5P (M5 Pruned), and ANN-AR (Additive Regression) for water quality assessment in the rapidly urbanizing and industrializing Bagh River Basin, India. The Relief algorithm was employed to select the most influential water quality input parameters, including Nitrate (NO3−), Magnesium (Mg2+), Sulphate (SO42−), Calcium (Ca2+), and Potassium (K+). The comparative analysis of developed ANN and its hybrid models was carried out using statistical indicators (i.e., Nash-Sutcliffe Efficiency (NSE), Pearson Correlation Coefficient (PCC), Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Root Square Error (RRSE), Relative Absolute Error (RAE), and Mean Bias Error (MBE)) and graphical representations (i.e., Taylor diagram). Results indicate that the integration of support vector machine (SVM) with ANN significantly improves performance, yielding impressive statistical indicators: NSE (0.879), R2 (0.904), MAE (22.349), and MBE (12.548). The methodology outlined in this study can serve as a template for enhancing the predictive capabilities of ANN models in various other environmental and ecological applications, contributing to sustainable development and safeguarding natural resources.

MLm5C: A high-precision human RNA 5-methylcytosine sites predictor based on a combination of hybrid machine learning models

RNA modification serves as a pivotal component in numerous biological processes. Among the prevalent modifications, 5-methylcytosine (m5C) significantly influences mRNA export, translation efficiency and cell differentiation and are also associated with human diseases, including Alzheimer’s disease, autoimmune disease, cancer, and cardiovascular diseases. Identification of m5C is critically responsible for understanding the RNA modification mechanisms and the epigenetic regulation of associated diseases. However, the large-scale experimental identification of m5C present significant challenges due to labor intensity and time requirements. Several computational tools, using machine learning, have been developed to supplement experimental methods, but identifying these sites lack accuracy and efficiency. In this study, we introduce a new predictor, MLm5C, for precise prediction of m5C sites using sequence data. Briefly, we evaluated eleven RNA sequence-derived features with four basic machine learning algorithms to generate baseline models. From these 44 models, we ranked them based on their performance and subsequently stacked the Top 20 baseline models as the best model, named MLm5C. The MLm5C outperformed the-state-of-the-art predictors. Notably, the optimization of the sequence length surrounding the modification sites significantly improved the prediction performance. MLm5C is an invaluable tool in accelerating the detection of m5C sites within the human genome, thereby facilitating in the characterization of their roles in post-transcriptional regulation.

Prediction of compressive strength of glass powder concrete based on artificial intelligence

The investigation of glass powder concrete (GPC) is motivated by the imperative to alleviate the pronounced environmental impact associated with conventional concrete construction and untreated waste glass. Compressive strength (CS) is a crucial criterion for assessing concrete quality. Traditionally, determining compressive strength relies on resource-intensive compression tests. This study addresses these challenges by evaluating GPC's compressive strength using fundamental and hybrid machine learning models. The study employed four machine learning models: Backpropagation Neural Network (BPNN), Extreme Gradient Boosting (XGB), Support Vector Regression (SVR), and Random Forest Regression (RFR). Additionally, four hybrid models (SSA-BPNN, SSA-XGB, SSA-SVR, SSA-RFR) were devised using the Sparrow Search Algorithm (SSA) to forecast compressive strength. Input variables included cement content, moisture content, sand content, coarse aggregate content, glass powder (GP) content, and curing age. The study reveals that optimized models generally outperform baseline models in predicting accuracy. Specifically, SSA-XGB demonstrated the highest accuracy, with R2 = 0.9645 and RMSE = 3.0640 MPa. Feature importance analysis highlights cement content and curing age as the most influential variables affecting GPC. Two-dimensional partial dependence plots (PDP) elucidate the interrelationship between GP, curing age, and cement content, confirming that, due to the volcanic ash effect and increased alkalinity, GP makes a more substantial contribution to strength. In summary, this study provides systematic recommendations for forecasting GPC, contributing significantly to both literature and practical applications. And the database containing 1045 samples will be disclosed in Appendix Afor researchers to use.

Cross-Platform Bug Localization Strategies: Utilizing Machine Learning for Diverse Software Environment Adaptability

This paper introduces a novel hybrid machine learning model that combines Long Short-Term Memory (LSTM) networks and SHapley Additive exPlanations (SHAP) to enhance bug localization across multiple software platforms. The aim is to adapt to the variability inherent in different operating systems and provide transparent, interpretable results for software developers. Our methodology includes comprehensive preprocessing of bug report data using advanced natural language processing techniques, followed by feature extraction through word embeddings to accommodate the sequential nature of text data. The LSTM model is trained and evaluated on a dataset of simulated bug reports, with the results interpreted using SHAP values to ensure clarity in decision-making. The results demonstrate the model’s robustness, adaptability, and consistent performance across platforms, as evidenced by accuracy, precision, recall, and F1 scores. The dataset's distribution of bug categories and statuses further provides valuable insights into common software development issues.

A hybrid machine learning model for sentiment analysis and satisfaction assessment with Turkish universities using Twitter data

Social media platforms, such as X (Twitter), contain enormous amounts of data, and collecting valuable information from that data assists in making more informed decisions. In recent years, governments and institutions have begun to explore the possibilities of utilizing social networks as a platform to supply and enhance the quality of their services. Consequently, there is an increased demand to estimate people’s satisfaction with Turkish Universities written in Turkish wherever it is challenging and requires more effort to cope with its rich morphological structure to perform a Sentiment Analysis task. This study proposes a Turkish text sentiment analysis methodology for estimating people’s satisfaction with Turkish Universities by employing nine conventional machine learning models, deep learning techniques, and BERT-based transformers upon an original manually annotated dataset consisting of 17,793 tweets in Turkish. An innovative hybrid architecture, named BERT-BiLSTM-CNN, integrates Bidirectional Encoder Representations from Transformers (BERT), Bidirectional Long Short-Term Memory (BiLSTM), and a triple parallel Convolutional Neural Network (CNN) branch, achieving exceptional accuracy in sentiment analysis of Turkish tweets. During testing, the proposed architecture revealed an impressive accuracy rate of over 0.9101, an F1 Score of 0.8801, and a Receiver Operating Characteristic (ROC) of 0.9632 for analyzing sentiment, demonstrating that the model outperformed the state-of-the-art models, and it is capable of coping with the linguistic complexities of Turkish sentiment analysis. This work provides new insights into sentiment analysis by proposing a hybrid model that combines several computational methodologies to improve the understanding of attitudes.

A comparative analysis of feature selection models for spatial analysis of floods using hybrid metaheuristic and machine learning models.

The research aims to propose a feature selection model for hydraulic analysis as such a model has not been proposed previously. For this purpose, hybrids of three metaheuristic algorithms, particle swarm optimization (PSO), ant colony optimization (ACO), and genetic algorithm (GA) with two machine learning models which are support vector machine (SVM) and K-nearest neighbor (KNN) are employed. The dataset considered was hydraulic having an association with flood and possessed topographic, geo-environmental, and human-induced variables. The dataset considered had multicollinearity heteroscedasticity and autocorrelation problems. The metaheuristic algorithms were evaluated by varying the number of population size. Among them, PSO performed better by providing an appropriate number of features with a lower number of iterations. We have analyzed the performance of SVM with different kernels; linear, radial basis function (RBF), sigmoid, and polynomial, as the original SVM is designed only for linear datasets but the hydraulic dataset possesses non-linear characteristics as well. The performance of different kernels in terms of their accuracies is evaluated and recorded. This study showed that RBF performed the best and sigmoid showed the least accuracy for GA, PSO, and ACO algorithms. The performance of KNN is evaluated in terms of accuracies by varying the K-values. It was found that KNN shows low accuracy with a small K-value which then attained a maximum level by increasing K-values, and it finally started decreasing, explicitly, by further enhancing K-values. While comparing the performance of hybrids of GA, PSO, and ACO with SVM and KNN, it was analyzed that KNN performed better with these meta-heuristics with PSO-KNN which performed the best among the baseline models. Thus, the study proposes that PSO-KNN can be utilized as a feature selection technique to obtain optimal data subsets for hydraulic modeling and analysis.

Urban storm water prediction by applying machine learning techniques and geomorphological characteristics

ABSTRACT The Storm Water Management Model (SWMM) is a reliable software program for simulating stormwater runoff in combined drainage facilities. However, it faces challenges in efficiency, especially when executing real-time forecasting. This research explores an alternative machine learning (ML) approach to predict water levels in sewer and street drainage systems utilizing SWMM data for model training. The goal is to construct a hybrid ML model for urban flooding prediction, considering hydrological conditions, geomorphologic properties, and drainage facility features. A robust training method is introduced to deliberate complex flow conditions. The suggested approach achieves favourable performance in predictive accuracy and computational efficiency. Findings include: (1) similarity between ML and SWMM flooding depth on street nodes increases from 62.3% to 96.2% with the proposed training; (2) the proposed model is about 50 times faster than SWMM in urban flood simulations; (3) reliable predictions from the ML model are demonstrated through four accuracy metrics.

Short-Term Electricity Consumption Forecasting at Residential Level Using Two-Phase Hybrid Machine Learning Model

Short-Term Electricity Consumption Forecasting at Residential Level Using Two-Phase Hybrid Machine Learning Model

Machine learning-driven optimization of plasma-catalytic dry reforming of methane

This study investigates the dry reformation of methane (DRM) over Ni/Al2O3 catalysts in a dielectric barrier discharge (DBD) non-thermal plasma reactor. A novel hybrid machine learning (ML) model is developed to optimize the plasma-catalytic DRM reaction with limited experimental data. To address the non-linear and complex nature of the plasma-catalytic DRM process, the hybrid ML model integrates three well-established algorithms: regression trees, support vector regression, and artificial neural networks. A genetic algorithm (GA) is then used to optimize the hyperparameters of each algorithm within the hybrid ML model. The ML model achieved excellent agreement with the experimental data, demonstrating its efficacy in accurately predicting and optimizing the DRM process. The model was subsequently used to investigate the impact of various operating parameters on the plasma-catalytic DRM performance. We found that the optimal discharge power (20 W), CO2/CH4 molar ratio (1.5), and Ni loading (7.8 wt%) resulted in the maximum energy yield at a total flow rate of ∼51 mL/min. Furthermore, we investigated the relative significance of each operating parameter on the performance of the plasma-catalytic DRM process. The results show that the total flow rate had the greatest influence on the conversion, with a significance exceeding 35% for each output, while the Ni loading had the least impact on the overall reaction performance. This hybrid model demonstrates a remarkable ability to extract valuable insights from limited datasets, enabling the development and optimization of more efficient and selective plasma-catalytic chemical processes.

Hybrid Machine Learning Models for Fine-grained Air Quality Forecasting

Air pollution has become a major environmental issue that is causing many deaths each year and putting the environment and human health at serious risk. It causes the greenhouse effect, contributes to global warming, and increases the risk of lung cancer and other diseases that affect the respiratory system, including allergies. Setting and upholding strict air quality standards is essential to effectively combating air pollution. The air quality index (AQI) is a measurement used to determine the amount of pollutants in the atmosphere. By utilizing the capabilities of machine learning algorithms, precise forecasting of the fine-grained AQI is made feasible. To predict the AQI, a number of algorithms have been used, including logistic regression, decision tree regression, KNN, SVR, and linear regression. This project's main goal is to create models with machine learning algorithms and determine which model is best for AQI prediction.

Enhancing photovoltaic energy forecasting: a progressive approach using wavelet packet decomposition

Abstract Accurate photovoltaic (PV) energy forecasting plays a crucial role in the efficient operation of PV power stations. This study presents a novel hybrid machine-learning (ML) model that combines Gaussian process regression with wavelet packet decomposition to forecast PV power half an hour ahead. The proposed technique was applied to the PV energy database of a station located in Algeria and its performance was compared to that of traditional forecasting models. Performance evaluations demonstrate the superiority of the proposed approach over conventional ML methods, including Gaussian process regression, extreme learning machines, artificial neural networks and support vector machines, across all seasons. The proposed model exhibits lower normalized root mean square error (nRMSE) (2.116%) and root mean square error (RMSE) (208.233 kW) values, along with a higher coefficient of determination (R2) of 99.881%. Furthermore, the exceptional performance of the model is maintained even when tested with various prediction horizons. However, as the forecast horizon extends from 1.5 to 5.5 hours, the prediction accuracy decreases, evident by the increase in the RMSE (710.839 kW) and nRMSE (7.276%), and a decrease in R2 (98.462%). Comparative analysis with recent studies reveals that our approach consistently delivers competitive or superior results. This study provides empirical evidence supporting the effectiveness of the proposed hybrid ML model, suggesting its potential as a reliable tool for enhancing PV power forecasting accuracy, thereby contributing to more efficient grid management.

Performance evaluation of rock fragmentation prediction based on RF‐BOA, AdaBoost‐BOA, GBoost‐BOA, and ERT‐BOA hybrid models

AbstractRock fragmentation is an important indicator for assessing the quality of blasting operations. However, accurate prediction of rock fragmentation after blasting is challenging due to the complicated blasting parameters and rock properties. For this reason, optimized by the Bayesian optimization algorithm (BOA), four hybrid machine learning models, including random forest, adaptive boosting, gradient boosting, and extremely randomized trees, were developed in this study. A total of 102 data sets with seven input parameters (spacing‐to‐burden ratio, hole depth‐to‐burden ratio, burden‐to‐hole diameter ratio, stemming length‐to‐burden ratio, powder factor, in situ block size, and elastic modulus) and one output parameter (rock fragment mean size, X50) were adopted to train and validate the predictive models. The root mean square error (RMSE), the mean absolute error (MAE), and the coefficient of determination () were used as the evaluation metrics. The evaluation results demonstrated that the hybrid models showed superior performance than the standalone models. The hybrid model consisting of gradient boosting and BOA (GBoost‐BOA) achieved the best prediction results compared with the other hybrid models, with the highest R2 value of 0.96 and the smallest values of RMSE and MAE of 0.03 and 0.02, respectively. Furthermore, sensitivity analysis was carried out to study the effects of input variables on rock fragmentation. In situ block size (XB), elastic modulus (E), and stemming length‐to‐burden ratio (T/B) were set as the main influencing factors. The proposed hybrid model provided a reliable prediction result and thus could be considered an alternative approach for rock fragment prediction in mining engineering.

A comparative analysis and prediction of the economic growth of Pakistan using machine learning models

This article investigates a comparative analysis of machine learning models for Pakistan's Gross Domestic Product (GDP), an important indicator of the nation's economic development. GDP is crucial to assess well-versed decisions. Since machine learning techniques are more sophisticated, much interest has been developed in predicting GDP to handle complex data patterns and enhance prediction accuracy. In this study, we evaluated the performance of a variety of machine learning algorithms like Auto-Regressive Integrated Moving Average (ARIMA), double exponential smoothing, Multilayer Perceptron (MLP), Neural Network Auto-Regressive (NNAR), and hybrid machine learning models on data from 1960 to 2022. The MLP used in Artificial Neural Networks (ANNs) outperforms based on the outcomes. This comparative analysis provides insights into the most suitable model for accurate prediction of Pakistani GDP for the years 2023 to 2032. This article provides a detailed analysis of various machine learning models used to predict Pakistan's GDP accurately. GDP prediction is an essential indicator of a nation's economic development and is crucial in making informed decisions. With the advancements in machine learning techniques, there has been a growing interest in predicting GDP due to their efficiency in handling complex data patterns and improving prediction accuracy.

Developing hybrid machine learning models to assign health score to railcar fleets for optimal decision making

A large amount of data is generated during the operation of a railcar fleet, which can easily lead to dimensional disaster and reduce the resiliency of the railcar network. To solve these issues and offer predictive maintenance, this research introduces a hybrid fault diagnosis expert system method that combines density-based spatial clustering of applications with noise (DBSCAN) and principal component analysis (PCA). Firstly, the DBSCAN method is used to cluster categorical data that are similar to one another within the same group. Secondly, PCA algorithm is applied to reduce the dimensionality of the data and eliminate redundancy in order to improve the accuracy of fault diagnosis. Finally, we explain the engineered features and evaluate the selected models by using the Gain Chart and Area Under Curve (AUC) metrics. We use the hybrid expert system model to enhance maintenance planning decisions by assigning a health score to the railcar system of the North American Railcar Owner (NARO). The model is based on data from one specific railcar type. We utilize the results of data-driven models to assign a health rate to each railcar. Finally, we use data from NARO to evaluate and verify our proposed framework.

Advanced stacked integration method for forecasting long-term drought severity: CNN with machine learning models

Study regionEight governorates in upper Egypt namely Aswan, Asyut, Beni-Suef, Fayoum, Luxor, Minya, Qena and Sohag. Study focusThis study aims to develop novel hybrid machine learning (ML) models for forecasting the drought phenomena based on limited inputs for the eight Egyptian govern-orates, and ii) evaluate the performance and accuracy of the developed ML models for predicting Palmer Drought Severity Index (PDSI) to recommend the optimal model based on performance statistical metrics. The hybrid ML models were Convolution Neural Networks (CNN)-Long Short-Term Memory (LSTM), CNN-Random Forest (RF), CNN-Support Vector Machine (SVR), and CNN-Extreme Gradient Boosting (XGB). New hydrological insights for the regionResults showed that CNN-LSTM model outperformed the others followed by CNN-RF. Values of NSE, MAE, MARE, IA, R2, and RMSE for CNN-LSTM were 0.885, 0.915, − 2.073, 0.967, 0.885, and 0.573, respectively. For the testing stage CNN-SVR model was found to perform the best; average values of NSE, MAE, MARE, IA, R2, and RMSE were 0.828, 0.364, − 2.903, 0.950, 0.828 and 0.688, respectively. This study provided a way forward for convenient estimation of the PDSI Index from the meteorological data in terms of advancing deep learning algorithms. The developed hybrid models, more or less, can satisfactory predict PDSI values. Additionally, the study suggests the CNN-LSTM model as the most suitable model to advance future investigation in the study area.