Multilayer Perceptron Neural Network Model Research Articles

Analysing LULC transformations using remote sensing data: insights from a multilayer perceptron neural network approach

ABSTRACT The study examines the complex dynamics of changes in LULC over three decades, focused on the years 1992, 2002, 2012, and 2022. The research highlights the significance of comprehending these alterations within the framework of environmental and socio-economic consequences. The changes in land use and land cover (LULC) have significant and far-reaching effects on ecosystems, biodiversity, and human livelihoods. This study offers useful information for politicians, conservationists, and urban planners by examining historical patterns and forecasting future changes. The study utilized a Multilayer Perceptron Neural Network (MLP-NN), a well-known machine learning technique that excels at collecting intricate patterns. This model’s design had three layers: input, hidden, and output. The model underwent 10,000 iterations during its training process, and a thorough statistical analysis was conducted to assess the impact of each driving component. The MLP-NN model demonstrated impressive performance, with a skill measure of 0.8724 and an accuracy rate of 89.08%. The accuracy of the LULC estimates for 2022 was verified by comparing them with observed data, ensuring the model’s reliability. Moreover, the presence of evidence likely was found to be a significant factor that had a substantial impact on the accuracy of the model. The study highlights the effectiveness of the MLP-NN model in accurately predicting changes in LULC. The model’s exceptional accuracy and proficiency make it a powerful tool for future LULC forecasts. Identifying the primary causes of model performance and understanding their implications may help to enhance land management strategies, encourage spatial planning, guide accurate decision-making, and facilitate the development of policies that align with sustainable growth and development.

Predicting intensive care unit-acquired weakness: A multilayer perceptron neural network approach

In this editorial, we comment on the article by Wang and Long, published in a recent issue of the World Journal of Clinical Cases . The article addresses the challenge of predicting intensive care unit-acquired weakness (ICUAW), a neuromuscular disorder affecting critically ill patients, by employing a novel processing strategy based on repeated machine learning. The editorial presents a dataset comprising clinical, demographic, and laboratory variables from intensive care unit (ICU) patients and employs a multilayer perceptron neural network model to predict ICUAW. The authors also performed a feature importance analysis to identify the most relevant risk factors for ICUAW. This editorial contributes to the growing body of literature on predictive modeling in critical care, offering insights into the potential of machine learning approaches to improve patient outcomes and guide clinical decision-making in the ICU setting.

Analysis and Prediction of Urban Surface Transformation Based on Small Baseline Subset Interferometric Synthetic Aperture Radar and Sparrow Search Algorithm-Convolutional Neural Network-Long Short-Term Memory Model.

With the acceleration of urbanisation, urban areas are subject to the combined effects of the accumulation of various natural factors, such as changes in temperature leading to the thermal expansion or contraction of surface materials (rock, soil, etc.) and changes in precipitation and humidity leading to an increase in the self-weight of soil due to the infiltration of water along the cracks or pores in the ground. Therefore, the subsidence of urban areas has now become a serious geological disaster phenomenon. However, the use of traditional neural network prediction models has limitations when examining the causal relationships between time series surface deformation data and multiple influencing factors and when applying multiple influencing factors for predictive analyses. To this end, Sentinel-1A data from March 2017 to February 2023 were used as the data source in this paper, based on time series deformation data acquired using the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique. A sparrow search algorithm-convolutional neural network-long short-term memory (SSA-CNN-LSTM) neural network prediction model was built. The six factors of temperature, humidity, precipitation, and ground temperature at three different depths below the surface (5 cm, 10 cm, and 15 cm) were taken as the input of the model, and the surface deformation data were taken as the output of the neural network model. The correlation between the spatial and temporal evolution characteristics of the ground subsidence in urban areas and various influencing factors was analysed using grey correlation analysis, which proved that these six factors contribute to some extent to the deformation of the urban surface. The main urban area of Hohhot City, Inner Mongolia Autonomous Region, was used as the study area. In order to verify the efficacy of this neural network prediction model, the prediction effects of the multilayer perceptron (MLP), backpropagation (BP), and SSA-CNN-LSTM models were compared and analysed, with the values of the correlation coefficients of the feature points of A1, B1, and C1 being in the range of 0.92, 0.83, and 0.93, respectively. The results show that compared with the traditional MLP and BP neural network models, the SSA-CNN-LSTM model achieves a higher performance in predicting time series surface deformation data in urban areas, which provides new ideas and methods for this area of research.

Investigating the rheological characteristics of alkali-activated concrete using contemporary artificial intelligence approaches

Abstract Using artificial intelligence-based tools, this research aims to establish a direct correlation between the alkali-activated concrete (AAC) mix design factors and their performances. More specifically, the machine learning system was fed new property data obtained from AAC mixes used in laboratory experiments. The rheological parameters (yield stress [static/dynamic] and plastic viscosity) of AAC were predicted using the multilayer perceptron neural network (MLPNN) and bagging ensemble (BE) models. In addition, the R 2 values, k-fold analyses, statistical checks, and the dissimilarity between the experimental and predicted compressive strength were employed to assess the performance of the created models. Also, the SHapley additive exPlanation (SHAP) approach was used for examining the relevance of influencing parameters. The BE approach was found to be significantly accurate in all prediction models, with R 2 greater than 0.90, and MLPNN models were found to be moderately precise, with R 2 slightly below 0.90. However, the error assessment through statistical checks and k-fold analysis also validated the higher precision of BE models over the MLPNN models. Building models that can calculate rheological properties of AAC for different values of input parameters could save a lot of time and money compared to doing the tests in a laboratory. In order to ascertain the required amounts of raw materials of AAC, investigators, as well as businesses, may find the SHAP study helpful.

Automated real-time prediction of geological formation tops during drilling operations: an applied machine learning solution for the Norwegian Continental Shelf

AbstractAccurate prediction of geological formation tops is a crucial task for optimizing hydrocarbon exploration and production activities. This research investigates and conducts a comprehensive comparative analysis of several advanced machine learning approaches tailored for the critical application of geological formation top prediction within the complex Norwegian Continental Shelf (NCS) region. The study evaluates and benchmarks the performance of four prominent machine learning models: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest ensemble method, and Multi-Layer Perceptron (MLP) neural network. To facilitate a rigorous assessment, the models are extensively evaluated across two distinct datasets - a dedicated test dataset and a blind dataset independent for validation. The evaluation criteria revolve around quantifying the models' predictive accuracy in successfully classifying multiple geological formation top types. Additionally, the study employs the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm as a baseline benchmarking technique to contextualize the relative performance of the machine learning models against a conventional clustering approach. Leveraging two model-agnostic feature importance analysis techniques - Permutation Feature Importance (PFI) and Shapley Additive exPlanations (SHAP), the investigation identifies and ranks the most influential input variables driving the predictive capabilities of the models. The comprehensive analysis unveils the MLP neural network model as the top-performing approach, achieving remarkable predictive accuracy with a perfect score of 0.99 on the blind validation dataset, surpassing the other machine learning techniques as well as the DBSCAN benchmark. However, the SVM model attains superior performance on the initial test dataset, with an accuracy of 0.99. Intriguingly, the PFI and SHAP analyses converge in consistently pinpointing depth (DEPT), revolution per minute (RPM), and Hook-load (HKLD) as the three most impactful parameters influencing model predictions across the different algorithms. These findings underscore the potential of sophisticated machine learning methodologies, particularly neural network-based models, to significantly enhance the accuracy of geological formation top prediction within the geologically complex NCS region. However, the study emphasizes the necessity for further extensive testing on larger datasets to validate the generalizability of the high performance observed. Overall, this research delivers an exhaustive comparative evaluation of state-of-the-art machine learning techniques, offering critical insights to guide the optimal selection, development, and real-world deployment of accurate and reliable predictive modeling strategies tailored for hydrocarbon exploration and reservoir characterization endeavors in the NCS. Graphical abstract

AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods.

The coronavirus disease 2019 (COVID-19) pandemic continues to pose significant challenges to the public health sector, including that of the United Arab Emirates (UAE). The objective of this study was to assess the efficiency and accuracy of various deep-learning models in forecasting COVID-19 cases within the UAE, thereby aiding the nation's public health authorities in informed decision-making. This study utilized a comprehensive dataset encompassing confirmed COVID-19 cases, demographic statistics, and socioeconomic indicators. Several advanced deep learning models, including long short-term memory (LSTM), bidirectional LSTM, convolutional neural network (CNN), CNN-LSTM, multilayer perceptron, and recurrent neural network (RNN) models, were trained and evaluated. Bayesian optimization was also implemented to fine-tune these models. The evaluation framework revealed that each model exhibited different levels of predictive accuracy and precision. Specifically, the RNN model outperformed the other architectures even without optimization. Comprehensive predictive and perspective analytics were conducted to scrutinize the COVID-19 dataset. This study transcends academic boundaries by offering critical insights that enable public health authorities in the UAE to deploy targeted data-driven interventions. The RNN model, which was identified as the most reliable and accurate for this specific context, can significantly influence public health decisions. Moreover, the broader implications of this research validate the capability of deep learning techniques in handling complex datasets, thus offering the transformative potential for predictive accuracy in the public health and healthcare sectors.

Hybrid multilayer perceptron and convolutional neural network model to predict extreme regional precipitation dominated by the large-scale atmospheric circulation

Recent advances in deep learning have provided tools that enable meteorologists to predict extreme precipitation using massive atmospheric data. However, individual models are constrained by imbalanced samples and prone to false alarms owing to the rarity of extreme precipitation events. In this study, a novel ensemble learning model, that is, hybrid multilayer perceptron and convolutional neural network (MLP-CNN) model is proposed for the binary prediction of extreme precipitation in Central-Eastern China (CEC), with a daily time horizon. The MLP-CNN model achieves an overall accuracy of 86% in predicting extreme and non-extreme precipitation days using the anomalous fields of two large-scale atmospheric predictors, i.e., geopotential height at 500 hPa and vertically integrated water vapor transport. Subsequently, we employ the MLP-CNN to predict extreme precipitation with a 1–15 day leadtime. The performance of MLP-CNN tends to decrease with increasing leading time of circulation anomalies. However, 1–2 days of advance forecasting can be considered a reference for predicting the occurrence probabilities of extreme precipitation. Finally, based on various evaluation metrics, MLP-CNN outperforms the independent predictions from MLP, CNN, and two other machine learning models (i.e., random forest and support vector machine). Overall, in scenarios where samples are limited, the utilization of hybrid models presents an opportunity for optimizing predictions for extreme precipitation.

Prediction of compressive strength of concrete based on improved artificial bee colony-multilayer perceptron algorithm

There are many factors that affect the compressive strength of concrete. The relationship between compressive strength and these factors is a complex nonlinear problem. Empirical formulas commonly used to predict the compressive strength of concrete are based on summarizing experimental data of several different mix proportions and curing periods, and their generality is poor. This article proposes an improved artificial bee colony algorithm (IABC) and a multilayer perceptron (MLP) coupled model for predicting the compressive strength of concrete. To address the shortcomings of the basic artificial bee colony algorithm, such as easily falling into local optima and slow convergence speed, this article introduces a Gaussian mutation operator into the basic artificial bee colony algorithm to optimize the initial honey source position and designs an MLP neural network model based on the improved artificial bee colony algorithm (IABC-MLP). Compared with traditional strength prediction models, the ABC-MLP model can better capture the nonlinear relationship of the compressive strength of concrete and achieve higher prediction accuracy when considering the compound effect of multiple factors. The IABC-MLP model built in this study is compared with the ABC-MLP and particle swarm optimization (PSO) coupling algorithms. The research shows that IABC can significantly improve the training and prediction accuracy of MLP. Compared with the ABC-MLP and PSO-MLP coupling models, the training accuracy of the IABC-MLP model is increased by 1.6% and 4.5%, respectively. This model is also compared with common individual learning algorithms such as MLP, decision tree (DT), support vector machine regression (SVR), and random forest algorithms (RF). Based on the comparison of prediction results, the proposed method shows excellent performance in all indicators and demonstrates the superiority of heuristic algorithms in predicting the compressive strength of concrete.

Low-carbon embodied alkali-activated materials for sustainable construction: A comparative study of single and ensemble learners

Abstract Popular and eco-friendly alkali-activated materials (AAMs) replace Portland cement concrete. Due to the considerable compositional variability of AAMs and the inability of established materials science methods to understand composition–performance relationships, accurate property forecasts have proved impossible. This study set out to develop AAM compressive strength (CS) evaluation machine learning (ML) models using techniques including extreme gradient boosting (XGB), bagging regressor (BR), and multi-layer perceptron neural network (MLPNN). Ten input variables were used with a large dataset of 676 points. Statistical and K-fold studies were also used to evaluate the developed models’ correctness. XGB predicted the CS of AAM the best, followed by BR and MLPNN. The MLPNN and BR models had R 2 values of 0.80 and 0.90, respectively, whereas the XGB model had 0.94. Results from statistical analyses and k-fold cross-validation of the used ML models further attest to their validity. The built models can potentially compute the CS of AAMs for a variety of input parameter values, reducing the requirement for costly and time-consuming laboratory testing. Researchers and businesses may find this study useful in determining the necessary quantities of AAMs’ raw components.

Novel hybrid intelligence predictive model based on successive variational mode decomposition algorithm for monthly runoff series

A high-accuracy estimation of the runoff has always been an extremely relevant and challenging subject in hydrology science.Therefore, in the current research, a novel hybrid decomposition-integration-optimization based model is developed to enhance the estimation precision of the runoff. The suggested predictive model is a combination of successive variational mode decomposition (SVMD) technique and Multi-Layer Perceptron neural network (MLP) model integrated with particle swarm optimization (PSO) meta-heuristic algorithm (i.e., hybrid SVMD-MLP-PSO model). To test its performance, the mean monthly runoff data recorded from Sep 1986-Aug 2017 in Dez River basin (MRDRm), southwest of Iran, are used. The performance of the recommended model is also matched with other different hybrid and single models including MLP-PSO, SVMD-MLP, and MLP as the benchmark model. In all models, the sequence-to-one regression module of forecasting (i.e., without using meteorological parameters recorded in the study region) is utilized. In the SVMD based hybrid models, the optimal value of compactness of mode (α) for the original MRDRm time series is achieved at 100. Then, the PACF(partial autocorrelation function) diagram related to the lag length from each decomposed intrinsic mode function (IMF) sub-signals sequence generated is operated to select the ideal input variables. Performance evaluation metrics prove that the hybrid SVMD-MLP-PSO model under the best predictor and meta-parameters, outperformed with an R2 of 0.89, modified 2012 version of Kling-Gupta efficiency (KGEʹ) of 0.83, volumetric efficiency (VE) of 0.91, Nash–Sutcliffe efficiency (NSE) of 0.88, and RMSE of 13.91 m3/s. Comparatively, the standalone MLP as the benchmark model results in an R2 of 0.24, VE of 0.33, KGEʹ of 0.2, NSE of 0.29, and RMSE of 153.39 m3/s.

Significant risk factors for intensive care unit-acquired weakness: A processing strategy based on repeated machine learning.

Intensive care unit-acquired weakness (ICU-AW) is a common complication that significantly impacts the patient's recovery process, even leading to adverse outcomes. Currently, there is a lack of effective preventive measures. To identify significant risk factors for ICU-AW through iterative machine learning techniques and offer recommendations for its prevention and treatment. Patients were categorized into ICU-AW and non-ICU-AW groups on the 14th day post-ICU admission. Relevant data from the initial 14 d of ICU stay, such as age, comorbidities, sedative dosage, vasopressor dosage, duration of mechanical ventilation, length of ICU stay, and rehabilitation therapy, were gathered. The relationships between these variables and ICU-AW were examined. Utilizing iterative machine learning techniques, a multilayer perceptron neural network model was developed, and its predictive performance for ICU-AW was assessed using the receiver operating characteristic curve. Within the ICU-AW group, age, duration of mechanical ventilation, lorazepam dosage, adrenaline dosage, and length of ICU stay were significantly higher than in the non-ICU-AW group. Additionally, sepsis, multiple organ dysfunction syndrome, hypoalbuminemia, acute heart failure, respiratory failure, acute kidney injury, anemia, stress-related gastrointestinal bleeding, shock, hypertension, coronary artery disease, malignant tumors, and rehabilitation therapy ratios were significantly higher in the ICU-AW group, demonstrating statistical significance. The most influential factors contributing to ICU-AW were identified as the length of ICU stay (100.0%) and the duration of mechanical ventilation (54.9%). The neural network model predicted ICU-AW with an area under the curve of 0.941, sensitivity of 92.2%, and specificity of 82.7%. The main factors influencing ICU-AW are the length of ICU stay and the duration of mechanical ventilation. A primary preventive strategy, when feasible, involves minimizing both ICU stay and mechanical ventilation duration.

Song’s & Non-Song’s Pattern Classification Using Neural Network

The problem of pattern classification between musical or non-musical song audio snippets and non-songs is the main subject of this paper. The machine learning approach accomplishes the goal of automatically interpreting recordings to see if they are songs or not. There are two types of songs: musical and non-musical. This is due to the possibility that it could be difficult to discern between non-musical songs and non-song patterns. Thus, it is necessary to have such systems that differentiate between non-song patterns and songs, whether or not they are melodic. The extraction and selection of characteristics have made advantage of the preprocessing phases. The features that were obtained were pitch, intensity, length (duration), tempo, and sample rate. The Back-propagation Multi-layer Perceptron Neural Network model is used for both dataset testing and model training. This technique will distinguish between a musical or non-musical song and a non-musical piece (such conversation and speech). Training data for the dataset included recordings of speeches and dialogues in Hindi and Urdu for non-song audio files, as well as audio of various male and female singers from Pakistan and India for song audio files in Hindi and Urdu. When the classifier was evaluated using a range of audio samples, 90% of its classifications for songs and non-songs were accurate.

From consistent subjective assessment of skin sensitivity severity to its accurate objective scoring.

Sensitive skin (SenS) is a syndrome leading to unpleasant sensations with little visible signs. Grading its severity generally relies on questionnaires or subjective ratings. The SenS status of 183 subjects was determined by trained assessors. Answers from a four-item questionnaire were converted into numerical scores, leading to a 0-15 SenS index that was asked twice or thrice. Parameters from hyperspectral images were used as input for a multi-layer perceptron (MLP) neural network to predict the four-item questionnaire score of subjects. The resulting model was used to evaluate the soothing effect of a cosmetic cream applied to one hemiface, comparing it to that of a placebo applied to the other hemiface. The four-item questionnaire score accurately predicts SenS assessors' classification (92.7%) while providing insight into SenS severity. Most subjects providing repeatable replies are non-SenS, but accepting some variability in answers enables identifying subjects with consistent replies encompassing a majority of SenS subjects. The MLP neural network model predicts the SenS score of subjects with consistent replies from full-face hyperspectral images (R2 Validation set =0.969). A similar quality is obtained with hemiface images. Comparing the effect of applying a soothing cosmetic to that of a placebo revealed that subjects with the highest instrumental index (>5) show significant SenS improvement. A four-item questionnaire enables calculating a SenS index grading its severity. Objective evaluation using hyperspectral images with an MLP neural network accurately predicts SenS severity and its favourable evolution upon the application of a soothing cream.

Accelerated prediction of stacking fault energy in FCC medium entropy alloys using multilayer perceptron neural networks: correlation and feature analysis

A multilayer perceptron neural networks (MLPNN) model is developed for robust and quick prediction of stacking fault energy (SFE) to overcome the challenges faced in the calculation of SFE via experimentation and atomistic calculations in FCC medium entropy alloys (MEA). The present investigation employs a three-step hybrid feature selection approach to obtain a comprehensive understanding of the prominent features that influence the SFE, as well as the interrelationships among these features. The feature space encompasses various features related to composition, lattice stability, and elemental properties, of MEAs. The findings indicate that the estimation of SFE relies on five crucial factors: temperature, lattice stability, specific heat, ionization energy, and Allen electronegativities. Furthermore, a mathematical relationship for the estimation of the SFE is derived, considering the various influencing and prominent factors. Consequently, the MLPNN model for robust SFE prediction in MEAs is developed and the performance is evaluated using R 2 scores, with values of 0.87 and 0.85 obtained for the training and testing datasets, respectively. This efficient strategy introduces a novel opportunity for the engineering of SFE in the extensive range of alloy chemistry of MEAs, enabling the quick prediction of SFE, and facilitating the systematic exploration of new alloys for the development of mechanisms that may accommodate deformation through octahedral/partial slip, twinning, and/or transformation.

Can Machine Learning Predict Running Kinematics Based on Upper Trunk GPS-Based IMU Acceleration? A Novel Method of Conducting Biomechanical Analysis in the Field Using Artificial Neural Networks

This study aimed to investigate whether running kinematics can be accurately estimated through an artificial neural network (ANN) model containing GPS-based accelerometer variables and anthropometric data. Thirteen male participants with extensive running experience completed treadmill running trials at several speeds. Participants wore a GPS device containing a triaxial accelerometer, and running kinematics were captured by an 18-camera motion capture system for each trial. Multiple multilayer perceptron neural network models were constructed to estimate participants’ 3D running kinematics. The models consisted of the following input variables: 3D peak accelerometer acceleration during foot stance (g), stance time (s), running speed (km/h), participant height (cm), leg length (cm), and mass (kg). Pearson’s correlation coefficient (r), root mean squared error (RMSE), and relative root mean squared error (rRMSE) showed that ANN models provide accurate estimations of joint/segment angles (mean rRMSE = 13.0 ± 4.3%) and peak segment velocities (mean rRMSE = 22.1 ± 14.7%) at key gait phases across foot stance. The highest accuracies were achieved for flexion/extension angles of the thorax, pelvis, and hip, and peak thigh flexion/extension and vertical velocities (rRMSE < 10%). The current findings offer sports science and medical practitioners working with this data a method of conducting field-based analyses of running kinematics using a single IMU.

Analyzing predictors of pearl millet supply chain using an artificial neural network

Purpose The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield. Design/methodology/approach Descriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them. Findings The MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R2 values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield. Research limitations/implications To the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield. Originality/value The literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.

A two-layer stacking regression model for predicting yarn quality of small samples

In addressing the prevalent challenges associated with the limited generalization ability and weak adaptability to small samples observed in neural network models in contemporary yarn quality prediction research, this study proposes a two-layer stacking regression model. The initial layer of the stacking model integrates three base models: random forest, gradient boosting decision tree, and adaptive boosting. The secondary layer employs the linear regression algorithm. The efficacy of the stacking model was juxtaposed against the performance assessment of a multilayer perceptron neural network model, in addition to evaluations of the single random forest, gradient boosting decision tree, and adaptive boosting models, respectively. The generalization ability of the models was assessed through experiments that randomly partitioned the sample dataset based on 10 distinct random seeds. The adaptability to small samples of the models was evaluated through experiments spanning nine varying training set sample sizes. The experiment results underscore that the stacking model outperforms in aspects of prediction accuracy, generalization ability, and adaptability to small samples.

Prediction of artificial neural network for sulfate removal from wastewater and application analysis of key factors in anaerobic biological system

Prediction and application of the SO42- removal performance of an anaerobic biological system was conducted through the integration of artificial neural networks (ANNs). The present investigation involved the utilization of Radial Basis Function Neural Networks (RBFNN) and Multilayer Perceptron Neural Networks (MLPNN) for more rigorous prediction of SO42- removal rates. The findings of the study demonstrated that reducing the chemical oxygen demand (COD) to sulfate ratio (COD/SO42-) led to a consequent decline in the removal of SO42-. In the context of predicting SO42- reduction under conditions characterized by low COD/SO42-, MLPNN exhibited superior suitability when compared to RBFNN. The sulfate removal process was primarily influenced by various operation parameters, with the COD/SO42- being particularly influential. The proposed sensitive MLPNN model contributed to the prediction of effluent quality and potential real-time adjustments of wastewater treatment operations. The predictive evaluation of effective sulfate (SO42-) removal from sulfate-rich wastewater under various operation parameters is a significant point of guideline.

Hour-Ahead Photovoltaic Power Prediction Combining BiLSTM and Bayesian Optimization Algorithm, with Bootstrap Resampling for Interval Predictions

Photovoltaic (PV) power prediction plays a critical role amid the accelerating adoption of renewable energy sources. This paper introduces a bidirectional long short-term memory (BiLSTM) deep learning (DL) model designed for forecasting photovoltaic power one hour ahead. The dataset under examination originates from a small PV installation located at the Polytechnic School of the University of Alcala. To improve the quality of historical data and optimize model performance, a robust data preprocessing algorithm is implemented. The BiLSTM model is synergistically combined with a Bayesian optimization algorithm (BOA) to fine-tune its primary hyperparameters, thereby enhancing its predictive efficacy. The performance of the proposed model is evaluated across diverse meteorological and seasonal conditions. In deterministic forecasting, the findings indicate its superiority over alternative models employed in this research domain, specifically a multilayer perceptron (MLP) neural network model and a random forest (RF) ensemble model. Compared with the MLP and RF reference models, the proposed model achieves reductions in the normalized mean absolute error (nMAE) of 75.03% and 77.01%, respectively, demonstrating its effectiveness in this type of prediction. Moreover, interval prediction utilizing the bootstrap resampling method is conducted, with the acquired prediction intervals carefully adjusted to meet the desired confidence levels, thereby enhancing the robustness and flexibility of the predictions.

Last-Mile Optimization Using Neural Networks

In the era of extensive data acquisition from manufacturing and transportation processes, the utilization of machine learning and deep learning techniques has emerged as a potent force for informed decision-making and optimized deliveries in contemporary urban landscapes. This study presents a novel approach grounded in deep learning, where product data are systematically gathered to construct a multilayer perceptron neural network model. This model proves instrumental in efficiently classifying product flows within the urban milieu. To validate its efficacy, machine learning classifiers are deployed, and their performance is juxtaposed with the neural network model. Addressing the critical question of the paper’s significance, our experimental evaluation unequivocally demonstrates the superior classification accuracy of the proposed multilayer perceptron model when compared to traditional machine learning models operating on the same product dataset. This advancement is not merely a theoretical achievement but translates into tangible improvements in last-mile delivery processes, marked by significant cost reduction and the mitigation of delays. The transformative potential of our approach is further underscored by the strategic application of a deep learning algorithm for optimization and illustrative purposes. This holistic methodology not only positions our work as a noteworthy contribution to the realm of product classification but also establishes a concrete pathway for enhancing the sustainability and efficiency of urban logistics. This paper, thus, goes beyond the conventional application of machine learning models, offering a paradigm shift in the intersection of deep learning, urban logistics, and sustainable development.