Lower Root Mean Square Error Values Research Articles

Predicting the performance of green stormwater infrastructure using multivariate long short-term memory (LSTM) neural network

The expected performance of Green Stormwater Infrastructure (GSI) is typically quantified through numerical models based on hydrologic parameters and physics-based equations. With numerical models, the choice of a spatio-temporal discretization scheme for the computational domain is a strenuous task that requires extensive calibration and potentially lab-based parameters and experimentation. The performance of GSI has high temporal dynamics due to natural, anthropogenic, and climatic processes that are not well represented by the traditional physics-based hydrologic models, which are calibrated against only a few historical observations and have a user-defined and constrained set of computational outcomes. Deep learning-based predictive models, such as Long Short-Term Memory (LSTM) neural networks, offer an exciting opportunity to quantify GSI performance, accounting for its highly dynamic and constantly evolving nature by leveraging advancements in observational data. A LSTM regression can overcome some of the limitations associated with traditional hydrological models to aid the development of a fully data-informed GSI performance predictor. To demonstrate the LSTM and traditional model outcomes, both methods were applied to a rain garden in Villanova, PA, USA. Specifically, an LSTM model was used to predict the recession of ponded water depth in the rain garden using five years of observed data. A set of eight predictors (i.e., precipitation, air temperature, soil temperature, soil moisture content at a depth of 10 cm, 35 cm, 65 cm and 91 cm, water depth) and a target variable (i.e., recession rate) were considered for training/testing the LSTM model. A comparative study with the USEPA Storm Water Management Model (SWMM) was performed to observe the performance of a physics-based model and a LSTM model for continuous recession rate time series and specific storms. The LSTM model had a performance score, Root Mean Square Error (RMSE), of 0.081 for the continuous time series, outperforming the SWMM performance with a score of 2.173 when compared to observed data. In the case of storm-specific prediction, LSTM also outperformed SWMM simulation for four storms with lower RMSE values when compared to the observed data. The application of the LSTM model in predicting GSI performance is a crucial stride towards efficient real-time forecasting.

Assessment of hybrid machine learning algorithms using TRMM rainfall data for daily inflow forecasting in Três Marias Reservoir, eastern Brazil

This study investigates the application of the Gaussian Radial Basis Function Neural Network (GRNN), Gaussian Process Regression (GPR), and Multilayer Perceptron Optimized by Particle Swarm Optimization (MLP-PSO) models in analyzing the relationship between rainfall and runoff and in predicting runoff discharge. These models utilize autoregressive input vectors based on daily-observed TRMM rainfall and TMR inflow data. The performance evaluation of each model is conducted using statistical measures to compare their effectiveness in capturing the complex relationships between input and output variables. The results consistently demonstrate that the MLP-PSO model outperforms the GRNN and GPR models, achieving the lowest root mean square error (RMSE) across multiple input combinations. Furthermore, the study explores the application of the Empirical Mode Decomposition-Hilbert-Huang Transform (EMD-HHT) in conjunction with the GPR and MLP-PSO models. This combination yields promising results in streamflow prediction, with the MLP-PSO-EMD model exhibiting superior accuracy compared to the GPR-EMD model. The incorporation of different components into the MLP-PSO-EMD model significantly improves its accuracy. Among the presented scenarios, Model M4, which incorporates the simplest components, emerges as the most favorable choice due to its lowest RMSE values. Comparisons with other models reported in the literature further underscore the effectiveness of the MLP-PSO-EMD model in streamflow prediction. This study offers valuable insights into the selection and performance of different models for rainfall-runoff analysis and prediction.

Application of machine learning algorithms and Sentinel-2 satellite for improved bathymetry retrieval in Lake Victoria, Tanzania

Estimating bathymetric information is vital for aquaculture and navigation applications. Free, high-resolution satellite imagery provides a cost-effective solution for routine bathymetric measurements. We tested six algorithms to retrieve water depth in the Mwanza Gulf of Lake Victoria using Sentinel-2 satellite imagery: the conventional Stumpf method, Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), Neural Network (NNET), and Support Vector Machine (SVM). In-situ depth points collected via echo sounders were used to train and validate the algorithms. Performance evaluation metrics included coefficient of determination (R2), mean absolute error (MAE), root-mean-square error (RMSE), and spatial autocorrelation of residuals. Among the algorithms tested, the Stumpf model exhibited moderate performance with an R2 of 0.441, higher MAE (2.078 m), and RMSE (2.964 m) values. The RF algorithm improved performance with an R2 of 0.957, lower MAE (0.476 m), and RMSE (0.823 m). The GBM and XGB algorithms achieved R2 values of 0.960 and 0.956, respectively, with low MAE (0.484 m for GBM, 0.482 m for XGB) and RMSE (0.795 m for GBM, 0.830 m for XGB) values. The NNET algorithm outperformed the GBM and XGB models, obtaining an R2 of 0.963, the lowest MAE (0.438 m), and RMSE (0.761 m). The SVM algorithm demonstrated the best performance with an R2 of 0.965, the lowest MAE (0.403 m), and RMSE (0.745 m), implying the highest accuracy in depth estimation. SVM also showed stable generalization across different locations with insignificant spatial autocorrelation of residuals. Therefore, SVM is recommended for repetitive bathymetry calculations.

Primary Growth Models Investigation of Pseudomonas nitroreducens Growth on Octylphenol Polyethoxylates

This study evaluated the degradation of octylphenol polyethoxylates by Pseudomonas nitrore-ducens TX1 using secondary kinetics analysis. Nonlinear kinetic regression was employed through the utilization of curve-fitting software in order to fit the digitized growth degradation data. A comprehensive analysis was conducted using various statistical metrics including root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), accu-racy factor (AF), corrected AICc (Akaike Information Criterion), Bayesian Information Criterion (BIC), and Hannan-Quinn information criterion (HQC). The accuracy and statistical analysis of the kinetic models used showed that only the Huang, Baranyi Roborts, modified Gompertz, Bu-chanan-3-phase, modified Richards and Von Bertalanffy model fit the data, with modified Logis-tics having the best model with low RMSE and AICc values, highest adjusted R2 values, and Bias Factor and Accuracy Factor values closest to unity. The calculated values for the modified Logis-tics constant maximum growth rate (m), maximum growth value (A) and lag period (l), were 0.179 (h-1), 2.199 and 13.015 h, respectively. Growth curve of the bacterium on varying concen-trations of this compound can then be modelled using this model and the maximum growth rate value can be utilized for secondary modelling works further revealing important parameters.

Electrospun nanofiber membrane diameter prediction using a combined response surface methodology and machine learning approach

Despite the widespread interest in electrospinning technology, very few simulation studies have been conducted. Thus, the current research produced a system for providing a sustainable and effective electrospinning process by combining the design of experiments with machine learning prediction models. Specifically, in order to estimate the diameter of the electrospun nanofiber membrane, we developed a locally weighted kernel partial least squares regression (LW-KPLSR) model based on a response surface methodology (RSM). The accuracy of the model's predictions was evaluated based on its root mean square error (RMSE), its mean absolute error (MAE), and its coefficient of determination (R2). In addition to principal component regression (PCR), locally weighted partial least squares regression (LW-PLSR), partial least square regression (PLSR), and least square support vector regression model (LSSVR), some of the other types of regression models used to verify and compare the results were fuzzy modelling and least square support vector regression model (LSSVR). According to the results of our research, the LW-KPLSR model performed far better than other competing models when attempting to forecast the membrane's diameter. This is made clear by the much lower RMSE and MAE values of the LW-KPLSR model. In addition, it offered the highest R2 values that could be achieved, reaching 0.9989.

Improvement of Computational Efficiency and Accuracy by Firefly Algorithm and Random Forest for Compressive Strength Modeling of Recycled Concrete

It is an important direction for the sustainable development of pavement to mix the discarded concrete blocks with gradation according to a certain proportion after crushing, cleaning and other technological processes, partially or completely replace aggregate, and then add cement, water, and so on to make recycled concrete for pavement paving, but the traditional evaluation model for the compressive strength (CS) of recycled concrete cannot meet the requirements of efficient calculation. To address such issues, the present research proposed to apply the firefly algorithm (FA) to optimize the random forest (RF) model. The results were demonstrated by comparing the consistency of predicted and actual values, and also by analyzing the correlation coefficient (R) and root-mean-square error (RMSE). Higher R values (0.9756 and 0.9328) and lower RMSE values (3.0752 and 6.4369) for the training and test sets present the reliability of the FA and RF hybrid machine learning model. To understand the influence law of input indexes on the output index, the importance and sensitivity of variables are further analyzed. The results displayed that effective water-cement ratio (WC) and nominal maximum recycled concrete aggregate size (NMR) have the greatest impact on the output variable, with importance scores of 2.5947 and 2.4315, respectively, while the change in the recycled concrete aggregate replacement rate (RCA) has a weak influence, with an importance score of 0.4695. Introducing FA to RF for the compressive strength modeling of recycled concrete can significantly improve the computational efficiency and accuracy.

Comparative study of genetic programming-based algorithms for predicting the compressive strength of concrete at elevated temperature

The elevated temperature severely influences the mixed properties of concrete, causing a decrease in its strength properties. Accurate proportioning of concrete components for obtaining the required compressive strength (C-S) at elevated temperatures is a complicated and time-taking process. However, using evolutionary programming techniques such as gene expression programming (GEP) and multi-expression programming (MEP) provides the accurate prediction of concrete C-S. This article presents the genetic programming-based models (such as gene expression programming (GEP) and multi-expression programming (MEP)) for forecasting the concrete compressive strength (C-S) at elevated temperatures. In this regard, 207 C-S values at elevated temperatures were obtained from previous studies. In the model’s development, C-S was considered as the output parameter with the nine most influential input parameters, including; Nano silica, cement, fly ash, water, temperature, silica fume, superplasticizer, sand, and gravels. The efficacy and accuracy of the GEP and MEP-based models were assessed by using statistical measures such as mean absolute error (MAE), correlation coefficient (R2), and root mean square error (RMSE). Moreover, models were also evaluated for external validation using different validation criteria recommended by previous studies. In comparing GEP and MEP models, GEP gave higher R2 and lower RMSE and MAE values of 0.854, 5.331 MPa, and 0.018 MPa respectively, indicating a strong correlation between actual and anticipated outputs. Thus, the GEP-based model was used further for sensitivity analysis, which revealed that cement is the most influencing factor. In addition, the proposed GEP model provides simple mathematical expression that can be easily implemented in practice.

Music Recommendation System using Collaborative Filtering with SVD

This research provides a music recommendation system that creates tailored recommendations for users based on their listening history using a collaborative filtering algorithm and Singular Value Decomposition (SVD). Initially, the research methodology attempted to use cosine similarity to generate recommendations, but it was found to be ineffective due to the inability to handle sparse matrices for large datasets. Therefore, the research shifted its approach to using SVD to overcome this issue. The Amazon Digital Music dataset is used for the implementation of the system, which contains user ratings and reviews for various music products. The dataset is divided into three matrices using the SVD algorithm: the user matrix, the song matrix, and the diagonal matrix. With the use of these matrices, it is possible to forecast missing ratings for unrated products. The predicted ratings are then used to generate personalized recommendations for the user. The Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) metrics are used to gauge the system's performance. According to the evaluation's findings, the system performs admirably in terms of accuracy and efficacy, with low RMSE and MAE values. This indicates that the system can generate accurate recommendations for users based on their listening history, which can enhance the user experience and engagement with music streaming services. In conclusion, the work highlights the effectiveness of the collaborative filtering algorithm with SVD in generating personalized music recommendations for users. The failure of the initial approach using cosine similarity due to the inability to handle sparse matrices for large datasets underscores the importance of selecting appropriate algorithms for specific datasets. The proposed system demonstrates the effectiveness of using SVD for generating accurate and personalized recommendations for users, and future work could explore other machine learning techniques to further improve the system's performance.

A Novel MBAS-RF Approach to Predict Mechanical Properties of Geopolymer-Based Compositions.

Using gels to replace a certain amount of cement in concrete is conducive to the green concrete industry, while testing the compressive strength (CS) of geopolymer concrete requires a substantial amount of substantial effort and expense. To solve the above issue, a hybrid machine learning model of a modified beetle antennae search (MBAS) algorithm and random forest (RF) algorithm was developed in this study to model the CS of geopolymer concrete, in which MBAS was employed to adjust the hyperparameters of the RF model. The performance of the MBAS was verified by the relationship between 10-fold cross-validation (10-fold CV) and root mean square error (RMSE) value, and the prediction performance of the MBAS and RF hybrid machine learning model was verified by evaluating the correlation coefficient (R) and RMSE values and comparing with other models. The results show that the MBAS can effectively tune the performance of the RF model; the hybrid machine learning model had high R values (training set R = 0.9162 and test set R = 0.9071) and low RMSE values (training set RMSE = 7.111 and test set RMSE = 7.4345) at the same time, which indicated that the prediction accuracy was high; NaOH molarity was confirmed as the most important parameter regarding the CS of geopolymer concrete, with the importance score of 3.7848, and grade 4/10 mm was confirmed as the least important parameter, with the importance score of 0.5667.

A global long-term, high-resolution satellite radar backscatter data record (1992–2022+): merging C-band ERS/ASCAT and Ku-band QSCAT

Abstract. Satellite radar backscatter contains unique information on land surface moisture, vegetation features, and surface roughness and has thus been used in a range of Earth science disciplines. However, there is no single global radar data set that has a relatively long wavelength and a decades-long time span. We here provide the first long-term (since 1992), high-resolution (∼8.9 km instead of the commonly used ∼25 km resolution) monthly satellite radar backscatter data set over global land areas, called the long-term, high-resolution scatterometer (LHScat) data set, by fusing signals from the European Remote Sensing satellite (ERS; 1992–2001; C-band; 5.3 GHz), Quick Scatterometer (QSCAT, 1999–2009; Ku-band; 13.4 GHz), and the Advanced SCATterometer (ASCAT; since 2007; C-band; 5.255 GHz). The 6-year data gap between C-band ERS and ASCAT was filled by modelling a substitute C-band signal during 1999–2009 from Ku-band QSCAT signals and climatic information. To this end, we first rescaled the signals from different sensors, pixel by pixel. We then corrected the monthly signal differences between the C-band and the scaled Ku-band signals by modelling the signal differences from climatic variables (i.e. monthly precipitation, skin temperature, and snow depth) using decision tree regression. The quality of the merged radar signal was assessed by computing the Pearson r, root mean square error (RMSE), and relative RMSE (rRMSE) between the C-band and the corrected Ku-band signals in the overlapping years (1999–2001 and 2007–2009). We obtained high Pearson r values and low RMSE values at both the regional (r≥0.92, RMSE ≤ 0.11 dB, and rRMSE ≤ 0.38) and pixel levels (median r across pixels ≥ 0.64, median RMSE ≤ 0.34 dB, and median rRMSE ≤ 0.88), suggesting high accuracy for the data-merging procedure. The merged radar signals were then validated against the European Space Agency (ESA) ERS-2 data, which provide observations for a subset of global pixels until 2011, even after the failure of on-board gyroscopes in 2001. We found highly concordant monthly dynamics between the merged radar signals and the ESA ERS-2 signals, with regional Pearson r values ranging from 0.79 to 0.98. These results showed that our merged radar data have a consistent C-band signal dynamic. The LHScat data set (https://doi.org/10.6084/m9.figshare.20407857; Tao et al., 2023) is expected to advance our understanding of the long-term changes in, e.g., global vegetation and soil moisture with a high spatial resolution. The data set will be updated on a regular basis to include the latest images acquired by ASCAT and to include even higher spatial and temporal resolutions.

Comparison between the Operational and Statistical Daily Maximum and Minimum Temperature Forecasts on the Central Coast of Peru

Abstract Multiple linear regression models were developed for 1–3-day lead forecasts of maximum and minimum temperature for two locations in the city of Lima, on the central coast of Peru (12°S), and contrasted with the operational forecasts issued by the National Meteorological and Hydrological Service—SENAMHI and the output of a regional numerical atmospheric model. We developed empirical models, fitted to data from the 2000–13 period, and verified their skill for the 2014–19 period. Since El Niño produces a strong low-frequency signal, the models focus on the high-frequency weather and subseasonal variability (60-day cutoff). The empirical models outperformed the operational forecasts and the numerical model. For instance, the high-frequency annual correlation coefficient and root-mean-square error (RMSE) for the 1-day lead forecasts were 0.37°–0.53° and 0.74°–1.76°C for the empirical model, respectively, but from around −0.05° to 0.24° and 0.88°–4.21°C in the operational case. Only three predictors were considered for the models, including persistence and large-scale atmospheric indices. Contrary to our belief, the model skill was lowest for the austral winter (June–August), when the extratropical influence is largest, suggesting an enhanced role of local effects. Including local specific humidity as a predictor for minimum temperature at the inland location substantially increased the skill and reduced its seasonality. There were cases in which both the operational and empirical forecast had similar strong errors and we suggest mesoscale circulations, such as the low-level cyclonic vortex over the ocean, as the culprit. Incorporating such information could be valuable for improving the forecasts. Significance Statement We wanted to compare the temperature of the operational forecast of the Meteorological and Hydrological Service, an atmospheric model, and persistence with the observed temperatures on the Peruvian central coast. In addition, we generated an empirical forecast model considering both atmospheric and local predictors. We got better results with this empirical model, considering the highest Pearson correlations and the lowest RMSE values. These results will allow us to use this empirical model as the main tool to automate the forecast on the central coast of Peru. Future work should be aimed at testing the skill of this model for forecasting in other cities of Peru.

Development of hybrid SVM-FA, DT-FA and MLR-FA models to predict the flexural strength (FS) of recycled concrete

Recycled concrete from construction waste used as road material is a current sustainable approach. To provide feasible suggestions for civil engineers to prepare recycled concrete with high flexural strength (FS) for the road pavement, the present study proposed three hybrid machine learning models by combining support vector machine (SVM), decision tree (DT) and multiple linear regression (MLR) with the firefly algorithm (FA) for the computational optimization, named as SVM-FA, DT-FA, and MLR-FA, respectively. Effective water-cement ratio (WC), aggregate-cement ratio (AC), recycled concrete aggregate replacement ratio (RCA), nominal maximum recycled concrete aggregate size (NMR), nominal maximum normal aggregate size (NMN), bulk density of recycled concrete aggregate (BDR), bulk density of normal aggregate (BDN), water absorption of RCA (WAR) and water absorption of NA (WAN) were employed as the input variables. To determine the predicting results of varying hybrid models, root mean square error (RMSE) and correlation coefficient (R) were used as performance indexes. The results showed that the SVM-FA demonstrated the highest R values and the lowest RMSE values, and the fitting effect of the predicted values and the actual values of the FS of recycled concrete is the best. All the above analysis proving that the SVM optimized by FA hyperparameters has the highest prediction accuracy and SVM-FA can provide engineers a more accurate and convenient tool to evaluate the FS of recycled concrete. The results of sensitivity analysis showed that WC has the most significant influence on the FS of recycled concrete, while RCA has the weakest influence on the FS, which should be noticed when engineers apply recycled concrete to road design in the future.

Predicting the performance of radio over free space optics system using machine learning techniques

The efficiency and reliability of radio over free space optical communication may be significantly degraded by different weather conditions, thus leading to decrease and increase of signal to noise ratio (SNR) and bit error rate (BER) of the recovered signal respectively. Therefore, real time optical performance estimation is of utmost importance to enable the awareness of channel conditions and for achieving high quality of service. In this work, a terrestrial radio over free space optics (RoFSO) system has been investigated by employing 32-QAM and 64-QAM OFDM modulation schemes under rain, haze and clear weather conditions. Performance of the system is further estimated by employing artificial neural networks (ANN), k-nearest neighbours (KNN) and decision tree (DT) machine learning (ML) techniques with root mean square error (RMSE) and coefficient of determination (R2) as performance metrices. Atmospheric attenuation and different internal system parameters act as input features with BER and SNR of the received signal as the modelling targets. The best performing model has been found to be by applying an ANN approach with lower RMSE values for SNR and BER as 1.87 and 1.26 respectively. The value of R2 predicted by the model are 0.97139 and 0.97802 for SNR and BER data sets respectively.

INFLUENCE OF POINT SOURCES OF POLLUTION ON AIR QUALITY IN MAOPOLSKA FIRST TESTS OF A NEW VERSION OF FORECASTING OF AIR POLLUTION PROPAGATION SYSTEM

Polluted air is dangerous to human life and health. Particulate matter, among others PM10, isone of the most harmful substances. In Maopolska and its capital Krakw, the concentrations ofharmful substances often exceed the standards set by the World Health Organization. Krakw,thanks to the ban on residential heating with solid fuels, has significantly reduced emissions ofpollution, but they remain high in the remaining part of the region, affecting air quality in thecapital as well. With the frequent occurrence of high concentrations of pollutants, in addition tothe necessary measures aimed at reducing emissions, forecasting of air pollutant concentrations isneeded to inform the population if normative concentrations are likely to be exceeded. The FAPPS(Forecasting of Air Pollution Propagation System), based on the AROME/MM5/CALMET/CALPUFF model ensemble, has been operating in Maopolska since 2014 and has been used tocreate pollution concentration forecasts for Krakw and Maopolska. In this study, the influence ofemissions from point sources on air quality in Maopolska was investigated based on the results ofmodellingwith this system. Modelling results indicate that this influence is negligible. The qualityof PM10 forecasts for four versions of the FAPPS system, differing in the meteorological modelused MM5 (Fifth-Generation Penn State/NCAR Mesoscale Model) or WRF (Weather Researchand Forecasting) and the emission input (2015 emissions from the Maopolska Marshals Office,updated with data from the City of Krakw for 2018, or the 2020 emission inventory from theCentral Emission Database) was tested. The quality of forecasts was assessed based on the resultsof measurements at the 22 air pollution monitoring stations for three smog episodes that occurredon 1117.11.2021, 1115.12.2021 and 1318.03.2022. The best results for Krakw were obtainedusing an approach based on the WRF model and the emission inventory from the Central EmissionDatabase, for which an RMSE (Root Mean Square Error) value of 30.02 g/m3 was obtained forselected episodes. In the case of Maopolska, the lowest RMSE value (33.58 g/m3) was obtainedfor the system using the emission inventory obtained from the Marshals Office and the WRF model. First tests indicate that changing the meteorological model from MM5 to WRF can lead toimproved modelling results, but, further research is needed to confirm it.

Neural network model for predicting the biomethane yield in an anaerobic digester using biomass composition profiles

The production of biomethane or renewable natural gas from anaerobic digestion (AD) of agriculture and agroindustry wastes has gained increasing interest towards decarbonisation of fuels and energy supply. Methane yield is a critical step for assessing biomethane projects. In this work, an artificial neural network (ANN) model was developed to predict the methane yield using a collected dataset of 340 experimental data. The input parameters to the network were biomass composition in terms of total solids (TS), volatile solids (VS), lipids, protein, and lignin. The root mean square error (RMSE) of the model was 0.031 L CH4/g VS, with a mean absolute percentage error of 9.2%. The performance of the ANN models was validated using additional mono- and co-digestion experimental datasets. ANN-prediction power was also compared to the outputs of five multiple linear regression models from the literature. Results clearly demonstrated that, compared to the conventional multiple regression models, the proposed ANN-based model has a superior predictive power with lower RMSE values and lower prediction error in most cases. As such, the model is useful for preliminary stages of process design and evaluation of AD-based bioenergy projects. The model has the advantage of requiring only the analysis for five composition values and it has been implemented in a Python function which can also facilitate its wide application.

Experimental investigation and AI prediction modelling of ceramic waste powder concrete – An approach towards sustainable construction

The ceramic waste powder (CWP) is generated in the ceramic industry during the cutting and polishing stages. It is harmful to the environment and needs a massive area for disposal. Therefore, an alternative way is required to reduce the environmental pollution and landfill caused by CWP. The aim of the study is to establish an Artificial Intelligence (AI) model for CWP concrete from the experimental results to save time and cost. Advancements in AI have made the estimation of concrete mechanical characteristics possible by employing Machine Learning (ML) approaches. In the current study, 60 concrete mixes with waste CWP are made as a partial replacement of cement by 10% and 20%. The plain concrete's ultrasonic pulse velocity (UPV) is taken as a reference. Furthermore, supervised ML techniques (i.e., Bagging, XG Boost, AdaBoost) and standalone (Decision tree) are employed to foresee the UPV of CWP concrete (CWPC). The prediction model's performance is evaluated using R2, Root Mean Square Error (RMSE) values, and Mean Absolute Error (MAE). The k-fold cross-validation is used to validate the performance of the prediction model. The XG Boost model, with an R2 value of 0.95, performed better compared to Bagging, AdaBoost, and DT models. Among all ensemble and individual models, the XG Boost model performs better with higher R2 and lower RMSE (0.081 km/s) and MAE (0.063 km/s) values. Therefore, the CWPC, as a construction material, would reduce land degradation and water pollution. In addition, applying ML techniques for estimating concrete characteristics would have reduced the consumption of efforts, resources, and time of researchers in the construction sector.

Prediction of Air Quality Index Using Machine Learning Techniques: A Comparative Analysis

An index for reporting air quality is called the air quality index (AQI). It measures the impact of air pollution on a person’s health over a short period of time. The purpose of the AQI is to educate the public on the negative health effects of local air pollution. The amount of air pollution in Indian cities has significantly increased. There are several ways to create a mathematical formula to determine the air quality index. Numerous studies have found a link between air pollution exposure and adverse health impacts in the population. Data mining techniques are one of the most interesting approaches to forecast AQI and analyze it. The aim of this paper is to find the most effective way for AQI prediction to assist in climate control. The most effective method can be improved upon to find the most optimal solution. Hence, the work in this paper involves intensive research and the addition of novel techniques such as SMOTE to make sure that the best possible solution to the air quality problem is obtained. Another important goal is to demonstrate and display the exact metrics involved in our work in such a way that it is educational and insightful and hence provides proper comparisons and assists future researchers. In the proposed work, three distinct methods—support vector regression (SVR), random forest regression (RFR), and CatBoost regression (CR)—have been utilized to determine the AQI of New Delhi, Bangalore, Kolkata, and Hyderabad. After comparing the results of imbalanced datasets, it was found that random forest regression provides the lowest root mean square error (RMSE) values in Bangalore (0.5674), Kolkata (0.1403), and Hyderabad (0.3826), as well as higher accuracy compared to SVR and CatBoost regression for Kolkata (90.9700%) and Hyderabad (78.3672%), while CatBoost regression provides the lowest RMSE value in New Delhi (0.2792) and the highest accuracy is obtained for New Delhi (79.8622%) and Bangalore (68.6860%). Regarding the dataset that was subjected to the synthetic minority oversampling technique (SMOTE) algorithm, it is noted that random forest regression provides the lowest RMSE values in Kolkata (0.0988) and Hyderabad (0.0628) and higher accuracies are obtained for Kolkata (93.7438%) and Hyderabad (97.6080%) in comparison to SVR and CatBoost regression, whereas CatBoost regression provides the highest accuracies for New Delhi (85.0847%) and Bangalore (90.3071%). This demonstrated definitely that datasets that had the SMOTE algorithm applied to them produced a higher accuracy. The novelty of this paper lies in the fact that the best regression models have been picked through thorough research by analyzing their accuracies. Moreover, unlike most related papers, dataset balancing is carried out through SMOTE. Moreover, all of the implementations have been documented via graphs and metrics, which clearly show the contrast in results and help show what actually caused the improvement in accuracy.

Comparative performance of Sentinel-2 MSI and Landsat-8 OLI data in canopy cover prediction using Random Forest model: Comparing model performance and tuning parameters

Quantifying canopy cover using Random Forest (RF) model’s appropriate tuning parameters value and sensor based predictor variables is always challenging, especially in fragmented dry deciduous forests. Therefore, this study was designed to compare the performances of Sentinel-2 and Landsat-8 based models using the RF model for predicting canopy cover with assessing variables’ relative importance and correlation. Sentinel-2 and Landsat-8 based bands and spectral indices were used as predictor variables. We compared different mtry, ntree and bag fraction values of the RF model. R-square (R2) and root mean square error (RMSE) were used for comparing the model performance. The results showed that the lowest RMSE value was associated with the default value (predictors/3) or more than the default value of mtry, with bag fraction 0.3–0.7 for Sentinel-2 and 0.3–0.4 for Landsat-8. Model accuracy has increased and stabilized with increase of ntree, and received the lowest RMSE to ntree of more than 1000. Except for SWIR indices based model of Landsat-8, all other Landsat-8 based model’s accuracy was lesser compared to Sentinel-2 based models. Model accuracy of Sentinel-2 based full model (except red edge indices) was marginally better (R2 = 0.899, RMSE = 6.883 %) than Landsat-8 based full model (R2 = 0.886, RMSE = 7.089 %). But with the incorporation of red edge indices, full model RMSE had decreased further from 6.883 % to 6.747 %, and R2 had increased from 0.899 to 0.918. The full model of Sentinel-2 tended to spread variable importance among more variables, but the full model of Landsat-8 slightly tends to concentrate variable importance with fewer variables. However, SWIR bands and indices were the most important predictor variables and highly correlated with canopy cover. These findings can solve the parameter value choice of RF model, and the use of the Sentinel-2 based model will be superior to Landsat-8 based model.

Deep learning-based marine big data fusion for ocean environment monitoring: Towards shape optimization and salient objects detection

ObjectiveDuring the last few years, underwater object detection and marine resource utilization have gained significant attention from researchers and become active research hotspots in underwater image processing and analysis domains. This research study presents a data fusion-based method for underwater salient object detection and ocean environment monitoring by utilizing a deep model.MethodologyA hybrid model consists of an upgraded AlexNet with Inception v-4 for salient object detection and ocean environment monitoring. For the categorization of spatial data, AlexNet is utilized, whereas Inception V-4 is employed for temporal data (environment monitoring). Moreover, we used preprocessing techniques before the classification task for underwater image enhancement, segmentation, noise and fog removal, restoration, and color constancy.ConclusionThe Real-Time Underwater Image Enhancement (RUIE) dataset and the Marine Underwater Environment Database (MUED) dataset are used in this research project’s data fusion and experimental activities, respectively. Root mean square error (RMSE), computing usage, and accuracy are used to construct the model’s simulation results. The suggested model’s relevance form optimization and conspicuous item prediction issues in the seas is illustrated by the greatest accuracy of 95.7% and low RMSE value of 49 when compared to other baseline models.

Forecasting of groundnut yield using meteorological variables

Agriculture is one of the most significant economic sectors in India, which is strongly dependent on climatic conditions. Groundnut is one of the major oilseed crops of Gujarat and Junagadh has highest production of groundnut in Gujarat. For effective, forward-looking, and current planning, especially in agriculture, which is fraught with uncertainty, reliable and timely forecasts are essential. Therefore, effective yield forecast of such important oilseed crop is necessary for future planning and policy making. In the present investigation, the time series data of groundnut yield and weather parameters of 29 years Junagadh (1991-92 to 2019-20) were used. The week wise weather indices were generated using correlation between de-trend yield and weekly weather variables. Multiple Linear Regression (MLR) and Discriminant Function Analysis were used to develop yield forecasting model using weather indices before three week and one week before harvest, respectively. These models were compared using Coefficient of multiple determination (Adj. R2), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The study found that model-1A, developed using MLR technique have high value of adj. R2(78.8%) and low RMSE value (671.72). Multiple linear regression (MLR) was found to be more accurate than the discriminant function analysis approach.