Input Combinations Research Articles

Solar Radiation Prediction in Adrar, Algeria: A Case Study of Hybrid Extreme Machine-Based Techniques

This study delves into the application of hybrid extreme machine-based techniques for solar radiation prediction in Adrar, Algeria. The models under evaluation include the Extreme Learning Machine (ELM), Weighted Extreme Learning Machine (WELM), and Self-Adaptive Extreme Learning Machine (SA-ELM), with a comparative analysis based on various performance metrics. The results show that SA-ELM achieves the highest accuracy with an R2 of 0.97, outperforming ELM and WELM by 4.6% and 15.4% respectively in terms of R2. SA-ELM also has the lowest MPE, RMSE and RRMSE values, indicating a higher accuracy in predicting global radiation. Furthermore, comparison with previously employed prediction techniques solidifies SA-ELM’s superiority, evident in its 0.275 RMSE.The study explores different input combinations for predicting global radiation in the study region, concluding that incorporating all relevant inputs yields optimal performance, although reduced input scenarios can still provide practical accuracy when data availability is limited. These results highlight the effectiveness of the SA-ELM model in accurately predicting global radiation, which is expected to have significant implications for renewable energy applications in the region. However, further testing and evaluation of the models in different regions and under different weather conditions is recommended to improve the generalizability and robustness of the results.

Strength assessment of structural masonry walls: analysis based on machine learning approaches

ABSTRACT In conventional masonry buildings, masonry walls are key structural load-bearing elements. Likewise, masonry infill walls strengthen framed constructions against lateral stress. The material characteristics of brick units and mortar determine the compressive strength of structural masonry walls. In this study, advanced machine learning (ML) techniques were utilized to estimate the compressive strength of structural masonry walls based on the material properties of brick units and mortar. The Young’s modulus of brick units (Eu), compressive strength of brick units (Fcu), Young’s modulus of mortar (Em), and compressive strength of mortar (Fcm) were used as input parameters to model the compressive strength (Fc) of the structural masonry wall. Gradient Tree Boosting (GTB), Elman Neural Network (ENN), and Multivariate Adaptive Regression Splines (MARS) were developed using four diverse input and output (I/O) combinations to explore the effect of each input parameter on output estimation. The data used for modeling were obtained from prior studies published in the literature. The model’s performance was evaluated based on different statistical (error and efficiency) indices. For the third and fourth (I/O) combinations, the MARS model significantly outperformed other models. However, for the first I/O combination, the GTB model performed well. The study also revealed that the compressive strength of a structural masonry wall is more likely to depend on the strength and quality of the brick units than on the strength of the mortar.

A novel hybrid model combined with ensemble embedded feature selection method for estimating reference evapotranspiration in the North China Plain

The reference evapotranspiration (ETo) is a key parameter in achieving sustainable use of agricultural water resources. To accurately acquire ETo under limited conditions, this study combined the northern goshawk optimization algorithm (NGO) with the extreme gradient boosting (XGBoost) model to propose a novel NGO-XGBoost model. The performance of this model was evaluated using meteorological data from 30 stations in the North China Plain and compared with XGBoost, random forest (RF), and k nearest neighbor (KNN) models. An ensemble embedded feature selection (EEFS) method combined with the results from RF, XGBoost, adaptive boosting (AdaBoost), and categorical boosting (CatBoost) models is used to obtain the importance of meteorological factors in estimating ETo, and thereby determine the optimal combination of inputs to the model. The results indicated that by using the top 3, 4, and 5 important factors as input combinations, all models achieved high ETo estimation accuracy. It is worth noting that there were significant spatial differences in the estimation precisions of the four models, but the NGO-XGBoost model exhibited consistently high estimation precisions, with global performance indicator (GPI) rankings of 1st, and the range of coefficient of determination (R2), nash efficiency coefficient (NSE), root mean square error (RMSE), mean absolute error (MAE) and mean bias error (MBE) were 0.920–0.998, 0.902–0.998, 0.078–0.623 mm d−1, 0.058–0.430 mm d−1, and −0.254–0.062 mm d−1, respectively. Furthermore, the accuracy of the NGO-XGBoost model in estimating ETo varied across different seasons, which was more significantly affected by humidity and wind speed in winter. When the target station data was insufficient, the NGO-XGBoost model was trained by using the historical data from neighboring stations and still maintained a high precision. Overall, this study recommends a reliable method for estimating ETo, which provides a reference for accurately calculating ETo in the North China Plain in the absence of meteorological data.

Prescribing medications for Parkinson's: part two

Around 10 million people worldwide have Parkinson's and in the UK the estimated figure is 153 000. The condition is characterised by motor symptoms including tremors, stiffness, slowness, balance problems and/or gait disorders, but people can develop a wide range of associated psychological and physical problems. Treatment for Parkinson's includes a combination of multidisciplinary input from specialists such as physiotherapy, speech and language therapy and occupational therapy, alongside the use of recognised medication regimens. Most first-line medication requires titration and is started at a low dose and increased gradually, to reduce the risk of side effects and to monitor response to treatment. Medications for Parkinson's are complex to manage, and many have side effects that could affect patient concordance. Any prescribing decisions should be discussed with the person with Parkinson's and their family members. There are a variety of medications available to manage motor and non-motor symptoms in Parkinson's, but this article will focus on levodopa, dopamine agonists, MAO-B inhibitors and catechol-O-methyltransferase inhibitors.

Effect of Organic Nutrient Management Practices on Growth, Yield and Pests Incidence of Scented Rice (Oryza sativa L.) in Rainfed Situation of Assam

The field experiment was conducted in organic block on experimental farm of AAU-Assam Rice Research Institute, Assam Agricultural University, Titabar, Assam during kharif season of 2019 and 2020 to study the effect of organic nutrient management practices on growth, yield and pests incidence of scented rice (Oryza sativa L.) in rainfed situation of Assam. Eight treatment combinations of organic inputs were laid out in Randomized Block Design with three replications including one control where no organic nutrients were added. The study revealed that combined application of different proportion of green manure, enriched compost, azolla, vermicompost, neem cake with bio fertilizer consortium and rock phosphate as different treatment significantly improved the growth behavior, yield and also reduced pests incidence of scented rice. Among the organic nutrient management treatments, combination of Green manure (2.5 t/ha)+ Vermicompost (2.5 t/ha)+ Biofertilizer consortium (4 kg/ha seedling) as root dip +Rock phosphate (17 kg/ha)+Azolla (20 kg/ha) recorded the highest yield (3.80 t/ha) by improving yield attributing parameters, viz., Panicles (276.3 /m2), Spikelet /panicle (25.50 number) and Panicle weight (3.37g). The organic treatments also significantly enhanced the growth physiology viz., leaf area index (4.32), and specific leaf weight (8.56 mg/cm2) at panicle initiation stage (90 DAT). In organic management treatments, pests and disease incidence significantly reduced compared to control plot. Highest reduction of dead heart (DH%) and white ear head (WEH%) infestation due to stem borer, gall midge (silver shoot) and leaf folder damage was noticed in the treatment combination of Green manure (2.5 t/ha) + Vermicompost ( 2.5 t/ha)+ Biofertilizer consortium (4 kg/ha seedling) as root dip+Rock phosphate (17 kg/ha)+Azolla (20 kg/ha).

Prediction of daily river water temperatures using an optimized model based on NARX networks

Water temperature is an important physical indicator of rivers because it impacts many other physical and biogeochemical processes and controls the metabolism of aquatic species in rivers. Having a good knowledge of river thermal dynamics is of great importance. In this study, an advanced machine learning based model that is fast, accurate and easy to use, namely the nonlinear autoregressive network with exogenous inputs (NARX) neural network, was coupled with Bayesian Optimization (BO) algorithm for optimizing the number of NARX hidden nodes and lagged input/target values and the Bayesian Regularization (BR) backpropagation algorithm for the NARX training, to forecast daily river water temperatures (RWT). Long-term observed data from 18 rivers of the Vistula River Basin, one of the largest rivers in Europe, were used for model testing, and model performance was compared with the air2stream model. The results showed that the NARX-based model performs significantly better than the air2stream model in the calibration and validation stages, and can better capture the seasonal pattern and peak values of RWT. Input combinations impact the performance of the NARX-based model in RWT modeling, and air temperature and the day of the year (DOY) are the major inputs, while streamflow and rainfall play a minor role on modeling RWT at the Vistula River Basin. Considering that future times series of air temperatures are easily accessible from climate models and DOY is easy to be considered in the model, the NARX-based model can serve as a promising tool to investigate the impact of climate change on river thermal dynamics.

Immunizing Image Classifiers Against Localized Adversary Attack

This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks (CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations. When combined with 3D convolution and deep curriculum learning optimization (CLO), it significantly improves the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10 and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing accuracy improvements over previous techniques. The results indicate that the combination of the volumetric input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating adversary training.

Deep Convolutional Neural Network for Dedicated Regions-of-Interest Based Multi-Parameter Quantitative Ultrashort Echo Time (UTE) Magnetic Resonance Imaging of the Knee Joint.

We proposed an end-to-end deep learning convolutional neural network (DCNN) for region-of-interest based multi-parameter quantification (RMQ-Net) to accelerate quantitative ultrashort echo time (UTE) MRI of the knee joint with automatic multi-tissue segmentation and relaxometry mapping. The study involved UTE-based T1 (UTE-T1) and Adiabatic T1ρ (UTE-AdiabT1ρ) mapping of the knee joint of 65 human subjects, including 20 normal controls, 29 with doubtful-minimal osteoarthritis (OA), and 16 with moderate-severe OA. Comparison studies were performed on UTE-T1 and UTE-AdiabT1ρ measurements using 100%, 43%, 26%, and 18% UTE MRI data as the inputs and the effects on the prediction quality of the RMQ-Net. The RMQ-net was modified and retrained accordingly with different combinations of inputs. Both ROI-based and voxel-based Pearson correlation analyses were performed. High Pearson correlation coefficients were achieved between the RMQ-Net predicted UTE-T1 and UTE-AdiabT1ρ results and the ground truth for segmented cartilage with acceleration factors ranging from 2.3 to 5.7. With an acceleration factor of 5.7, the Pearson r-value achieved 0.908 (ROI-based) and 0.945 (voxel-based) for UTE-T1, and 0.733 (ROI-based) and 0.895 (voxel-based) for UTE-AdiabT1ρ, correspondingly. The results demonstrated that RMQ-net can significantly accelerate quantitative UTE imaging with automated segmentation of articular cartilage in the knee joint.

What Is in a <unittitle>? Cross-lingual Topic Detection & Information Retrieval in Archives Portal Europe

Archives Portal Europe (APE, www.archivesportaleurope.net ) is the portal of European archives, an aggregator that connects on a single research point the catalogues and digitised archival material of all archives in and about Europe. It currently hosts material from more than 30 countries and from a variety of archival institutions (such as State archives, city archives, university and parish archives, private institutions, and more). It is maintained by the Archives Portal Europe Foundation, an international consortium of State archives and other archival institutions that aim to connect the archival material of single institutions into one digital repository to allow universal access to the archival heritage of Europe, promoting new forms of archival research beyond national or local boundaries. One of the research tools made available by Archives Portal Europe is by topics; however, these are currently maintained manually by the archivists, and the vast amount of archival material ingested in the portal makes it impossible to have a comprehensive body of topics that describe the whole of the APE repository. Archives are traditionally not organised by their subject content, but around the entity (person, organization, body) that created and/or collected the documents in the course of their activities. While this is an undisputed pillar of archival management, the availability of online digital repositories for archival research requires new tools for digital archival research, particularly when different archival traditions from different countries and different types of institutions are merged into a unique research portal. Topic detection becomes a fundamental tool to guide archival research and to allow archives to be accessible to potentially world-wide users in a situation where national and linguistics barriers blur or are re-defined. This article presents the preliminary results and plan for future iterations of an AI tool for automated topic detection in a multi- lingual environment, where human-created taxonomies act as bases for the algorithms to aggregate relevant material around a specific topic. The development is based on supervised machine learning, with a combination of human inputs in different languages, and of the usage of Wikipedia pages to model the relevant vocabulary and entities.

Influence of Organic and Inorganic Inputs on Vegetative, Flowering and Yield Attributes of Marigold cv. Pusa Narangi Gainda

The present investigation was carried out on “Influence of organic and inorganic inputs on vegetative, flowering and yield attributes of marigold cv. Pusa NarangiGainda” at instructional farm, Department of Floriculture and Landscape Architecture, College of Horticulture and Forestry, Pasighat, Arunachal Pradesh during the year 2021-22. A field experiment was laid out in Randomized Block Design (RBD) with 13 treatments in three replications. Findings revealed significant (p<0.05) influence of organic and inorganic inputs on vegetative, flowering and yield characters of the marigold. Maximum plant height (130.70 cm), number of primary branches (11.20), number of secondary branches (14.40), leaf area (56.57 cm2), East-Westplant spread (49.25 cm), and North-South plant spread (46.50 cm) were associated with treatments T12-FYM @ 25 t.ha-1 + Vermicompost @ 5 t.ha-1 + Mustard oil cake @ 5 t.ha-1 + Dolomite @ 222 kg.ha-1. Treatment T12 also had earlier bud initiation (44.10 days), maximum number of flowers per plant (20.23), and flower yield (159.73 q). However, the maximum flower diameter (62.19 mm) was recorded in the treatment T10 - FYM @ 25 t.ha-1 + Mustard oil cake @ 5 t.ha-1 + Dolomite @ 222 kg.ha-1. The study demonstrates that combination of organic inputs significantly enhances the vegetative, flowering and yield attributes in marigold that could be exploited for increased productivity of the crop.

Surface soil moisture retrieval based on transfer learning using SAR data on a local scale

ABSTRACT Retrieving surface soil moisture on a local scale using Synthetic Aperture Radar (SAR) data and Deep Learning (DL) models necessitates a substantial volume of data, which may not be available in all scenarios. In this study, the application of transfer learning was introduced as a novel approach to address the scarcity of training samples for DL models in the context of soil moisture retrieval. The proposed DL model was initially trained using International Soil Moisture Network (ISMN) data, followed by a fine-tuned process on a local scale using field trip data from an agricultural area in Karaj, Iran. The proposed DL model was compared against Random Forest Regressor (RFR), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP) based on Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R2 indicators. All models underwent hyperparameter-tunning, and their performance was evaluated using 8-fold cross-validation (CV) and various combinations of inputs. The proposed DL model outperformed other models on a local scale achieving an RMSE, MAE, and R2 of 2.42 vol%, 1.66 vol%, and 0.90, respectively. The MLP model also exhibited good performance with an RMSE, MAE, and R2 of 2.84 vol%, 2.04 vol%, and 0.88 compared to the RFR with 2.83 vol%, 2.20 vol%, and 0.86, respectively. Additionally, the SVR yielded an RMSE, MAE, and R2 of 3.71 vol%, 3.05 vol%, and 0.78. However, the RMSE, MAE, and R2 of the MLP and the proposed DL model without using transfer learning deteriorated by around 18%, 32%, and 34%, respectively.

Multi-sequence spatio-temporal feature fusion network for peak-hour passenger flow prediction in urban rail transit

ABSTRACT This research addresses the challenge of predicting URT station passenger flow during peak hour. The Multi-Sequence Spatio-Temporal Feature Fusion Network Model (MSSTFFN) based on trend decomposition is introduced to capture complex spatio-temporal correlations. This model combines seasonal trend decomposition, graph convolutional neural networks, and modified Transformer networks. The MSSTFFN model is evaluated using actual data from Hangzhou City. The results indicate that, in comparison to the baseline model, this model consistently delivers the best prediction results across various datasets as well as prediction tasks. It exhibits exceptional and consistent performance in prediction sub-tasks involving different input and prediction step combinations, highlighting its advanced, robust, and versatile nature. Through micro-comparisons of specific prediction results for different types of stations, the practical application value is verified. Furthermore, through the design of ablation experiments and testing on various datasets, the contribution value of the features and model’s generalization capability are validated.

Incorporating artificial intelligence-powered prediction models for exergy efficiency evaluation in parabolic trough collectors

Exergy analysis is essential for evaluating the second law of thermodynamics efficiency in solar thermal applications such as parabolic trough collectors (PTCs). This study creates ML models to tackle complex challenges in renewable energy systems and components. Six prediction models were developed such as Adaptive Boosting (AdaBoost), Multivariate adaptive regression splines (MARS), Stochastic Gradient Descent (SGD), Tweedie Regressor, voting, and stacking ensemble learning, were developed to predict the exergy efficiency of PTCs. The base fluids were three molten salts (Solar Salt, Hitec, and Hitec XL). Three nanoparticle types (Al2O3, CuO, and SiO2) were mixed homogeneously in a single-phase approach to prepare nine nanofluids. The output was predicted based on different input parameters such as molten salts, nanoparticle types, input temperature, volume fraction, Reynolds number (Re), Nusselt number (Nu), and friction factor (f). The results indicated that the stacking regressor efficiently predicted the exergy efficiency using (3-5) input parameters with a coefficient of determination (R2 = 0.963), followed by the AdaBoost algorithm with R2 = 0.947 using the fifth input combination over the testing phase. Further, AdaBoost and Stacking Regressors models were compared with the previously published study and showed an overall accuracy of R2 = 0.9472 and R2 = 0.9634, respectively.

Identifying Damage in Structures: Definition of Thresholds to Minimize False Alarms in SHM Systems

In recent years, the development of quick and streamlined methods for the detection and localization of structural damage has been achieved by analysing key dynamic parameters before and after significant events or as a result of aging. Many Structural Health Monitoring (SHM) systems rely on the relationship between occurred damage and variations in eigenfrequencies. While it is acknowledged that damage can affect eigenfrequencies, the reverse is not necessarily true, particularly for minor frequency variations. Thus, reducing false positives is essential for the effectiveness of SHM systems. The aim of this paper is to identify scenarios where observed changes in eigenfrequencies are not caused by structural damage, but rather by non-stationary combinations of input and system response (e.g., wind effects, traffic vibrations), or by stochastic variations in mass, damping, and stiffness (e.g., environmental variations). To achieve this, statistical variations of thresholds were established to separate linear non-stationary behaviour from nonlinear structural behaviour. The Duffing oscillator was employed in this study to perform various nonlinear analyses via Monte Carlo simulations.

Improved monthly streamflow prediction using integrated multivariate adaptive regression spline with K-means clustering: implementation of reanalyzed remote sensing data

AbstractThis study investigates monthly streamflow modeling at Kale and Durucasu stations in the Black Sea Region of Turkey using remote sensing data. The analysis incorporates key meteorological variables, including air temperature, relative humidity, soil wetness, wind speed, and precipitation. The study also investigates the accuracy of multivariate adaptive regression (MARS) with Kmeans clustering (MARS-Kmeans) by comparing it with single MARS, M5 model tree (M5Tree), random forest regression (RF), multilayer perceptron neural network (MLP). In the first modeling stage, principal component regression is applied to diverse input combinations, both with and without lagged streamflow (Q), resulting in twenty-three and twenty input combinations, respectively. Results demonstrate the critical role of including lagged Q for improved model accuracy, as models without lagged Q exhibit significant performance degradation. The second stage involves a comparative analysis of the MARS-Kmeans model with other machine-learning models, utilizing the best-input combination. MARS-Kmeans, incorporating three clusters, consistently outperforms other models, showcasing superior accuracy in predicting monthly streamflow.

Performance of digital technologies in assessing fall risks among older adults with cognitive impairment: a systematic review.

Older adults with cognitive impairment (CI) are twice as likely to fall compared to the general older adult population. Traditional fall risk assessments may not be suitable for older adults with CI due to their reliance on attention and recall. Hence, there is an interest in using objective technology-based fall risk assessment tools to assess falls within this population. This systematic review aims to evaluate the features and performance of technology-based fall risk assessment tools for older adults with CI. A systematic search was conducted across several databases such as PubMed and IEEE Xplore, resulting in the inclusion of 22 studies. Most studies focused on participants with dementia. The technologies included sensors, mobile applications, motion capture, and virtual reality. Fall risk assessments were conducted in the community, laboratory, and institutional settings; with studies incorporating continuous monitoring of older adults in everyday environments. Studies used a combination of technology-based inputs of gait parameters, socio-demographic indicators, and clinical assessments. However, many missed the opportunity to include cognitive performance inputs as predictors to fall risk. The findings of this review support the use of technology-based fall risk assessment tools for older adults with CI. Further advancements incorporating cognitive measures and additional longitudinal studies are needed to improve the effectiveness and clinical applications of these assessment tools. Additional work is also required to compare the performance of existing methods for fall risk assessment, technology-based fall risk assessments, and the combination of these approaches.

Transparent objects segmentation based on polarization imaging and deep learning

Previous investigations relied on intensity, degree of linear polarization, and angle of linear polarization for transparent object (TO) segmentation. However, no research have examined TO segmentation using other polarization parameters. This paper presents an adaptive edge-enhanced TO segmentation network that is capable of selecting the most optimal combination of inputs from 12 polarization parameters in an identical epoch. This study employed a laboratory-built system with polarized illumination and polarization camera to build a multi-polarization cue-based TO dataset. The optimal polarization scheme has been determined based on segmentation accuracy, support by comparative studies. To verify the feasibility of this optimal network, several ablation and comparison experiments have been conducted. It provides a prospective TO recognition strategy for industrial detection.

One to twelve-month-ahead forecasting of MODIS-derived Qinghai Lake area, using neuro-fuzzy system hybridized by firefly optimization.

Lakes, as the main sources of surface water, are of great environmental and ecological importance and largely affect the climatic conditions of the surrounding areas. Lake area fluctuations are very effective on plant and animal biodiversity in the areas covered. Hence, accurate and reliable forecasts of ​lake area might provide the awareness of water and climate resources and the survival of various species dependent on area fluctuations. Using machine learning methods, the current study numerically predicted area fluctuations of ​China's largest lake, Qinghai, over 1 to 12months ahead of lead time. To this end, Moderate Resolution Imaging Spectroradiometer (MODIS) sensor images were used to monitor the monthly changes in the area of ​the lake from 2000 to 2021. Predictive inputs included the MODIS-derived lake area time latency specified by the autocorrelation function. The data was divided into two periods of the train (initial 75%) and test (final 25%), and the input combinations were arranged so that the model in the test period could be used to predict 12 scenarios, including forecast horizons for the next 1 to 12months. The adaptive neuro-fuzzy inference system (ANFIS) was utilized as a predictive model. The firefly algorithm (FA) was also used to optimize ANFIS and improve its accuracy, as a hybrid model ANFIS-FA. Based on evaluation criteria such as root mean square error (RMSE) (477-594 km2) and R2 (88-92%), the results confirmed the acceptable accuracy of the models in all forecast horizons, even long-term horizons (10months, 11months, and 12months). Based on the normalized RMSE criterion (0.095-0.125), the models' performance was reported to be appropriate. Furthermore, the firefly algorithm improved the prediction accuracy of the ANFIS model by an average of 16.9%. In the inter-month survey, the models had fewer forecast errors in the dry months (February-March) than in the wet months (October-November). Using the current method can provide remarkable information about the future state of lakes, which is very important for managers and planners of water resources, environment, and natural ecosystems. According to the results, the current approach is satisfactory in predicting MODIS-derived fluctuations of Qinghai Lake area and has research value for other lakes.

A novel machine learning tool for current and future flood susceptibility mapping by integrating remote sensing and geographic information systems

Flood mapping is essential for managing and mitigating the risks associated with flood events. This study integrates weighted overlay analysis and analytical network process to create a flood susceptibility map. Twelve factors were selected to assess flood risk in the region, and an analytical network process was used to derive weights of factors. Weighted overlay analysis was then used to apply weights to the flood conditioning factors to prepare the flood susceptibility map. The most prominent historical flood extent was derived by using Landsat 8, Sentinel-2 and Sentinel-1 satellite imageries, spectral indices and decision tree model to generate a flood inventory map. The flood susceptibility map was assessed for accuracy using the flood inventory map, achieving an overall accuracy rate of 88.3 %. The primary factors affecting flood risk were elevation, slope, and distance from the stream, with high-risk areas mainly located downstream. To develop the flood risk assessment model, the essential factors were classified into four main categories: topographical, environmental, hydrological, and anthropological. All possible input combinations were checked using the outlier robust extreme learning machine for one to twelve input variables, and the best model was selected using the outlier robust extreme learning machine with the input combination of five variables (i.e., normalizes difference vegetation index, geology, precipitation, elevation, and slope). Based on the Sixth Intergovernmental Panel on climate change report, the developed outlier robust extreme learning machine was used to project potential flood areas in the next 78 years. Based on the different scenarios, the results indicated that 10.93 %, 8.55 %, 8.09 %, 11.99 %, and 16.28 % of all areas are projected to be highly and very highly flood susceptible. The tools and methods outlined in this study can assist decision-makers in preparing for and responding to floods. Moreover, the method developed in this study can be implemented in watersheds and urban areas and help manage flood risk in urban drainage systems.

Performance Analysis of Intelligent Computational Algorithms for Biomass Higher Heating Value Prediction

Higher heating value (HHV) is an essential parameter to consider when evaluating and choosing biomass substrates for combustion and  power generation. Traditionally, HHV is determined in the laboratory using an adiabatic oxygen bomb calorimeter. Meanwhile, this  approach is laborious and cost-intensive. Hence, it is essential to explore other viable options. In this study, two distinct artificial  intelligence-based techniques, namely, a support vector machine (SVM) and an artificial neural network (ANN) were employed to develop  proximate analysis-based biomass HHV prediction models. The input variables comprising ash, volatile matter, and fixed carbon were  paired to form four separate inputs to the prediction models. The overall findings showed that both the ANN and the SVM tools can guarantee accurate prediction in all the input combinations. The optimal prediction performances were observed when fixed carbon and  volatile matter were paired as the input combination. This combination showed that the ANN outperformed the SVM, having presented  the least root mean squared error of 0.0008 and the highest correlation coefficient of 0.9274. This study, therefore, concluded that the  ANN is more preferred compared to SVM for biomass HHV prediction based on the proximate analysis.