A novel hybrid of meta-optimization approach for flash flood-susceptibility assessment in a monsoon-dominated watershed, Eastern India

Environmental Earth Sciences, Journal Year: 2022, Volume and Issue: 81(5)

Published: Feb. 21, 2022

Language: Английский

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Bim-based energy analysis and optimization using insight 360 (case study)

Case Studies in Construction Materials, Journal Year: 2022, Volume and Issue: 18, P. e01755 - e01755

Published: Dec. 12, 2022

Building information modeling (BIM) is a modern data platform and management tool that promotes the development of green buildings. In Pakistan, building sector consumes more than 50% total energy consumption it growing at annual rates 4.7% 2.5% in household commercial sectors, respectively. The problem biggest single economic drag on Pakistan BIM Council (PBC) attempting to implement adoption construction industry. Using Autodesk Insight 360 Green Studio, an analysis optimization case study A-Block COMSATS Abbottabad, chosen. This explores performance academic as order optimize use by rotating degrees 45-degree intervals utilizing install energy-efficient materials. Existing buildings have lower cost savings. financial savings are 585.10 kWh 550 $, Applying factors can result improved conceptual design with good environmental effectiveness, thus assisting pursuit sustainability.

Language: Английский

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Forest fire susceptibility mapping with sensitivity and uncertainty analysis using machine learning and deep learning algorithms

Advances in Space Research, Journal Year: 2023, Volume and Issue: 72(2), P. 426 - 443

Published: March 21, 2023

Language: Английский

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A comprehensive study on modern optimization techniques for engineering applications

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(8)

Published: July 4, 2024

Abstract Rapid industrialization has fueled the need for effective optimization solutions, which led to widespread use of meta-heuristic algorithms. Among repertoire over 600, 300 new methodologies have been developed in last ten years. This increase highlights a sophisticated grasp these novel methods. The biological and natural phenomena inform strategies seen paradigm shift recent observed trend indicates an increasing acknowledgement effectiveness bio-inspired tackling intricate engineering problems, providing solutions that exhibit rapid convergence rates unmatched fitness scores. study thoroughly examines latest advancements optimisation techniques. work investigates each method’s unique characteristics, properties, operational paradigms determine how revolutionary approaches could be problem-solving paradigms. Additionally, extensive comparative analyses against conventional benchmarks, such as metrics search history, trajectory plots, functions, are conducted elucidate superiority approaches. Our findings demonstrate potential optimizers provide directions future research refine expand upon intriguing methodologies. survey lighthouse, guiding scientists towards innovative rooted various mechanisms.

Language: Английский

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Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential

Geocarto International, Journal Year: 2021, Volume and Issue: 37(19), P. 5564 - 5584

Published: April 23, 2021

Delineation of the groundwater’s potential zones is a growing phenomenon worldwide due to high demand for fresh groundwater. Therefore, identification groundwater an important tool occurrence, protection, and management purposes. More specifically, in arid semi-arid regions, one most natural resources as it supplies water during drought period. The present research study focused on delineation Saveh City, northern part Markazi Province Iran. mapping was prepared using hybrid deep learning machine algorithm boosted tree (BT), artificial neural network (ANN), (DLNN), (DLT), boosting (DB). This carried out by fourteen conditioning factors 349 each springs non-springs points. performance model validated through statistical analysis sensitivity, specificity, positive predictive values (PPV), negative (NPV), receiver operating characteristic (ROC)-area under curve (AUC) analysis. validation result showed that success rate AUC very good DB (0.87–0.99) other models are also i.e. BT (0.81–0.90), ANN (0.77–0.82), DLNN (0.84–0.86), DLT (0.83–0.91). Among several used this altitude, rainfall, distance fault soil types more modeling. Finally, all had efficiency mapping, but recommended use Deep Boost better results future studies. work will be useful planners optimal planning

Language: Английский

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Modelling multi-hazard threats to cultural heritage sites and environmental sustainability: The present and future scenarios

Journal of Cleaner Production, Journal Year: 2021, Volume and Issue: 320, P. 128713 - 128713

Published: Aug. 20, 2021

Language: Английский

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Flash-flood hazard susceptibility mapping in Kangsabati River Basin, India

Geocarto International, Journal Year: 2021, Volume and Issue: 37(23), P. 6713 - 6735

Published: July 12, 2021

Flood-susceptibility mapping is an important component of flood risk management to control the effects natural hazards and prevention injury. We used a remote-sensing geographic information system (GIS) platform machine-learning model develop susceptibility map Kangsabati River Basin, India where flash common due monsoon precipitation with short duration high intensity. And in this subtropical region, climate change's impact helps influence distribution rainfall temperature variation. tested three models-particle swarm optimization (PSO), artificial neural network (ANN), deep-leaning (DLNN)-and prepared final classify flood-prone regions study area. Environmental, topographical, hydrological, geological conditions were included models, was selected based on relations between potentiality causative factors multi-collinearity analysis. The results validated evaluated using area under receiver operating characteristic (ROC) curve (AUC), which indicator current state environment value >0.95 implies greater floods. AUC values for ANN, DLNN, PSO training datasets 0.914, 0.920, 0.942, respectively. Among these showed best performance 0.942. approach applicable eastern part India, allow mitigation help improve region.

Language: Английский

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Ensemble of Machine-Learning Methods for Predicting Gully Erosion Susceptibility

Remote Sensing, Journal Year: 2020, Volume and Issue: 12(22), P. 3675 - 3675

Published: Nov. 10, 2020

Gully formation through water-induced soil erosion and related to devastating land degradation is often a quasi-normal threat human life, as it responsible for huge loss of surface soil. Therefore, gully susceptibility (GES) mapping necessary in order reduce the adverse effect diminishes this type harmful consequences. The principle goal present research study develop GES maps Garhbeta I Community Development (C.D.) Block; West Bengal, India, by using machine learning algorithm (MLA) boosted regression tree (BRT), bagging ensemble BRT-bagging with K-fold cross validation (CV) resampling techniques. combination aforementioned MLAs approaches state-of-the-art soft computing, not used evaluation. In further progress our work, here we total 20 conditioning factors (GECFs) 199 head cut points modelling GES. variables’ importance, which erosion, was determined based on random forest (RF) among several GECFs study. output result model’s performance validated receiver operating characteristics-area under curve (ROC-AUC), sensitivity, specificity, positive predictive value (PPV) negative (NPV) statistical analysis. predicted shows that most well fitted where AUC K-3 fold 0.972, whereas PPV NPV 0.94, 0.93, 0.96 respectively, training dataset, followed BRT model. Thus, from concluded BRT-Bagging can be applied new approach studies spatial prediction outcome work helpful policy makers implementing remedial measures minimize damages caused erosion.

Language: Английский

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Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms

Geomatics Natural Hazards and Risk, Journal Year: 2021, Volume and Issue: 12(1), P. 469 - 498

Published: Jan. 1, 2021

Spatial modelling of gully erosion at regional level is very relevant for local authorities to establish successful counter-measures and change land-use planning. This work exploring researching the potential a genetic algorithm-extreme gradient boosting (GE-XGBoost) hybrid computer education solution spatial mapping susceptibility erosion. The new machine learning approach combine extreme (XGBoost) algorithm (GA). GA metaheuristic being used improve efficiency XGBoost classification approach. A GIS database has been developed that contains recorded instances incidents 18 conditioning variables. These parameters are as predictive variables assess condition non-erosion or in given region within Kohpayeh-Sagzi River Watershed research area Iran. Exploratory results indicate proposed GE-XGBoost model superior other benchmark with desired precision (89.56%). Therefore, newly built may be promising method large-scale susceptibility.

Language: Английский

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Deep Neural Network Utilizing Remote Sensing Datasets for Flood Hazard Susceptibility Mapping in Brisbane, Australia

Remote Sensing, Journal Year: 2021, Volume and Issue: 13(13), P. 2638 - 2638

Published: July 5, 2021

Large damages and losses resulting from floods are widely reported across the globe. Thus, identification of flood-prone zones on a flood susceptibility map is very essential. To do so, 13 conditioning factors influencing occurrence in Brisbane river catchment Australia (i.e., topographic, water-related, geological, land use factors) were acquired for further processing modeling. In this study, artificial neural networks (ANN), deep learning (DLNN), optimized DLNN using particle swarm optimization (PSO) exploited to predict estimate susceptible areas future floods. The significance analysis region highlighted that altitude, distance river, sediment transport index (STI), slope played most important roles, whereas stream power (SPI) did not contribute hazardous situation. performance models was evaluated against statistical tests such as sensitivity, specificity, area under curve (AUC), true skill statistic (TSS). PSO-DLNN obtained highest values sensitivity (0.99) training stage compare with ANN. Moreover, validations specificity TSS recorded 0.98 0.90, respectively, compared those by ANN DLNN. best accuracies AUC (0.99 testing datasets), followed Therefore, proved its robustness other methods.

Language: Английский

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