Prediction of sustainable concrete utilizing rice husk ash (RHA) as supplementary cementitious material (SCM): Optimization and hyper-tuning DOI Creative Commons
Muhammad Nasir Amin,

Kaffayatullah Khan,

Abdullah Mohammad Abu Arab

et al.

Journal of Materials Research and Technology, Journal Year: 2023, Volume and Issue: 25, P. 1495 - 1536

Published: June 6, 2023

Rice Husk ash (RHA) utilization in concrete as a waste material can contribute to the formation of robust cementitious matrix with utmost properties. The strength HPC when subjected compression test is determined by combination and quantity materials used its production. Thus, making mixed design process challenging ambiguous. objective this research forecast containing RHA, using diverse range machine learning techniques, including both individual ensemble learners such bagging boosting. This study will cause significant implications for sustainable construction practices facilitating development an efficient effective method forecasting HPC. Individual (ML) algorithms are incorporated methods bagging, adaptive boosting, random forest algorithms. These techniques use create twenty different sub-models. Afterward, these sub-models train optimized achieving best possible value R2. were further fine-tuned obtain In order assess or evaluate quality, reliability, generalizability data, K-Fold cross-validation utilized. Furthermore, index measuring statistical performance models validate compare assessment models. findings indicate that boosting enhances prediction accuracy weak models, minimum errors R2 > 0.92 achieved decision tree forest. general, model learner (ML).

Language: Английский

Urban flooding risk assessment based on GIS- game theory combination weight: A case study of Zhengzhou City DOI

Jiaqi Peng,

Jianmin Zhang

International Journal of Disaster Risk Reduction, Journal Year: 2022, Volume and Issue: 77, P. 103080 - 103080

Published: May 29, 2022

Language: Английский

Citations

125
Comprehensive Overview of Flood Modeling Approaches: A Review of Recent Advances DOI Creative Commons
Vijendra Kumar, Kul Vaibhav Sharma, Tommaso Caloiero

et al.

Hydrology, Journal Year: 2023, Volume and Issue: 10(7), P. 141 - 141

Published: June 30, 2023

As one of nature’s most destructive calamities, floods cause fatalities, property destruction, and infrastructure damage, affecting millions people worldwide. Due to its ability accurately anticipate successfully mitigate the effects floods, flood modeling is an important approach in control. This study provides a thorough summary modeling’s current condition, problems, probable future directions. The includes models based on hydrologic, hydraulic, numerical, rainfall–runoff, remote sensing GIS, artificial intelligence machine learning, multiple-criteria decision analysis. Additionally, it covers heuristic metaheuristic techniques employed evaluation examines advantages disadvantages various models, evaluates how well they are able predict course impacts floods. constraints data, unpredictable nature model, complexity model some difficulties that must overcome. In study’s conclusion, prospects for development advancement field discussed, including use advanced technologies integrated models. To improve risk management lessen society, report emphasizes necessity ongoing research modeling.

Language: Английский

Citations

107
Review on environmental aspects in smart city concept: Water, waste, air pollution and transportation smart applications using IoT techniques DOI

Meric Yilmaz Salman,

Halil Hasar

Sustainable Cities and Society, Journal Year: 2023, Volume and Issue: 94, P. 104567 - 104567

Published: April 2, 2023

Language: Английский

Citations

102
Multi-hazard susceptibility mapping based on Convolutional Neural Networks DOI Creative Commons
Kashif Ullah, Yi Wang, Zhice Fang

et al.

Geoscience Frontiers, Journal Year: 2022, Volume and Issue: 13(5), P. 101425 - 101425

Published: June 17, 2022

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard mitigation strategy includes assessing individual hazards as well their interactions. However, with the rapid development artificial intelligence technology, techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, study proposes mapping framework using classical deep algorithm Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations Google Earth images, extensive field surveys, topography, hydrology, environmental data sets train validate proposed CNN method. Next, method assessed in comparison conventional logistic regression k-nearest neighbor methods several objective criteria, i.e., coefficient determination, overall accuracy, mean absolute error root square error. Experimental results show that outperforms algorithms predicting probability floods, flows landslides. Finally, maps three are combined create map. It can be observed from map 62.43% area prone hazards, while 37.57% harmless. hazard-prone areas, 16.14%, 4.94% 30.66% susceptible landslides, respectively. terms concurrent 0.28%, 7.11% 3.13% joint occurrence floods flow, respectively, whereas, 0.18% subject all hazards. The benefit engineers, disaster managers local government officials involved sustainable land mitigation.

Language: Английский

Citations

89
Prediction model for rice husk ash concrete using AI approach: Boosting and bagging algorithms DOI
Muhammad Nasir Amin, Bawar Iftikhar,

Kaffayatullah Khan

et al.

Structures, Journal Year: 2023, Volume and Issue: 50, P. 745 - 757

Published: Feb. 22, 2023

Language: Английский

Citations

86
A comparative assessment of flood susceptibility modelling of GIS-based TOPSIS, VIKOR, and EDAS techniques in the Sub-Himalayan foothills region of Eastern India DOI
Rajib Mitra, Jayanta Das

Environmental Science and Pollution Research, Journal Year: 2022, Volume and Issue: 30(6), P. 16036 - 16067

Published: Sept. 30, 2022

Language: Английский

Citations

75
Flash-flood hazard using deep learning based on H2O R package and fuzzy-multicriteria decision-making analysis DOI
Romulus Costache,

Tran Trung Tin,

Alireza Arabameri

et al.

Journal of Hydrology, Journal Year: 2022, Volume and Issue: 609, P. 127747 - 127747

Published: March 24, 2022

Language: Английский

Citations

74
A review of recent advances in urban flood research DOI Creative Commons
Candace Agonafir, Tarendra Lakhankar,

R. Khanbilvardi

et al.

Water Security, Journal Year: 2023, Volume and Issue: 19, P. 100141 - 100141

Published: July 13, 2023

Due to a changing climate and increased urbanization, an escalation of urban flooding occurrences its aftereffects are ever more dire. Notably, the frequency extreme storms is expected increase, as built environments impede absorption water, threat loss human life property damages exceeding billions dollars heightened. Hence, agencies organizations implementing novel modeling methods combat consequences. This review details concepts, impacts, causes flooding, along with associated endeavors. Moreover, this describes contemporary directions towards flood resolutions, including recent hydraulic-hydrologic models that use modern computing architecture trending applications artificial intelligence/machine learning techniques crowdsourced data. Ultimately, reference utility provided, scientists engineers given outline advances in research.

Language: Английский

Citations

62
A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources Management DOI Creative Commons

Maria Drogkoula,

Konstantinos Kokkinos, Nicholas Samaras

et al.

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(22), P. 12147 - 12147

Published: Nov. 8, 2023

This paper offers a comprehensive overview of machine learning (ML) methodologies and algorithms, highlighting their practical applications in the critical domain water resource management. Environmental issues, such as climate change ecosystem destruction, pose significant threats to humanity planet. Addressing these challenges necessitates sustainable management increased efficiency. Artificial intelligence (AI) ML technologies present promising solutions this regard. By harnessing AI ML, we can collect analyze vast amounts data from diverse sources, remote sensing, smart sensors, social media. enables real-time monitoring decision making applications, including irrigation optimization, quality monitoring, flood forecasting, demand enhance agricultural practices, distribution models, desalination plants. Furthermore, facilitates integration, supports decision-making processes, enhances overall sustainability. However, wider adoption faces challenges, heterogeneity, stakeholder education, high costs. To provide an management, research focuses on core fundamentals, major (prediction, clustering, reinforcement learning), ongoing issues offer new insights. More specifically, after in-depth illustration algorithmic taxonomy, comparative mapping all specific tasks. At same time, include tabulation works along with some concrete, yet compact, descriptions objectives at hand. leveraging tools, develop plans address world’s supply concerns effectively.

Language: Английский

Citations

51
Adapting cities to the surge: A comprehensive review of climate-induced urban flooding DOI Creative Commons

Gangani Dharmarathne,

Anushka Osadhi Waduge,

Madhusha Bogahawaththa

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 22, P. 102123 - 102123

Published: April 9, 2024

Climate change is a serious global issue causing more extreme weather patterns, resulting in frequent and severe events like urban flooding. This review explores the connection between climate flooding, offering statistical, scientific, advanced perspectives. Analyses of precipitation patterns show clear changes, establishing strong link heightened intensity rainfall events. Hydrological modeling case studies provide compelling scientific evidence attributing flooding to climate-induced changes. Urban infrastructure, including transportation networks critical facilities, increasingly vulnerable, worsening impact on people's lives businesses. Examining adaptation strategies, highlights need for resilient planning integration green infrastructure. Additionally, it delves into role technologies, such as artificial intelligence, remote sensing, predictive modeling, improving flood prediction, monitoring, management. The socio-economic implications are discussed, emphasizing unequal vulnerability importance inclusive policies. In conclusion, stresses urgency addressing through holistic analysis statistical trends, evidence, infrastructure vulnerabilities, adaptive measures. technologies comprehensive understanding essential developing effective, strategies. serves valuable resource, insights policymakers, researchers, practitioners striving climate-resilient futures amid escalating impacts.

Language: Английский

Citations

50