Machine learning for predicting greenhouse gas emissions from agricultural soils

The Science of The Total Environment, Год журнала: 2020, Номер 741, С. 140338 - 140338

Опубликована: Июнь 19, 2020

Язык: Английский

---

A review on applications of urban flood models in flood mitigation strategies

Natural Hazards, Год журнала: 2021, Номер 108(1), С. 31 - 62

Опубликована: Март 30, 2021

Язык: Английский

---

Towards better flood risk management: Assessing flood risk and investigating the potential mechanism based on machine learning models

Journal of Environmental Management, Год журнала: 2021, Номер 293, С. 112810 - 112810

Опубликована: Май 21, 2021

Язык: Английский

---

Toward an Integrated Disaster Management Approach: How Artificial Intelligence Can Boost Disaster Management

Sustainability, Год журнала: 2021, Номер 13(22), С. 12560 - 12560

Опубликована: Ноя. 13, 2021

Technical and methodological enhancement of hazards disaster research is identified as a critical question in management. Artificial intelligence (AI) applications, such tracking mapping, geospatial analysis, remote sensing techniques, robotics, drone technology, machine learning, telecom network services, accident hot spot smart city urban planning, transportation environmental impact are the technological components societal change, having significant implications for on response to disasters. Social science researchers have used various technologies methods examine disasters through disciplinary, multidisciplinary, interdisciplinary lenses. They employed both quantitative qualitative data collection analysis strategies. This study provides an overview current applications AI management during its four phases how vital all phases, leading faster, more concise, equipped response. Integrating geographic information system (GIS) (RS) into enables higher situational awareness, recovery operations. GIS RS commonly recognized key support tools Visualization capabilities, satellite images, artificial can assist governments making quick decisions after natural

Язык: Английский

---

A critical review of real-time modelling of flood forecasting in urban drainage systems

Journal of Hydrology, Год журнала: 2022, Номер 607, С. 127476 - 127476

Опубликована: Янв. 22, 2022

There has been a strong tendency in recent decades to develop real-time urban flood prediction models for early warning the public due large number of worldwide occurrences and their disastrous consequences. While significant breakthrough made so far, there are still some potential knowledge gaps that need further investigation. This paper presents comprehensive review current state-of-the-art future trends modelling forecasting drainage systems. Findings showed combination various sources rainfall measurement inclusion other data such as soil moisture, wind flow patterns, evaporation, fluvial infiltration should be more investigated models. Additionally, artificial intelligence is also present most new RTFF UDS consequently developments this technique expected appear works.

Язык: Английский

---

An Overview of Flood Concepts, Challenges, and Future Directions

Journal of Hydrologic Engineering, Год журнала: 2022, Номер 27(6)

Опубликована: Март 24, 2022

This review provides a broad overview of the current state flood research, challenges, and future directions. Beginning with discussion flood-generating mechanisms, synthesizes literature on forecasting, multivariate nonstationary frequency analysis, urban flooding, remote sensing floods. Challenges research directions are outlined highlight emerging topics where more work is needed to help mitigate risks. It anticipated that systems will likely have significant risk due compounding effects continued climate change land-use intensification. The timely prediction floods, quantification socioeconomic impacts developing mitigation strategies continue be challenging. There need bridge scales between model capabilities end-user needs by integrating multiscale models, stakeholder input, social citizen science input for monitoring, mapping, dissemination. Although much progress has been made in using applications, recent upcoming Earth Observations provide excellent potential unlock additional benefits applications. community can benefit from downscaled, as well ensemble scenarios consider changes. Efforts also data assimilation approaches, especially ingest local, citizen, media data. Also enhanced compound hazards assess reduce vulnerability impacts. dynamic complex interactions climate, societal change, watershed processes, human factors often confronted deep uncertainty highlights transdisciplinary science, policymakers, stakeholders vulnerability.

Язык: Английский

---

Fast simulation and prediction of urban pluvial floods using a deep convolutional neural network model

Journal of Hydrology, Год журнала: 2023, Номер 624, С. 129945 - 129945

Опубликована: Июль 18, 2023

Язык: Английский

---

Hydraulic modelling of inland urban flooding: Recent advances

Journal of Hydrology, Год журнала: 2022, Номер 609, С. 127763 - 127763

Опубликована: Март 25, 2022

Язык: Английский

---

Rapid Prediction Model for Urban Floods Based on a Light Gradient Boosting Machine Approach and Hydrological–Hydraulic Model

International Journal of Disaster Risk Science, Год журнала: 2023, Номер unknown

Опубликована: Фев. 9, 2023

Abstract Global climate change and sea level rise have led to increased losses from flooding. Accurate prediction of floods is essential mitigating flood in coastal cities. Physically based models cannot satisfy the demand for real-time urban flooding due their computational complexity. In this study, we proposed a hybrid modeling approach rapid floods, coupling physically model with light gradient boosting machine (LightGBM) model. A hydrological–hydraulic was used provide sufficient data LightGBM on personal computer storm water management (PCSWMM). The variables related rainfall, tide level, location points were as input To improve accuracy, hyperparameters are optimized by grid search algorithm K-fold cross-validation. Taking Haidian Island, Hainan Province, China case optimum values learning rate, number estimators, leaves 0.11, 450, 12, respectively. Nash-Sutcliffe efficiency coefficient (NSE) test set 0.9896, indicating that has reliable predictions outperforms random forest (RF), extreme (XGBoost), k-nearest neighbor (KNN). From model, analyzed dominant predicting inundation depth Gini index study area. provides scientific reference control cities considering its superior performance efficiency.

Язык: Английский

---

Risk-driven composition decoupling analysis for urban flooding prediction in high-density urban areas using Bayesian-Optimized LightGBM

Journal of Cleaner Production, Год журнала: 2024, Номер 457, С. 142286 - 142286

Опубликована: Апрель 20, 2024

Язык: Английский

---