Artificial Intelligence Techniques in Hydrology and Water Resources Management
The sustainable management of water cycles is crucial in the context of climate change and global warming. It involves managing global, regional, and local water cycles, as well as urban, agricultural, and industrial water cycles, to conserve water resources and their relationships with energy, food...
Sábháilte in:
| Formáid: | Online |
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| Teanga: | Béarla |
| Foilsithe / Cruthaithe: |
MDPI - Multidisciplinary Digital Publishing Institute
2023
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| Ábhair: | |
| Rochtain ar líne: | ONIX_20230623_9783036577852_141 |
| Clibeanna: |
Níl clibeanna ann, Bí ar an gcéad duine le clib a chur leis an taifead seo!
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| _version_ | 1869515122235408384 |
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| collection | Directory of Open Access Books |
| description | The sustainable management of water cycles is crucial in the context of climate change and global warming. It involves managing global, regional, and local water cycles, as well as urban, agricultural, and industrial water cycles, to conserve water resources and their relationships with energy, food, microclimates, biodiversity, ecosystem functioning, and anthropogenic activities. Hydrological modeling is indispensable for achieving this goal, as it is essential for water resources management and the mitigation of natural disasters. In recent decades, the application of artificial intelligence (AI) techniques in hydrology and water resources management has led to notable advances. In the face of hydro-geo-meteorological uncertainty, AI approaches have proven to be powerful tools for accurately modeling complex, nonlinear hydrological processes and effectively utilizing various digital and imaging data sources, such as ground gauges, remote sensing tools, and in situ Internet of Things (IoT) devices. The thirteen research papers published in this Special Issue make significant contributions to long- and short-term hydrological modeling and water resources management under changing environments using AI techniques coupled with various analytics tools. These contributions, which cover hydrological forecasting, microclimate control, and climate adaptation, can promote hydrology research and direct policy making toward sustainable and integrated water resources management. |
| format | Online |
| id | doab-20.500.12854ir-100909 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1009092023-06-23T09:52:47Z Artificial Intelligence Techniques in Hydrology and Water Resources Management Chang, Fi-John Chang, Li-Chiu Chen, Jui-Fa ANN roadside IoT sensors simulations of the gridded rainstorms 2D inundation simulation and real-time error correction weather types and features meteorological feature extraction artificial neural network self-organizing map (SOM) urban agriculture resource utilization efficiency urban northern Taiwan machine learning random forest regression analysis support vector machine threshold rainfall threshold runoff XGBoost stochastic rainfall generator Huff rainfall curve copula GeoAI artificial intelligence hydrological hydraulic fluvial water quality geomorphic modeling anomaly detection deep reinforcement learning telemetry water level time series ensemble multi-model ensemble precipitation forecasting persian gulf deep learning dam inflow RNN LSTM GRU hyperparameter rainfall time series artificial neural networks Multiple Linear Regression Chania CNN ELM temporary rivers hydrological extremes multivariate stochastic model autoregressive model Markov model daily temperature temperature generator Bayesian neural network forecasting uncertainty multi-step ahead forecasting probabilistic streamflow forecasting variational inference smart microclimate-control system (SMCS) system dynamics water–energy–food nexus agricultural resilience hydroinformatics hydrological modeling early warning uncertainty sustainability The sustainable management of water cycles is crucial in the context of climate change and global warming. It involves managing global, regional, and local water cycles, as well as urban, agricultural, and industrial water cycles, to conserve water resources and their relationships with energy, food, microclimates, biodiversity, ecosystem functioning, and anthropogenic activities. Hydrological modeling is indispensable for achieving this goal, as it is essential for water resources management and the mitigation of natural disasters. In recent decades, the application of artificial intelligence (AI) techniques in hydrology and water resources management has led to notable advances. In the face of hydro-geo-meteorological uncertainty, AI approaches have proven to be powerful tools for accurately modeling complex, nonlinear hydrological processes and effectively utilizing various digital and imaging data sources, such as ground gauges, remote sensing tools, and in situ Internet of Things (IoT) devices. The thirteen research papers published in this Special Issue make significant contributions to long- and short-term hydrological modeling and water resources management under changing environments using AI techniques coupled with various analytics tools. These contributions, which cover hydrological forecasting, microclimate control, and climate adaptation, can promote hydrology research and direct policy making toward sustainable and integrated water resources management. 2023-06-23T09:52:45Z 2023-06-23T09:52:45Z 2023 book ONIX_20230623_9783036577852_141 9783036577852 9783036577845 https://directory.doabooks.org/handle/20.500.12854/100909 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/7377 https://mdpi.com/books/pdfview/book/7377 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-7784-5 10.3390/books978-3-0365-7784-5 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036577852 9783036577845 302 Basel open access |
| spellingShingle | ANN roadside IoT sensors simulations of the gridded rainstorms 2D inundation simulation and real-time error correction weather types and features meteorological feature extraction artificial neural network self-organizing map (SOM) urban agriculture resource utilization efficiency urban northern Taiwan machine learning random forest regression analysis support vector machine threshold rainfall threshold runoff XGBoost stochastic rainfall generator Huff rainfall curve copula GeoAI artificial intelligence hydrological hydraulic fluvial water quality geomorphic modeling anomaly detection deep reinforcement learning telemetry water level time series ensemble multi-model ensemble precipitation forecasting persian gulf deep learning dam inflow RNN LSTM GRU hyperparameter rainfall time series artificial neural networks Multiple Linear Regression Chania CNN ELM temporary rivers hydrological extremes multivariate stochastic model autoregressive model Markov model daily temperature temperature generator Bayesian neural network forecasting uncertainty multi-step ahead forecasting probabilistic streamflow forecasting variational inference smart microclimate-control system (SMCS) system dynamics water–energy–food nexus agricultural resilience hydroinformatics hydrological modeling early warning uncertainty sustainability Artificial Intelligence Techniques in Hydrology and Water Resources Management |
| title | Artificial Intelligence Techniques in Hydrology and Water Resources Management |
| title_full | Artificial Intelligence Techniques in Hydrology and Water Resources Management |
| title_fullStr | Artificial Intelligence Techniques in Hydrology and Water Resources Management |
| title_full_unstemmed | Artificial Intelligence Techniques in Hydrology and Water Resources Management |
| title_short | Artificial Intelligence Techniques in Hydrology and Water Resources Management |
| title_sort | artificial intelligence techniques in hydrology and water resources management |
| topic | ANN roadside IoT sensors simulations of the gridded rainstorms 2D inundation simulation and real-time error correction weather types and features meteorological feature extraction artificial neural network self-organizing map (SOM) urban agriculture resource utilization efficiency urban northern Taiwan machine learning random forest regression analysis support vector machine threshold rainfall threshold runoff XGBoost stochastic rainfall generator Huff rainfall curve copula GeoAI artificial intelligence hydrological hydraulic fluvial water quality geomorphic modeling anomaly detection deep reinforcement learning telemetry water level time series ensemble multi-model ensemble precipitation forecasting persian gulf deep learning dam inflow RNN LSTM GRU hyperparameter rainfall time series artificial neural networks Multiple Linear Regression Chania CNN ELM temporary rivers hydrological extremes multivariate stochastic model autoregressive model Markov model daily temperature temperature generator Bayesian neural network forecasting uncertainty multi-step ahead forecasting probabilistic streamflow forecasting variational inference smart microclimate-control system (SMCS) system dynamics water–energy–food nexus agricultural resilience hydroinformatics hydrological modeling early warning uncertainty sustainability |
| topic_facet | ANN roadside IoT sensors simulations of the gridded rainstorms 2D inundation simulation and real-time error correction weather types and features meteorological feature extraction artificial neural network self-organizing map (SOM) urban agriculture resource utilization efficiency urban northern Taiwan machine learning random forest regression analysis support vector machine threshold rainfall threshold runoff XGBoost stochastic rainfall generator Huff rainfall curve copula GeoAI artificial intelligence hydrological hydraulic fluvial water quality geomorphic modeling anomaly detection deep reinforcement learning telemetry water level time series ensemble multi-model ensemble precipitation forecasting persian gulf deep learning dam inflow RNN LSTM GRU hyperparameter rainfall time series artificial neural networks Multiple Linear Regression Chania CNN ELM temporary rivers hydrological extremes multivariate stochastic model autoregressive model Markov model daily temperature temperature generator Bayesian neural network forecasting uncertainty multi-step ahead forecasting probabilistic streamflow forecasting variational inference smart microclimate-control system (SMCS) system dynamics water–energy–food nexus agricultural resilience hydroinformatics hydrological modeling early warning uncertainty sustainability |
| url | ONIX_20230623_9783036577852_141 |