標(biāo)題: Titlebook: Applications of Machine Learning in Hydroclimatology; Roshan Srivastav,Purna C. Nayak Book 2025 The Editor(s) (if applicable) and The Auth [打印本頁] 作者: Sentry 時(shí)間: 2025-3-21 16:32
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書目名稱Applications of Machine Learning in Hydroclimatology讀者反饋學(xué)科排名
作者: 使無效 時(shí)間: 2025-3-21 22:03 作者: 安慰 時(shí)間: 2025-3-22 04:09 作者: 基因組 時(shí)間: 2025-3-22 06:19 作者: 精確 時(shí)間: 2025-3-22 10:18
Applications of Machine Learning in Hydroclimatology978-3-031-64403-0Series ISSN 2520-1298 Series E-ISSN 2520-1301 作者: 動(dòng)機(jī) 時(shí)間: 2025-3-22 16:14
Strafrechtliche Aspekte der Managerhaftungs. Over the years, various modelling frameworks have been developed and tested worldwide. These modelling frameworks, generally, are classified into three types: physics-based or process-based, black-box or data-driven and conceptual models. The physics-based models try to represent the catchment sc作者: 歸功于 時(shí)間: 2025-3-22 18:12 作者: HACK 時(shí)間: 2025-3-23 00:27 作者: 流浪者 時(shí)間: 2025-3-23 05:03 作者: 憂傷 時(shí)間: 2025-3-23 09:09 作者: Delirium 時(shí)間: 2025-3-23 12:42 作者: Definitive 時(shí)間: 2025-3-23 16:31 作者: Cantankerous 時(shí)間: 2025-3-23 22:02 作者: Isthmus 時(shí)間: 2025-3-23 22:52
Franco Dammacco,Domenico Sansonnonowledge, providing a wealth of rainfall output from diverse global climate models. However, the inherent spatial resolution of these models often limits their direct applicability for regional scale assessments. This study introduces a Hybrid Gamma Probability Distribution (HGPD) Model designed for作者: libertine 時(shí)間: 2025-3-24 02:52
https://doi.org/10.1007/978-4-431-67005-6f data and availability of recorded daily rainfalls may affect the feasibility of a larger range of rainfall-based studies in light of repercussions from climate change and extreme hydro-meteorological phenomena. A daily gridded rainfall time series dataset (2007–2020) over Mizoram state is construc作者: hysterectomy 時(shí)間: 2025-3-24 10:24 作者: 疾馳 時(shí)間: 2025-3-24 11:31 作者: 種植,培養(yǎng) 時(shí)間: 2025-3-24 18:46 作者: Nebulous 時(shí)間: 2025-3-24 22:01 作者: 松軟無力 時(shí)間: 2025-3-24 23:26 作者: 品牌 時(shí)間: 2025-3-25 06:48
River Discharge Forecasting in Mahanadi River Basin Based on Deep Learning Techniques,est for forecasting in engineering because of their forecasting accuracies. Two deep learning techniques long-short term memory (LSTM) and bidirectional long-short term memory (Bi-LSTM) have been applied and compared for forecasting river discharge data in this study. In LSTM, input flows in one dir作者: 大喘氣 時(shí)間: 2025-3-25 10:40 作者: 蛙鳴聲 時(shí)間: 2025-3-25 12:41
Genetic Algorithm-Aided Neural Network for Sediment Critical Shear Stress Modeling,ment under clay influence which was limited to their own experimental data. The present study aims to develop an ANN model to compute CSS of coarser sediment present in mobile channel bed made of cohesive sediment mixture. The proposed model was optimized using parent-selected operator GA (genetic a作者: narcotic 時(shí)間: 2025-3-25 19:13 作者: 過時(shí) 時(shí)間: 2025-3-25 22:47 作者: 啤酒 時(shí)間: 2025-3-26 04:12
The High-Resolution Statistical Downscaling of Seasonal Rainfall Forecasts Models for Comprehensivenowledge, providing a wealth of rainfall output from diverse global climate models. However, the inherent spatial resolution of these models often limits their direct applicability for regional scale assessments. This study introduces a Hybrid Gamma Probability Distribution (HGPD) Model designed for作者: 的是兄弟 時(shí)間: 2025-3-26 04:45
Prediction of Rainfall in One of the Wettest Regions in India Using Machine Learning Methods,f data and availability of recorded daily rainfalls may affect the feasibility of a larger range of rainfall-based studies in light of repercussions from climate change and extreme hydro-meteorological phenomena. A daily gridded rainfall time series dataset (2007–2020) over Mizoram state is construc作者: dissolution 時(shí)間: 2025-3-26 08:48 作者: Nomadic 時(shí)間: 2025-3-26 15:21 作者: Defraud 時(shí)間: 2025-3-26 18:13
Strafrechtliche Aspekte der Managerhaftungo predict streamflow. Because Artificial Neural Network (ANN) models forecast streamflow more accurately than time series models did in the 1990s, they became increasingly popular. Artificial intelligence (AI), an offshoot of computer science, can analyse long-series and large-scale hydrological dat作者: 尊嚴(yán) 時(shí)間: 2025-3-26 21:53 作者: 你正派 時(shí)間: 2025-3-27 04:02
Model-Based User Interface Reengineering,for model evaluation. Results show that both models perform well but Bi-LSTM is slightly better than LSTM in terms of both statistical measures. These results of both models are with different values of hyperparameters. The performance of these models can be different with the same values of hyperpa作者: Charitable 時(shí)間: 2025-3-27 07:26 作者: GLOOM 時(shí)間: 2025-3-27 12:43
Franco Dammacco,Domenico Sansonnod that linear ranked selected GA-ANN is the best-fitted model for both training and testing data. The CSS obtained for optimized input values for clay fraction by weight (CP), weighted geometric standard deviation of sediment mixture (SM), and dimensionless dry bulk unit weight of cohesive sediment 作者: orient 時(shí)間: 2025-3-27 17:01 作者: Erythropoietin 時(shí)間: 2025-3-27 21:20
Franco Dammacco,Domenico Sansonnoor of 0.142, compared to LSTM’s coefficient of determination of 0.865 and root mean square error of 0.148. GRU also had a lower mean absolute error of 0.097 compared to LSTM’s mean absolute error of 0.101. The study concludes that both GRU and LSTM can be used effectively in SSL modeling. However, G作者: 干旱 時(shí)間: 2025-3-27 22:40 作者: capillaries 時(shí)間: 2025-3-28 05:05
https://doi.org/10.1007/978-4-431-67005-6f each dataset in the case of extreme rainfalls with respect to IMDAA gridded rainfall (i.e., IMD Re-analysis), which has been taken as the observed/reference dataset. In this study, the quantile mapping and linear scaling bias correction methods are utilized to correct the rainfall datasets. The pr作者: STELL 時(shí)間: 2025-3-28 07:45
2520-1298 s the impact of climate change on flood risks, drought occurrences, and reservoir operations, providing insights into how these phenomena affect water resource management...To provide practical solutions, the b978-3-031-64405-4978-3-031-64403-0Series ISSN 2520-1298 Series E-ISSN 2520-1301 作者: 手勢(shì) 時(shí)間: 2025-3-28 13:40 作者: Hot-Flash 時(shí)間: 2025-3-28 15:55
Applications of Physics-Guided Machine Learning Architectures in Hydrology,tical forms. According to a few recent studies, deep-machine learning-based models that come under the category of data-driven models outperform the well-established conceptual hydrological models. These studies reported that the deep-learning models can better capture the information available in t作者: 步兵 時(shí)間: 2025-3-28 22:00 作者: 是突襲 時(shí)間: 2025-3-29 01:06
Estimation of Groundwater Levels Using Machine Learning Techniques,estimation. In addition, several studies from the recent past indicate the dominance of Ensemble Machine Learning in managing the sustainability of groundwater across the globe. So, the ability of ensemble machine learning models in estimating the groundwater level is discussed in the chapter. Furth作者: ARK 時(shí)間: 2025-3-29 04:59 作者: Dorsal 時(shí)間: 2025-3-29 10:49 作者: 大廳 時(shí)間: 2025-3-29 11:29 作者: dysphagia 時(shí)間: 2025-3-29 15:49 作者: Generalize 時(shí)間: 2025-3-29 19:49
Predictive Deep Learning Models for Daily Suspended Sediment Load in the Missouri River, USA,or of 0.142, compared to LSTM’s coefficient of determination of 0.865 and root mean square error of 0.148. GRU also had a lower mean absolute error of 0.097 compared to LSTM’s mean absolute error of 0.101. The study concludes that both GRU and LSTM can be used effectively in SSL modeling. However, G
