scholarly journals An Integrated Approach for Modeling Wetland Water Level: Application to a Headwater Wetland in Coastal Alabama, USA

Water ◽  
2018 ◽  
Vol 10 (7) ◽  
pp. 879 ◽  
Author(s):  
Mehdi Rezaeianzadeh ◽  
Latif Kalin ◽  
Mohamed Hantush
Keyword(s):  
Water Level ◽  
Integrated Approach ◽  
Wetland Water ◽  
Headwater Wetland

scholarly journals Modeling coordinated operation of multiple hydropower reservoirs at a continental scale using artificial neural network: the case of Brazilian hydropower system

RBRH ◽  
2021 ◽  
Vol 26 ◽  
Author(s):  
João Paulo Lyra Fialho Brêda ◽  
Rodrigo Cauduro Dias de Paiva ◽  
Olavo Corrêa Pedrollo ◽  
Otávio Augusto Passaia ◽  
Walter Collischonn
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Balance ◽  
Water Level ◽  
Integrated Approach ◽  
Water Levels ◽  
Ann Model ◽  
Storage Reservoirs ◽  
Ann Models ◽  
Artificial Neural

ABSTRACT Reservoirs considerably affect river streamflow and need to be accurately represented in environmental impact studies. Modeling reservoir outflow represents a challenge to hydrological studies since reservoir operations vary with flood risk, economic and demand aspects. The Brazilian Interconnected Energy System (SIN) is an example of a unique and complex system of coordinated operation composed by more than 160 large reservoirs. We proposed and evaluated an integrated approach to simulate daily outflows from most of the SIN reservoirs (138) using an Artificial Neural Network (ANN) model, distinguishing run-of-the-river and storage reservoirs and testing cases whether outflow and level data were available as input. Also, we investigated the influence of the proposed input features (14) on the simulated outflow, related to reservoir water balance, seasonality, and demand. As a result, we verified that the outputs of the ANN model were mainly influenced by local water balance variables, such as the reservoir inflow of the present day and outflow of the day before. However, other features such as the water level of 4 large reservoirs that represent different regions of the country, which infers about hydropower demand through water availability, seemed to influence to some extent reservoirs outflow estimates. This result indicates advantages in using an integrated approach rather than looking at each reservoir individually. In terms of data availability, it was tested scenarios with (WITH_Qout) and without (NO_Qout and SIM_Qout) observed outflow and water level as input features to the ANN model. The NO_Qout model is trained without outflow and water level while the SIM_Qout model is trained with all input features, but it is fed with simulated outflows and water levels rather than observations. These 3 ANN models were compared with two simple benchmarks: outflow is equal to the outflow of the day before (STEADY) and the outflow is equal to the inflow of the same day (INFLOW). For run-of-the-river reservoirs, an ANN model is not necessary as outflow is virtually equal to inflow. For storage reservoirs, the ANN estimates reached median Nash-Sutcliffe efficiencies (NSE) of 0.91, 0.77 and 0.68 for WITH_, NO_ and SIM_Qout respectively, compared to a median NSE of 0.81 and 0.29 for the STEADY and INFLOW benchmarks respectively. In conclusion, the ANN models presented satisfactory performances: when outflow observations are available, WITH_Qout model outperforms STEADY; otherwise, NO_Qout and SIM_Qout models outperform INFLOW.

scholarly journals Integrated hydrodynamic and machine learning models for compound flooding prediction in a data-scarce estuarine delta

2022 ◽  
Author(s):  
Joko Sampurno ◽  
Valentin Vallaeys ◽  
Randy Ardianto ◽  
Emmanuel Hanert
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Water Level ◽  
Integrated Approach ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Flood Hazards ◽  
Area Of Interest ◽  
Machine Learning Model

Abstract. Flood forecasting based on water level modeling is an essential non-structural measure against compound flooding over the globe. With its vulnerability increased under climate change, every coastal area became urgently needs a water level model for better flood risk management. Unfortunately, for local water management agencies in developing countries building such a model is challenging due to the limited computational resources and the scarcity of observational data. Here, we attempt to solve the issue by proposing an integrated hydrodynamic and machine learning approach to predict compound flooding in those areas. As a case study, this integrated approach is implemented in Pontianak, the densest coastal urban area over the Kapuas River delta, Indonesia. Firstly, we built a hydrodynamic model to simulate several compound flooding scenarios, and the outputs are then used to train the machine learning model. To obtain a robust machine learning model, we consider three machine learning algorithms, i.e., Random Forest, Multi Linear Regression, and Support Vector Machine. The results show that this integrated scheme is successfully working. The Random Forest performs as the most accurate algorithm to predict flooding hazards in the study area, with RMSE = 0.11 m compared to SVM (RMSE = 0.18 m) and MLR (RMSE = 0.19 m). The machine-learning model with the RF algorithm can predict ten out of seventeen compound flooding events during the testing phase. Therefore, the random forest is proposed as the most appropriate algorithm to build a reliable ML model capable of assessing the compound flood hazards in the area of interest.

scholarly journals Response characteristics of water level dynamic of the Baiyangdian Wetland to climate change and human activities

2019 ◽  
Vol 131 ◽  
pp. 01071
Author(s):  
Xiaofan Qi ◽  
Dechao Yin ◽  
Yonghui An ◽  
Yushan Wang ◽  
Lei Gong
Keyword(s):  
Climate Change ◽  
Water Level ◽  
Human Activities ◽  
Time Lags ◽  
The Sustainable Development ◽  
Response Characteristics ◽  
Wetland Water ◽  
Water Conservancy ◽  
Baiyangdian Wetland ◽  
And Control

Water level dynamics of Wetlands are influenced by both climate change and human activities. Understanding the influence characteristics is important for the management of wetland water resources and ecology. Based on the water level dynamic and precipitation of the Baiyangdian Wetland, and the NPI index, the paper analyzes the response characteristics of the water level dynamic to the precipitation, and the teleconnections between the water level dynamic and the NPI by adopting method of wavelet analysis. Results show that climate change plays an important role on the response of the water level dynamic to the precipitation and also to the NPI, and human activities can significantly change the response characteristics. The response time lags of the water level dynamics to the NPI is longer than that of the water level dynamics to the precipitations, which indicates that the wetland precipitation might be partially influenced by the NPI. The knowledge of the response characteristics obtained in the paper is beneficial for water conservancy and control of the wetland, and is favorable for the sustainable development of its eco-environment.

scholarly journals The wetland water level process and habitat of migratory birds in Lake Poyang

2011 ◽  
Vol 23 (1) ◽  
pp. 129-135 ◽  
Author(s):  
LIU Chenglin ◽  
◽  
TAN Yinjing ◽  
LIN Liansheng ◽  
TAO Hainan ◽  
...  
Keyword(s):  
Water Level ◽  
Migratory Birds ◽  
Wetland Water ◽  
Lake Poyang ◽  
Level Process
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