scholarly journals Unraveling the Role of Human Activities and Climate Variability in Water Level Changes in the Taihu Plain Using Artificial Neural Network

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 720 ◽  
Author(s):  
Yuefeng Wang ◽  
Hossein Tabari ◽  
Youpeng Xu ◽  
Yu Xu ◽  
Qiang Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Climate Variability ◽  
Water Level ◽  
Human Activities ◽  
Nonlinear Dependence ◽  
Rapid Urbanization ◽  
Evaluation Period ◽  
Water Level Changes ◽  
Artificial Neural

Water level, as a key indicator for the floodplain area, has been largely affected by the interplay of climate variability and human activities during the past few decades. Due to a nonlinear dependence of water level changes on these factors, a nonlinear model is needed to more realistically estimate their relative contribution. In this study, the attribution analysis of long-term water level changes was performed by incorporating multilayer perceptron (MLP) artificial neural network. We took the Taihu Plain in China as a case study where water level series (1954–2014) were divided into baseline (1954–1987) and evaluation (1988–2014) periods based on abrupt change detection. The results indicate that climate variables are the dominant driver for annual and seasonal water level changes during the evaluation period, with the best performance of the MLP model having precipitation, evaporation, and tide level as inputs. In the evaluation period, the contribution of human activities to water level changes in the 2000s is higher than that in the 1990s, which indicates that human activities, including the rapid urbanization, are playing an important role in recent years. The influence of human activities, especially engineering operations, on water level changes in the 2000s is more evident during the dry season (March-April-May (MAM) and December-January-February (DJF)).

scholarly journals Study of Water Level-Discharge Relationship Using Artificial Neural Network (ANN) in Sungai Gumum, Tasik Chini Pahang Malaysia

2010 ◽  
Vol 5 (1) ◽  
pp. 20-26 ◽  
Author(s):  
Othman Jaafar ◽  
Mohd Ekhwan Hj. Toriman ◽  
Mushrifah Hj. Idris ◽  
S.A. Sharifah M ◽  
Hafizan Hj. Juahir ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
Tasik Chini ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

scholarly journals Artificial Neural Network Model for Estimation of Suspended Sediment Load in Krishna River Basin, India

2020 ◽  
Vol 9 (3) ◽  
pp. 3701-3705
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
Suspended Sediment ◽  
River Basin ◽  
Flood Control ◽  
Sediment Load ◽  
Water Discharge ◽  
Suspended Sediment Load ◽  
Artificial Neural

The correct assessment of amount of sediment during design, management and operation of water resources projects is very important. Efficiency of dam has been reduced due to sedimentation which is built for flood control, irrigation, power generation etc. There are traditional methods for the estimation of sediment are available but these cannot provide the accurate results because of involvement of very complex variables and processes. One of the best suitable artificial intelligence technique for modeling this phenomenon is artificial neural network (ANN). In the current study ANN techniques used for simulation monthly suspended sediment load at Vijayawada gauging station in Krishna river basin, Andhra Pradesh, India. Trial & error method were used during the optimization of parameters that are involved in this model. Estimation of suspended sediment load (SSL) is done using water discharge and water level data as inputs. The water discharge, water level and sediment load is collected from January 1966 to December 2005. This approach is used for modelled the SSL. By considering the results, ANN has the satisfactory performance and more accurate results in the simulation of monthly SSL for the study location.

Dam Safety Reliability Analysis Based on Artificial Neural Network

2011 ◽  
Vol 255-260 ◽  
pp. 3620-3625
Author(s):  
Hai Wei ◽  
Hua Shu Yang ◽  
Liang Wu ◽  
Yue Gui
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
Reliability Index ◽  
Safety Evaluation ◽  
Mathematic Model ◽  
Dam Safety ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Artificial Neural

There are many factors, such as climate, flood, material, geology, structure, management, to influence dam safety. So dam safety evaluation, involving many fields, is very complicated, and very difficult to establish mathematic model for assessment. Artificial Neural Network (ANN) has many obvious advantages to deal with these problems influenced by multi-factor, consequently is widely used in engineering fields. This paper considered water level, temperature, main factors influencing dam deformation, as random variables, employed ANN and statistical model to establish performance function of dam hidden trouble deformation and abnormal deformation. Then reliability theory was used to analyze dam safety reliability and sensitivity. The results show that temperature has great effect on probability of dam hidden trouble deformation and abnormal deformation than reservoir water level, due to great variability of temperature. Change of Reliability index of dam is contrary to reservoir water level. Temperature, especially average temperature in 10 days and 5 days, has great effect on sensitivity of reliability index than water level.

Flood Risk Pattern Recognition Using Chemometric Technique: A Case Study In Kuantan River Basin

2014 ◽  
Vol 72 (1) ◽  
Author(s):  
Ahmad Shakir Mohd Saudi ◽  
Hafizan Juahir ◽  
Azman Azid ◽  
Mohd Khairul Amri Kamarudin ◽  
Mohd Fadhil Kasim ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
River Basin ◽  
Stream Flow ◽  
Pearson Correlation ◽  
Suspended Solid ◽  
Test Analysis ◽  
Risk Class ◽  
Artificial Neural

Integrated Chemometric and Artificial Neural Network were being applied in this study to identify the main contributor for flood, predicting hydrological modelling and risk of flood occurrence at the Kuantan river basin. Based on the Correlation Test analysis, the relationship for Suspended Solid and Stream Flow with Water Level were very high with Pearson correlation of coefficient value more than 0.5. Factor Analysis had been carried out and based on the result, variables such as Stream Flow, Suspended Solid and Water Level turned out to be the major factors and had a strong factor pattern with the results of factor score with >0.7 respectively. Time series analysis was being employed and the limitation had been set up where the Upper Control Limit for Stream Flow, Suspended Solid and Water Level where at this level, it was predicted by using Artificial Neural Network (ANN) to be High Risk Class. The accuracy of prediction from this method stood at 97.8%.

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.

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