scholarly journals Impact of Water Level Fluctuations on Landslide Deformation at Longyangxia Reservoir, Qinghai Province, China

2022 ◽  
Vol 14 (1) ◽  
pp. 212
Author(s):  
Shufen Zhao ◽  
Runqiang Zeng ◽  
Hongxue Zhang ◽  
Xingmin Meng ◽  
Zonglin Zhang ◽  
...  
Keyword(s):  
Water Level ◽  
Single Point ◽  
Water Levels ◽  
Monitoring Data ◽  
Water Level Fluctuations ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Landslide Deformation ◽  
Level Monitoring ◽  
Water Level Monitoring

The construction of Longyangxia Reservoir has altered the hydrogeological conditions of its banks. Infiltration and erosion caused by the periodic rise and fall of the water level leads to collapse of the reservoir banks and local deformation of the landslide. Due to heterogeneous topographic characteristics across the region, water level also varies between different location. Previous research on the influence of fluctuations in reservoir water level on landslide deformation has focused on single-point monitoring of specific slopes, and single-point water level monitoring data have often been used instead of water level data for the entire reservoir region. In addition, integrated remote sensing methods have seldom been used for regional analysis. In this study, the freely-available Landsat8 OLI and Sentinel-2 data were used to extract the water level of Longyangxia Reservoir using the NDWI method, and Sentinel-1A data were used to obtain landslide deformation time series using SBAS-InSAR technology. Taking the Chana, Chaxi, and Mangla River Estuary landslides (each having different reservoir water level depths) as typical examples, the influence of changes in reservoir water level on the deformation of three wading landslides was analyzed. Our main conclusions are as follows: First, the change in water level is the primary external factor controlling the deformation velocity and trend of landslides in the Longyangxia Reservoir, with falling water levels having the greatest influence. Second, the displacement of the Longyangxia Reservoir landslides lags water level changes by 0 to 62 days. Finally, this study provides a new method applicable other areas without water level monitoring data.

AUTOMATIC WATER LEVEL MONITORING WITH IOT AND LPWAN

2019 ◽  
pp. 95-103
Author(s):  
Krum Videnov ◽  
Vanya Stoykova
Keyword(s):  
Water Level ◽  
Real Time ◽  
Early Warning ◽  
Water Sources ◽  
Water Levels ◽  
Water Level Fluctuations ◽  
Level Monitoring ◽  
Water Level Monitoring

Monitoring water levels of lakes, streams, rivers and other water basins is of essential importance and is a popular measurement for a number of different industries and organisations. Remote water level monitoring helps to provide an early warning feature by sending advance alerts when the water level is increased (reaches a certain threshold). The purpose of this report is to present an affordable solution for measuring water levels in water sources using IoT and LPWAN. The assembled system enables recording of water level fluctuations in real time and storing the collected data on a remote database through LoRaWAN for further processing and analysis.

scholarly journals Simulation of Agent-Based Negative Selection Model (ABNSM) for Reservoir Water Level Monitoring

2020 ◽  
Vol 1529 ◽  
pp. 032004
Author(s):  
Siti Mazura Che Doi ◽  
Norita Md Norwawi ◽  
Roesnita Ismail ◽  
Mohd Helmy Abd Wahab ◽  
Syed Zulkarnain Syed Idrus
Keyword(s):  
Water Level ◽  
Negative Selection ◽  
Selection Model ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Agent Based ◽  
Level Monitoring ◽  
Water Level Monitoring

scholarly journals Numerical Analysis of Slope Stability under Reservoir Water Level Fluctuations Using a FEM-LEM-Combined Method

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Qingxiang Meng ◽  
Kun Qian ◽  
Lin Zhong ◽  
Jinjian Gu ◽  
Yue Li ◽  
...  
Keyword(s):  
Slope Stability ◽  
Water Level ◽  
Large Scale ◽  
Numerical Study ◽  
Soil Strength ◽  
Water Levels ◽  
Water Level Fluctuations ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
The Impact

Large-scale slopes at the banks of reservoirs pose a serious threat to the safety of hydropower stations. The fluctuation of the reservoir water level is a key factor in the slope stability. However, the parameters to describe the relationship among water content, matric suction, and soil strength are difficult to measure using unsaturated soil strength theory. To solve this problem, a simple FEM-LEM-combined scheme considering pore pressure, seepage force, and strength weakening is presented to calculate the safety factor. A numerical study on the impact of reservoir water level fluctuations on stability of a glaciofluvial deposit slope is implemented. Two typical profiles are used to estimate the stability of the glaciofluvial deposit slope in response to rising and lowering water levels. The results indicate that this method proposed a simple and efficient tool for water level-induced slope stability analysis.

scholarly journals Application of Neural Network Models and ANFIS for Water Level Forecasting of the Salve Faccha Dam in the Andean Zone in Northern Ecuador

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2011
Author(s):  
Pablo Páliz Larrea ◽  
Xavier Zapata Ríos ◽  
Lenin Campozano Parra
Keyword(s):  
Neural Network ◽  
Water Level ◽  
Clustering Algorithm ◽  
Network Models ◽  
Water Levels ◽  
Subtractive Clustering ◽  
Neural Network Models ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Maximum Delay

Despite the importance of dams for water distribution of various uses, adequate forecasting on a day-to-day scale is still in great need of intensive study worldwide. Machine learning models have had a wide application in water resource studies and have shown satisfactory results, including the time series forecasting of water levels and dam flows. In this study, neural network models (NN) and adaptive neuro-fuzzy inference systems (ANFIS) models were generated to forecast the water level of the Salve Faccha reservoir, which supplies water to Quito, the Capital of Ecuador. For NN, a non-linear input–output net with a maximum delay of 13 days was used with variation in the number of nodes and hidden layers. For ANFIS, after up to four days of delay, the subtractive clustering algorithm was used with a hyperparameter variation from 0.5 to 0.8. The results indicate that precipitation was not influencing input in the prediction of the reservoir water level. The best neural network and ANFIS models showed high performance, with a r > 0.95, a Nash index > 0.95, and a RMSE < 0.1. The best the neural network model was t + 4, and the best ANFIS model was model t + 6.

Smart Water Level Monitoring System for Farmers

2019 ◽  
pp. 213-228
Author(s):  
Nalina Suresh ◽  
Valerianus Hashiyana ◽  
Victor Panduleni Kulula ◽  
Shreekanth Thotappa
Keyword(s):  
Water Level ◽  
Monitoring System ◽  
Cost Effective ◽  
Weather Conditions ◽  
Water Levels ◽  
Measurement Systems ◽  
Smart Water ◽  
Level Measurement ◽  
Level Monitoring ◽  
Water Level Monitoring

With advancement in technology and ever-changing weather conditions, accurate and affordable water level measurement systems has become necessary for farmers. This therefore brings about the need for a system incorporating the use of IoT technology that will monitor water levels at a cost-effective price with accurate and dependable results. The prototype will monitor water levels on a regular basis and the data captured will be stored in a database to help farmers improve the way they manage their water resource. Farmers will be able to monitor the water levels from any location at any given time. This chapter focuses on a Smart Water Level Monitoring System for Farmers and provides a smart way to manage water resources on farms in the most cost-effective and convenient manner for farmers.

scholarly journals Stability Analysis of Hydrodynamic Pressure Landslides with Different Permeability Coefficients Affected by Reservoir Water Level Fluctuations and Rainstorms

Water ◽  
2017 ◽  
Vol 9 (7) ◽  
pp. 450 ◽  
Author(s):  
Faming Huang ◽  
Xiaoyan Luo ◽  
Weiping Liu
Keyword(s):  
Water Level ◽  
Permeability Coefficient ◽  
Hydrodynamic Pressure ◽  
Water Level Fluctuations ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Stability Coefficient ◽  
Permeability Coefficients ◽  
Landslide Stability ◽  
The Stability

It is significant to study the variations in the stability coefficients of hydrodynamic pressure landslides with different permeability coefficients affected by reservoir water level fluctuations and rainstorms. The Sifangbei landslide in Three Gorges Reservoir area is used as case study. Its stability coefficients are simulated based on saturated-unsaturated seepage theory and finite element analysis. The operating conditions of stability coefficients calculation are reservoir water level variations between 175 m and 145 m, different rates of reservoir water level fluctuations, and a three-day continuous rainstorm. Results show that the stability coefficient of the hydrodynamic pressure landslide decreases with the drawdown of the reservoir water level, and a rapid drawdown rate leads to a small stability coefficient when the permeability coefficient ranges from 1.16 × 10−6 m/s to 4.64 × 10−5 m/s. Additionally, the landslide stability coefficient increases as the reservoir water level increases, and a rapid increase in the water level leads to a high stability coefficient when the permeability coefficient ranges from 1.16 × 10−6 m/s to 4.64 × 10−5 m/s. The landslide stability coefficient initially decreases and then increases as the reservoir water level declines when the permeability coefficient is greater than 4.64 × 10−5 m/s. Moreover, for structures with the same landslide, the landslide stability coefficient is most sensitive to the change in the rate of reservoir water level drawdown when the permeability coefficient increases from 1.16 × 10−6 m/s to 1.16 × 10−4 m/s. Additionally, the rate of decrease in the stability coefficient increases as the permeability coefficient increases. Finally, the three-day rainstorm leads to a significant reduction in landslide stability, and the rate of decrease in the stability coefficient initially increases and then decreases as the permeability coefficient increases.

scholarly journals Research and Application of Key Technologies for Dynamic Control of Reservoir Water Level in Flood Season

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3576
Author(s):  
Jun Zhang ◽  
Yaowu Min ◽  
Baofei Feng ◽  
Weixin Duan
Keyword(s):  
Water Supply ◽  
Water Level ◽  
Real Time ◽  
Flood Control ◽  
Dynamic Control ◽  
Water Levels ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Danjiangkou Reservoir ◽  
Flood Season

In today’s reservoir operation study, it is urgent to solve the issues on improving flood resource utilization, maximizing reservoir impoundment, and guaranteeing water supply through real-time regulation optimization under the premise of ensuring flood control safety and taking risks properly. Based on previous studies, the key real-time operation technologies for dynamic control of reservoir water levels in flood season are summarized. The Danjiangkou Reservoir was taken as an example, the division of flood stages, reservoir water level requirements for improving water supply guarantee, dynamic control indexes of reservoir water level for beneficial use in stages during the flood season, and flood control dispatching indexes are proposed. Moreover, a practicable real-time flood forecast operation scheme for Danjiangkou Reservoir was compiled. Its application in 2017 indicated that the established scheme can provide strong technical support to ensure the overall benefits of Danjiangkou Reservoir, including flood control, water supply, and power generation.

scholarly journals ICESat-2 Reservoir Applications: A case study from the largest man-made reservoir in India

2021 ◽  
Author(s):  
Surajit Ghosh ◽  
Atul Kaushik
Keyword(s):  
Water Level ◽  
Water Levels ◽  
Water Bodies ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Reservoir Storage ◽  
Inland Water ◽  
Change Impact ◽  
And Performance ◽  
Inland Water Bodies

Monitoring inland water levels is crucial for understanding hydrological processes to climate change impact leading to policy implementation. Satellite altimetry has proved to be an excellent technique to precisely measure water levels of rivers, lakes, and other inland water bodies. The ATL13 product of ICESat-2 space-borne LiDAR is solely dedicated to inland water bodies. The water surface heights were derived from ICESat-2's strong beams, and performance was assessed with respect to reservoir gauge observations. Statistical measurements were used to understand the agreement (R2= 0.99, %RMSE=0.08) among the datasets. An R2 value of 0.99 was observed between ICESat-2 derived water level anomaly and the reservoir storage anomaly. This study provides a unique opportunity to utilize the ATL13 data product to study reservoir water level variation and estimate the reservoir's storage. The methodology can also be helpful to understand the reservoir storage variation in a data-sparse region.

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