A predictive model for Lake Chad total surface water area using remotely sensed and modeled hydrological and meteorological parameters and multivariate regression analysis

Policelli, F.; Hubbard, Alfred; Jung, Hahn Chul; Zaitchik, B. F.; Ichoku, Charles

doi:10.1016/j.jhydrol.2018.11.037

Cited by 20 publications

(9 citation statements)

References 23 publications

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“…This method effectively improves the accuracy of water level calculation by altimeter in different reaches of Yarlung Zangbo River. Based on remote sensing images, satellite altimetry data, and hydrometeorological parameters, Frederick et al [18] used datasets for multiple regression analysis to establish a prediction model of the total surface water area of Lake Chad. The correlation between the total surface water area in a given month and one or more other hydrological parameters was analyzed and verified.…”

Section: Introductionmentioning

confidence: 99%

“…Combined with Landsat series and Sentinel-2 satellite images, the functional relationship between the water level and water storage capacity is constructed, and Ngoring Lake has been continuously monitored for nearly 30 years, including the changes in water level, lake area, and water storage capacity. Compared with previous studies [16][17][18], the data and methods used in this paper are novel and have a longer observation time. It is of great benefit to the follow-up study of lakes.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring and Analysis of Water Level–Water Storage Capacity Changes in Ngoring Lake Based on Multisource Remote Sensing Data

Zhu

Zhao

Qiu

et al. 2022

Water

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Mastering the fluctuation of water levels and the water storage capacity of plateau lakes is greatly important for monitoring the water balance of the Tibetan Plateau and predicting regional and global climate change. The water level of plateau lakes is difficult to measure, and the ground measured data of long-time series are difficult to obtain. Ngoring Lake is considered in this study, using spaceborne single-photon lidar ICESat-2/ATL13 inland lake standard data products, the water level values provided by Hydroweb laboratory, and the image data of an optical remote sensing satellite. A new method is proposed in the absence of measured data. The method uses multisource remote sensing data to estimate the long-term changes in the water levels, surface area, and water storage capacity of Ngoring Lake in the past three decades. The results show that the water level values of ICESat-2 and Hydroweb on overlapping observation days are highly correlated, with R2 = 0.9776, MAE = 0.420 m, RMSE = 0.077 m, and the average absolute height difference is 0.049 m. The fusion of multiple altimetry data can obtain more continuous long-time series water-level observation results. From 1992 to 2021, the water body information of Ngoring Lake basin fluctuated greatly and showed different variation characteristics in different time periods. The lowest water level in January 1997 was approximately 4268.49 m, and it rose to its highest in October 2009, approximately 4272.44 m. The change in the water level in the basin was mainly affected by natural factors, such as precipitation, air temperature, and human activities. The analysis shows that ICESat-2 can be combined with other remote sensing data to realize the long-time series dynamic monitoring of plateau lakes, showing great advantages in the comprehensive observation of plateau lakes in no man’s land.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Monitoring and Analysis of Water Level–Water Storage Capacity Changes in Ngoring Lake Based on Multisource Remote Sensing Data

Zhu

Zhao

Qiu

et al. 2022

Water

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“…Trends in sediment fluxes have been investigated since before the 1990s, but did not include South America, Africa, and Oceania; however, in 2019, an update on this research was done that considered these areas where the focus was on rivers rather than on lakes [11]. This points out that regardless of extreme sensitivity to seasonal and environmental changes, with lakes, the rate at which sediment flux could contribute immensely to the water quality has still not been explored for better lake management remedies [12,13].…”

Section: Introductionmentioning

confidence: 99%

A Sediment Diagenesis Model of Seasonal Nitrate and Ammonium Flux Spatial Variation Contributing to Eutrophication at Taihu, China

Sarpong

Norgbey

et al. 2020

IJERPH

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Algal blooms have thrived on the third-largest shallow lake in China, Taihu over the past decade. Due to the recycling of nutrients such as nitrate and ammonium, this problem has been difficult to eradicate. Sediment flux, a product of diagenesis, explains the recycling of nutrients. The objective was to simulate the seasonal spatial variations of nitrate and ammonium flux. In this paper, sediment diagenesis modeling was applied to Taihu with Environmental Fluid Dynamics Code (EFDC). Latin hypercube sampling was used to create an input file from twelve (12) nitrogen related parameters of sediment diagenesis and incorporated into the EFDC. The results were analyzed under four seasons: summer, autumn, winter, and spring. The concentration of NH4–N in the sediment–water column increased from 2.744903 to 22.38613 (g/m3). In summer, there was an accumulation of ammonium in the water column. In autumn and winter, the sediment was progressively oxidized. In spring, low-oxygen conditions intensify denitrification. This allows algal blooms to continue to thrive, creating a threat to water quality sustainability. The sediment diagenesis model, coupled with water quality measured data, showed an average relative error for Total Nitrogen (TN) of 38.137%, making the model suitable. Future studies should simulate phosphate flux and measure sediment fluxes on the lake.

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“…To solve this problem, many approaches and methods are used, such as numerical methods, regression models, etc. [2,[4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. But due to the lack of works containing a description of the method of early detection of threats based on their early forecasting, it becomes relevant to use neural network approaches and technologies to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

Threat prediction in complex distributed systems using artificial neural network technology

Palchevsky

Khristodulo

Pavlov

2020

CPT2020 the 8th International Scientific Conference on Computing in Physics and Technology Proceedings

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In the context of this article, a method for detecting threats based on their forecasting and development in complex distributed systems is proposed. Initially, the relevance of the research topic is substantiated from the point of view of the prospective use of various methods in the framework of threat management and their forecasting in complex distributed systems. Based on the analysis of these methods, a proprietary forecasting method based on the second generation recurrent neural network (RNN) was proposed. The mathematical formulation of the problem is presented, as well as the structure of this neural network and its mathematical model of self-learning, which allows achieving more accurate (with less error) results in the framework of threat prediction (in this case, the level of water rise at gauging stations) in complex distributed systems. An analysis was also made of the effectiveness of the existing and proposed forecasting methods, which showed the stability of the neural network in relation to other forecasting methods: the error of the neural network is 3-20% of actual (real) water levels; the least squares method reaches up to 34.5%, the numerical method in a generalized form - up to 36%; linear regression model – up to 47.5%. Thus, the neural network allows a fairly stable forecast of the flood situation over several days, which allows special services to carry out flood control measures.

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A predictive model for Lake Chad total surface water area using remotely sensed and modeled hydrological and meteorological parameters and multivariate regression analysis

Cited by 20 publications

References 23 publications

Monitoring and Analysis of Water Level–Water Storage Capacity Changes in Ngoring Lake Based on Multisource Remote Sensing Data

Monitoring and Analysis of Water Level–Water Storage Capacity Changes in Ngoring Lake Based on Multisource Remote Sensing Data

A Sediment Diagenesis Model of Seasonal Nitrate and Ammonium Flux Spatial Variation Contributing to Eutrophication at Taihu, China

Threat prediction in complex distributed systems using artificial neural network technology

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