scholarly journals Inland Reservoir Water Quality Inversion and Eutrophication Evaluation Using BP Neural Network and Remote Sensing Imagery: A Case Study of Dashahe Reservoir

Water ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 2844
Author(s):  
Yanhu He ◽  
Zhenjie Gong ◽  
Yanhui Zheng ◽  
Yuanbo Zhang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Water Quality ◽  
Bp Neural Network ◽  
Quality Parameters ◽  
Remote Sensing Images ◽  
Linear Inversion ◽  
Reservoir Water ◽  
Chl A ◽  
Inversion Model

In this study, an inland reservoir water quality parameters’ inversion model was developed using a back propagation (BP) neural network to conduct reservoir eutrophication evaluation, according to multi-temporal remote sensing images and field observations. The inversion model based on the BP neural network (the BP inversion model) was applied to a large inland reservoir in Jiangmen city, South China, according to the field observations of five water quality parameters, namely, Chlorophyl-a (Chl-a), Secchi Depth (SD), total phosphorus (TP), total nitrogen (TN), and Permanganate of Chemical Oxygen Demand (CODMn), and twelve periods of Landsat8 satellite remote sensing images. The reservoir eutrophication was evaluated. The accuracy of the BP inversion model for each water parameter was compared with that of the linear inversion model, and the BP inversion models of two parameters (i.e., Chl-a and CODMn) with larger fluctuation range were superior to the two multiple linear inversion models due to the ability of improving the generalization of the BP neural network. The Dashahe Reservoir was basically in the state of mesotrophication and light eutrophication. The area of light eutrophication accounted for larger proportions in spring and autumn, and the reservoir inflow was the main source of nutrient salts.

scholarly journals Remote sensing inversion of lake water quality parameters based on ensemble modelling

2020 ◽  
Vol 143 ◽  
pp. 02007
Author(s):  
Li Xiaojuan ◽  
Huang Mutao ◽  
Li Jianbao
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Entropy Weight ◽  
Ensemble Modelling ◽  
Chl A ◽  
Inversion Model ◽  
Entropy Weight Method ◽  
Weight Method

In this paper, combined with water quality sampling data and Landsat8 satellite remote sensing image data, the inversion model of Chl-a and TN water quality parameter concentration was constructed based on machine learning algorithm. After the verification and evaluation of the inversion results of the test samples, Chl-a TN inversion model with high correlation between model test results and measured data was selected to participate in remote sensing inversion ensemble modelling of water quality parameters. Then, the ensemble remote sensing inversion model of water quality parameters was established based on entropy weight method and error analysis. By applying the idea of ensemble modelling to remote sensing inversion of water quality parameters, the advantages of different models can be integrated and the precision of water quality parameters inversion can be improved. Through the evaluation and comparative analysis of the model results, the entropy weight method can improve the inversion accuracy to some extent, but the improvement space is limited. In the verification of the two methods of ensemble modelling based on error analysis, compared with the optimal results of a single model, the determination coefficient (R2) of Chlorophyll a and TN concentration inversion results was increased from 0.9288 to 0.9313 and from 0.8339 to 0.8838, and the root mean square error was decreased from 14.2615 μ/L to 10.4194 μ/L and from1.1002mg/L to 0.8621mg/L. At the same time, with the increase of the number of models involved in the set modelling, the inversion accuracy is higher.

scholarly journals Application of a Hybrid Optimized BP Network Model to Estimate Water Quality Parameters of Beihai Lake in Beijing

2019 ◽  
Vol 9 (9) ◽  
pp. 1863 ◽  
Author(s):  
Jianzhuo Yan ◽  
Zongbao Xu ◽  
Yongchuan Yu ◽  
Hongxia Xu ◽  
Kaili Gao
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Bp Neural Network ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Percentage Error ◽  
Bp Network ◽  
Hydrogen Ion Concentration ◽  
Absolute Percentage Error ◽  
Study Results

Nowadays, freshwater resources are facing numerous crises and pressures, resulting from both artificial and natural process, so it is crucial to predict the water quality for the department of water environment protection. This paper proposes a hybrid optimized algorithm involving a particle swarm optimization (PSO) and genetic algorithm (GA) combined BP neural network that can predict the water quality in time series and has good performance in Beihai Lake in Beijing. The data sets consist of six water quality parameters which include Hydrogen Ion Concentration (pH), Chlorophyll-a (CHLA), Hydrogenated Amine (NH4H), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), and electrical conductivity (EC). The performance of the model was assessed through the absolute percentage error ( A P E m a x ), the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the coefficient of determination ( R 2 ). Study results show that the model based on PSO and GA to optimize the BP neural network is able to predict the water quality parameters with reasonable accuracy, suggesting that the model is a valuable tool for lake water quality estimation. The results show that the hybrid optimized BP model has a higher prediction capacity and better robustness of water quality parameters compared with the traditional BP neural network, the PSO-optimized BP neural network, and the GA-optimized BP neural network.

scholarly journals Single-Tree Detection in High-Resolution Remote-Sensing Images Based on a Cascade Neural Network

2018 ◽  
Vol 7 (9) ◽  
pp. 367 ◽  
Author(s):  
Dong Tianyang ◽  
Zhang Jian ◽  
Gao Sibin ◽  
Shen Ying ◽  
Fan Jing
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
High Resolution ◽  
Bp Neural Network ◽  
Detection Method ◽  
Detection Methods ◽  
Remote Sensing Images ◽  
Tree Detection ◽  
Single Tree ◽  
Cascade Neural Network

Traditional single-tree detection methods usually need to set different thresholds and parameters manually according to different forest conditions. As a solution to the complicated detection process for non-professionals, this paper presents a single-tree detection method for high-resolution remote-sensing images based on a cascade neural network. In this method, we firstly calibrated the tree and non-tree samples in high-resolution remote-sensing images to train a classifier with the backpropagation (BP) neural network. Then, we analyzed the differences in the first-order statistic features, such as energy, entropy, mean, skewness, and kurtosis of the tree and non-tree samples. Finally, we used these features to correct the BP neural network model and build a cascade neural network classifier to detect a single tree. To verify the validity and practicability of the proposed method, six forestlands including two areas of oil palm in Thailand, and four areas of small seedlings, red maples, or longan trees in China were selected as test areas. The results from different methods, such as the region-growing method, template-matching method, BP neural network, and proposed cascade-neural-network method were compared considering these test areas. The experimental results show that the single-tree detection method based on the cascade neural network exhibited the highest root mean square of the matching rate (RMS_Rmat = 90%) and matching score (RMS_M = 68) in all the considered test areas.

scholarly journals WaterNet: A Convolutional Neural Network for Chlorophyll-a Concentration Retrieval

2020 ◽  
Vol 12 (12) ◽  
pp. 1966 ◽  
Author(s):  
Muhammad Aldila Syariz ◽  
Chao-Hung Lin ◽  
Manh Van Nguyen ◽  
Lalu Muhamad Jaelani ◽  
Ariel C. Blanco
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Chlorophyll A ◽  
Remote Sensing Images ◽  
Two Stage ◽  
Chl A ◽  
The Neural Network

The retrieval of chlorophyll-a (Chl-a) concentrations relies on empirical or analytical analyses, which generally experience difficulties from the diversity of inland waters in statistical analyses and the complexity of radiative transfer equations in analytical analyses, respectively. Previous studies proposed the utilization of artificial neural networks (ANNs) to alleviate these problems. However, ANNs do not consider the problem of insufficient in situ samples during model training, and they do not fully utilize the spatial and spectral information of remote sensing images in neural networks. In this study, a two-stage training is introduced to address the problem regarding sample insufficiency. The neural network is pretrained using the samples derived from an existing Chl-a concentration model in the first stage, and the pretrained model is refined with in situ samples in the second stage. A novel convolutional neural network for Chl-a concentration retrieval called WaterNet is proposed which utilizes both spectral and spatial information of remote sensing images. In addition, an end-to-end structure that integrates feature extraction, band expansion, and Chl-a estimation into the neural network leads to an efficient and effective Chl-a concentration retrieval. In experiments, Sentinel-3 images with the same acquisition days of in situ measurements over Laguna Lake in the Philippines were used to train and evaluate WaterNet. The quantitative analyses show that the two-stage training is more likely than the one-stage training to reach the global optimum in the optimization, and WaterNet with two-stage training outperforms, in terms of estimation accuracy, related ANN-based and band-combination-based Chl-a concentration models.

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