Jiannan Cai

Bio: Jiannan Cai is an academic researcher. The author has an hindex of 1, co-authored 1 publications receiving 4 citations.

Evaluating traditional empirical models and BPNN models in monitoring the concentrations of chlorophyll-a and total suspended particulate of eutrophic and turbid waters

Abstract: In order to use in situ sensed reflectance to monitor the concentrations of chlorophyll-a (Chl-a) and total suspended particulate (TSP) of waters in the Pearl River Delta, which is featured by the highly developed network of rivers, channels and ponds, 135 sets of simultaneously collected water samples and reflectance were used to test the performance of the traditional empirical models (band ratio, three bands) and the machine learning models of a back-propagation neural network (BPNN). The results of the laboratory analysis with the water samples show that the Chl-a ranges from 3 to 256 µg·L−1 with an average of 39 µg·L−1 while the TSP ranges from 8 to 162 mg·L−1 and averages 42.5 mg·L−1. Ninety sets of 135 samples are used as training data to develop the retrieval models, and the remaining ones are used to validate the models. The results show that the proposed band ratio models, the three-band combination models, and the corresponding BPNN models are generally successful in estimating the Chl-a and the TSP, and the mean relative error (MRE) can be lower than 30% and 25%, respectively. However, the BPNN models have no better performance than the traditional empirical models, e.g., in the estimation of TSP on the basis of the reflectance at 555 and 750 nm (R555 and R750, respectively), the model of BPNN (R555, R750) has an MRE of 23.91%, larger than that of the R750/R555 model. These results suggest that these traditional empirical models are usable in monitoring the optically active water quality parameters of Chl-a and TSP for eutrophic and turbid waters, while the machine learning models have no significant advantages, especially when the cost of training samples is considered. To improve the performance of machine learning models in future applications on the basis of ground sensor networks, large datasets covering various water situations and optimization of input variables of band configuration should be strengthened.

Suspended sediment load prediction using long short-term memory neural network

Abstract: Rivers carry suspended sediments along with their flow These sediments deposit at different places depending on the discharge and course of the river However, the deposition of these sediments impacts environmental health, agricultural activities, and portable water sources Deposition of suspended sediments reduces the flow area, thus affecting the movement of aquatic lives and ultimately leading to the change of river course Thus, the data of suspended sediments and their variation is crucial information for various authorities Various authorities require the forecasted data of suspended sediments in the river to operate various hydraulic structures properly Usually, the prediction of suspended sediment concentration (SSC) is challenging due to various factors, including site-related data, site-related modelling, lack of multiple observed factors used for prediction, and pattern complexityTherefore, to address previous problems, this study proposes a Long Short Term Memory model to predict suspended sediments in Malaysia's Johor River utilizing only one observed factor, including discharge data The data was collected for the period of 1988-1998 Four different models were tested, in this study, for the prediction of suspended sediments, which are: ElasticNet Linear Regression (LR), Multi-Layer Perceptron (MLP) neural network, Extreme Gradient Boosting, and Long Short-Term Memory Predictions were analysed based on four different scenarios such as daily, weekly, 10-daily, and monthly Performance evaluation stated that Long Short-Term Memory outperformed other models with the regression values of 9201%, 9656%, 9671%, and 9945% daily, weekly, 10-days, and monthly scenarios, respectively

Remote Sensing of Chlorophyll-a in Xinkai Lake Using Machine Learning and GF-6 WFV Images

Abstract: Lake ecosystem eutrophication is a crucial water quality issue that can be efficiently monitored with remote sensing. GF-6 WFV with a high spatial and temporal resolution provides a comprehensive record of the dynamic changes in water quality parameters in a lake. In this study, based on GF-6 WFV images and the field sampling data of Xingkai Lake from 2020 to 2021, the accuracy of three machine learning models (RF: random forest; SVR: support vector regression; and BPNN: back propagation neural network) was compared by considering 11 combinations of surface reflectance in different wavebands as input variables for machine learning. We mapped the spatiotemporal variations of Chl-a concentrations in Xingkai Lake from 20192021 and integrated machine learning algorithms to demonstrate that RF obtained a better degree of derived-fitting (Calibration: N = 82, RMSE = 0.82 μg/L, MAE = 0.57 μg/L, slope = 0.94, and R2 = 0.98; Validation: N = 40, RMSE = 2.12 μg/L, MAE = 1.58 μg/L, slope = 0.91, R2 = 0.89, and RPD = 2.98). The interannual variation from 2019 to 2021 showed that the Chl-a concentration in Xingkai Lake was low from June to July, while maximum values were observed from October to November, thus showing significant seasonal differences. Spatial distribution showed that Chl-a concentrations were higher in Xiao Xingkai Lake than in Da Xingkai Lake. Nutrient inputs (N, P) and other environmental factors such as high temperature could have an impact on the spatial and temporal distribution characteristics of Chl-a, therefore, combining GF-6 WFV satellite images with RF could realize large-scale monitoring and be more effective. Our results showed that remote-sensing-based machine learning algorithms provided an effective method to monitor lake eutrophication as well as technical support and methodological reference for inland lake water quality parameter inversion.

Advances in Water Quality Monitoring and Assessment in Marine and Coastal Regions

Abstract: Coastal environments include several different habitat typologies, from shorelines to estuaries, and rocky and muddy environments [...]

Evaluation of the Online Music Flipped Classroom under Artificial Intelligence and Wireless Networks

Abstract: The study aims to explore the online music flipped classrooms based on artificial intelligence (AI) and wireless networks. A backpropagation neural network (BPNN) algorithm optimized by the genetic algorithm (GA) is proposed to evaluate the teaching quality of music flipped classrooms and analyze the problems in the current teaching mode. First, an evaluation index system is established for online music flipped classrooms; second, a questionnaire is designed according to the index system. After the data are collected, the GA-BPNN evaluation model is used to evaluate the teaching quality of the music flipped classrooms. Finally, the model’s performance is evaluated based on the forecast accuracy compared with the model implemented only by the BPNN. The simulation results show that the GA-BPNN evaluation model can effectively evaluate the teaching quality of flipped classrooms, and the evaluation results are objective and accurate. The model overcomes the shortcomings of traditional evaluation methods. The study has great practical significance and provides a basis for improving the teaching quality of online flipped classrooms.

Prediction of Sediment Yields Using a Data-Driven Radial M5 Tree Model

Abstract: Reliable estimations of sediment yields are very important for investigations of river morphology and water resources management. Nowadays, soft computing methods are very helpful and famous regarding the accurate estimation of sediment loads. The present study checked the applicability of the radial M5 tree (RM5Tree) model to accurately estimate sediment yields using daily inputs of the snow cover fraction, air temperature, evapotranspiration and effective rainfall, in addition to the flow, in the Gilgit River, Upper Indus Basin (UIB) tributary, Pakistan. The results of the RM5Tree model were compared with support vector regression (SVR), artificial neural network (ANN), multivariate adaptive regression spline (MARS), M5Tree, sediment rating curve (SRC) and response surface method (RSM) models. The resulting accuracy of the models was assessed using Pearson’s correlation coefficient (R2), the root-mean-square error (RMSE) and the mean absolute percentage error (MAPE). The prediction accuracy of the RM5Tree model during the testing period was superior to the ANN, MARS, SVR, M5Tree, RSM and SRC models with the R2, RMSE and MAPE being 0.72, 0.51 tons/day and 11.99%, respectively. The RM5Tree model predicted suspended sediment peaks better, with 84.10% relative accuracy, in comparison to the MARS, ANN, SVR, M5Tree, RSM and SRC models, with 80.62, 77.86, 81.90, 80.20, 74.58 and 62.49% relative accuracies, respectively.