Water treatment is an important process, as it improves water quality and makes it better for any end use, whether it be drinking, industrial use, irrigation, water recreation, or any other kind of use. Turbidity is one of the fundamental measurements of the clarity of water in water treatment. Specifically, this component is an optical feature of the amount of light on scatter particles when light is shined on a water sample. It is crucial in water reservoirs to provide clean water, which is difficult to manage and predict. Hence, this study focuses on the use of robust deep learning models to analyze time-series data in order to predict the water quality of turbidity in a reservoir area. Deep learning models may become an alternative solution in predicting water quality because of their accuracy. This study is divided into two parts: (a) the first part uses the optical bands of blue (B), green (G), red (R), and infrared (IR) to build a regression function to monitor turbidity in water, and (b) the second part uses a hybrid model to analyze time-series turbidity data with the recursive neural network (RNN2) model. The selected models’ accuracies are compared based on the accuracy using the input data, forecasting level, and training time. The analysis shows that these models have their strengths and limitations under different analyzed conditions. Generally, RNN2 shows the performance regarding the root-mean-square error (RMSE) evaluation metric. The most significant finding is that the RNN2 model is suitable for the accurate prediction of water quality. The RMSE is used to facilitate a comparison of the accuracy of the sampling data. In the training model, the training data have an RMSE of 20.89, and the testing data have an RMSE of 30.11. The predicted R-squared values in the RNN2 model are 0.993 (training data) and 0.941 (testing data).

Taiwan is located at the junction of plates in which the stratum is relatively unstable, resulting in frequent earthquakes. Driftwood has always been regarded as a precious asset that enables ecoscientists to track earthquakes. In the event of a typhoon or heavy rain, the surface water flows to flush the woods from the hills to the coast. More specifically, a large rainfall or earthquake may cause floods and collapses, and the trees in the forest will be washed down. Therefore, this study used high-resolution images to build an image database of the new north coast of Taiwan, and a deep learning approach is incorporated to classify the driftwoods. To improve the interpretation of driftwood in the remote images, we initially import eight pieces of textured information which are employed to the raw bands (B, G, R, and IR). The usage of spatial information image extraction technology is incorporated into a deep learning analysis using two parallel approaches. The generative adversarial network (GAN) is used to analyze the color images alongside an ancillary image with texture information. Most of the salt–pepper effects are produced by applying a high-resolution thematic map, and an error matrix is generated to compare the differences between them. The raw data (original R + G + B + IR) images, when analyzed using GAN, have about 70% overall classification outcomes. Not all of the driftwood can be detected. By applying the texture information to the parallel approach, the overall accuracy is enhanced to 78%, and about 80% of the driftwood can be recognized.