Because of the trends in population growth and rapid industrialization and urbanization, the intensity and structure of land use are undergoing great changes. Henan Province is an important economic province and a major grain producing and energy consumption area, and its land use plays a key role in the sustainable development of the whole of China. This study takes Henan Province as the research object, selects panel statistical data from 2010 to 2020, and discusses the land use structure (LUS) in Henan Province in terms of three aspects: information entropy, analysis of land use dynamic change, and land type conversion matrix. Based on the indicator system “social economy (SE)—ecological environment (EE)—agricultural production (AP)—energy consumption (EC)”, a land use performance (LUP) evaluation model was constructed to judge the performance of various land use types in Henan Province. Finally, the relational degree between LUS and LUP was calculated through the grey correlation. The results show that among the eight land use types in the study area since 2010, land used for water and water conservancy facilities increased by 4%. In addition, transport and garden land changed significantly, and was mainly converted from cultivated land (decreased by 6674 km2) and other land. From the perspective of LUP, the increase in ecological environment performance is the most obvious, while agriculture performance is lagging behind; it is worth paying attention to the energy consumption performance, which is decreasing year by year. There is an obvious correlation between LUS and LUP. LUS in Henan Province presents a gradually stable state, and the transformation of land types promotes LUP. Proposing an effective and convenient evaluation method to explore the relationship between LUS and LUP is very beneficial in helping stakeholders to actively focus more on optimizing land resource management and decision making for the coordinated and sustainable development among agricultural, socio-economic, eco-environmental, and energy systems.

Fishery catch forecasting is a crucial aspect of aquatic research because of its relevance to establishing effective fishery management and resource allocation systems. In this study, we aim to forecast and analyze fish catch by collaboratively processing data using methods at multiple scales. To this end, we propose two computational fishery catch forecasting models. A neural network model based on the multi-timescale features of a convolutional neural network and a long short-term memory neural network is proposed and implemented to forecast short-term measures for the daily catch in the eastern ports of Hokkaido, Japan. Similarly, we propose a long-term catch forecasting and analysis model combining the autoregressive integrated moving average (ARIMA) method and a neural network to explore short-term water temperature and long-term catch dependence in the case of sparse data; we implement this method to investigate the total monthly catch in Hokkaido. The experimental results demonstrate that the proposed methods were able to effectively forecast and analyze fishery catch based on different data scales, volumes, and other complex situations. This is also the first work in the field that considers multiple perspectives.INDEX TERMS ARIMA model, fishery catch forecasting, neural network.

Machine learning methods provide new alternative methods and ideas for runoff prediction. In order to improve the application of machine learning methods in the field of runoff prediction, we selected five rivers with different conditions from north to south in Japan as the research objects, and compared the six watersheds and different types methods of time series prediction in machine learning methods, to evaluate the accuracy and applicability of these machine learning methods for daily runoff prediction in different watersheds, and improve the commonality problem found in the prediction process. The results show that before the improvement, the prediction results of the six methods in Kushiro river, Yodogawa river and Shinano Gawa river are good. After the improvement, the runoff prediction errors of the six methods in the five watersheds are greatly reduced, and the prediction accuracy and applicability are greatly improved. Among them, the improved deep temporal convolutional network (DeepTCN) has the best prediction effect and applicability. Of all prediction results in the five watersheds, the NSE coefficients are above 0.94. In general, the improved DeepTCN has the best comprehensive prediction effect, and has the potential to be widely recommended for runoff prediction

Effective processing of the massive amounts of information generated by a sewage treatment plant's purification process helps reduce the operating costs of sewage purification while enhancing both control over and the reliability of the purification process. To predict multivariate time series data at sewage treatment plants, we propose a method based on neural expansion analysis for time series forecasting. In addition, we offer a method based on an N-BEATS autoencoder network that combines seasonality analysis with a class of support vector machine algorithms to detect data anomalies in sewage treatment. We also validate the proposed method and compare it with other mainstream machine learning and statistical methods. The results show that the proposed prediction and anomaly detection methods outperform other methods. The prediction results are improved by the highest to 22% compared with the other methods, while the accuracy of anomaly detection, 98%, is also highest among all methods tested. Moreover, the model is more scientific and flexible, with systematic potential and significance.INDEX TERMS Sewage treatment, N-BEATS model, predictions, anomaly detection.