Floods are often caused by short-term heavy rainfall. An Integrated Flood Analysis System (IFAS) model is good at runoff simulation and a Long Short-Term Memory (LSTM) model is good at learning massive data and realizing rainfall forecast. In this paper, the applicability of the IFAS model to runoff simulation in the Tokachi River basin and the LSTM model to forecast hourly rainfall was studied, and the accuracy of flood prediction was also studied by inputting the optimal rainfall data forecasted by the LSTM model into the IFAS model. The research results show that the IFAS model can accurately simulate the runoff process in the Tokachi River basin. In the calibration period and the verification period, the Nash–Sutcliffe efficiency coefficient (NSE) of all simulation results are above 0.75; the LSTM model can achieve forecast hourly rainfall with high precision, the NSE of best forecast results is 0.86; the IFAS model can achieve flood prediction with high precision by using the optimal rainfall data forecasted by the LSTM model, the NSE of simulation result is 0.81. The above conclusions show that it is of great significance to combine the hourly rainfall forecasted by the LSTM model with the IFAS model for flood prediction.

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

Geothermal resource is green and clean energy, and geothermal field is widely distributed in the world. Its development and utilization has little harm to the environment, can change the current situation of energy consumption mainly based on fossil energy, reduce carbon emissions, and promote the development of techniques for sustainable processing of natural resources. However, each geothermal field has its own characteristic structure, origin, and storage, so it is necessary to carry out targeted research. In this paper, the geothermal characteristics and geological characteristics of the geothermal belt in Lushan County, China are analyzed by means of remote sensing interpretation, field investigation and observation, geophysical exploration, long-term observation, pumping test, and hydrochemical analysis. Result of this study shows that the geothermal belt of Lushan geothermal fields is as a result of primary thermal control and heat conduction structures of the near east-west Checun-Xiatang deep fault as well as secondary thermal control and heat conduction structures of near north-east and north-west secondary faults; and annual recoverable geothermal energy of whole geothermal field is 4.41 × 1011 MJ. The research results will be beneficial for the development and utilization of Lushan hot springs. At the same time, it also provides reference for more geothermal research.

Accurate runoff simulation is of great importance to understand watershed hydrologic cycle process, effective utilize water resources and respond flood disaster. Hydrologic model is one of the main tools for runoff simulation research and the continuous improvement in Machine Learning offers powerful tools for modeling of hydrologic process. This research took the runoff process of the Atsuma River basin in Hokkaido from 2015 to 2019 as object, proposed a special machine learning framework: Long-and Short-term Time-series Network (LSTNet) for runoff simulation, discussed the accuracy for runoff simulation of LSTNet model with (multivariate LSTNet Model) or without (univariate LSTNet Model) meteorological factors and Soil and Water Assessment Tool (SWAT) model respectively, analyzed the model selection for runoff simulation under different data conditions in the basin. The Nash-Sutcliffe efficiency coefficients (NSE) of the runoff simulation results in the validation (test) period were 0.633 (SWAT model), 0.643 (multivariate LSTNet model), and 0.716 (univariate LSTNet model) respectively. The results show that the accuracies of the two models for runoff simulation in the Atsuma River basin are all very high. SWAT model has prominent advantages in runoff simulation and shortcomings. LSTNet model shows great advantages and potential in runoff simulation. In summary, when target basin’ s data is accurate and complete, the accuracy of SWAT model in runoff simulation is high and stable. When the target basin lacks data or the quality of data is poor, LSTNet model can realize high-precision runoff simulation only based on the measured runoff data, which has a strong application.