The prediction accuracy of hourly air temperature is generally poor because of random changes, long time series, and the nonlinear relationship between temperature and other meteorological elements, such as air pressure, dew point, and wind speed. In this study, two deep-learning methods—a convolutional neural network (CNN) and long short-term memory (LSTM)—are integrated into a network model (CNN–LSTM) for hourly temperature prediction. The CNN reduces the dimensionality of the time-series data, while LSTM captures the long-term memory of the massive temperature time-series data. Training and validation sets are constructed using 60,133 hourly meteorological data (air temperature, dew point, air pressure, wind direction, wind speed, and cloud amount) obtained from January 2000 to October 2020 at the Yinchuan meteorological station in China. Mean absolute error (MAE), mean absolute percentage error (MAPE), and goodness of fit are used to compare the performances of the CNN, LSTM, and CNN–LSTM models. The results show that MAE, MAPE, RMSE, and PBIAS from the CNN–LSTM model for hourly temperature prediction are 0.82, 0.63, 2.05, and 2.18 in the training stage and 1.02, 0.8, 1.97, and −0.08 in the testing stage. Average goodness of fit from the CNN–LSTM model is 0.7258, higher than the CNN (0.5291), and LSTM (0.5949) models. The hourly temperatures predicted by the CNN–LSTM model are highly consistent with the measured values, especially for long time series of hourly temperature data.

Predicting the air temperature based on spatially accurate simulations is helpful to agricultural production, commercial activities, air transportation, water transportation, power supply, and national defense. Traditional prediction is not generally based on the time series obtained from multiple meteorological stations and from a spatial perspective. In this study, a deep convolution neural network with long short-term memory (CNN-LSTM) is constructed to extract the spatiotemporal features of temperature and the correlation between meteorological elements. The accuracies of the simulated and the predicted temperatures are spatially visualized by using the Kriging interpolation method. The accuracy of air temperature obtained from the CNN-LSTM was compared with those obtained from the CNN and the LSTM to verify its performance. The results show that there were prominent spatial variations in the temperature, with a latitudinal zonal structure in the southern Ningxia and a radial zonal structure in the northern Ningxia, China. The accuracies of the simulated and predicted temperature were high in areas with a small range of the annual temperature. The accuracies of the mean monthly temperature simulated by the CNN-LSTM in spring and autumn were higher than that in the summer. The predicted annual average temperature increased each year from 2020 to 2025. The CNN-LSTM had a higher accuracy of simulating and predicting the temperature as well as a better generalization ability than the CNN and LSTM.

The spatial allocation of water resources is optimised using the multi-objective functions and multi-constrained conditions of the Pareto ant colony algorithm (PACA). The objective function is the highest benefit to the economy, society and the environment, while the constraints include water supply, demand and quality. The PACA is improved by limiting local pheromone scope and dynamically updating global pheromone levels. Since both strategies guide the ant towards borders of high-pheromone concentration, the new approach enhances the global search capability and convergence speed. Programming, database management and interface tools are then integrated into geographic information systems (GIS) software. The study area is located in Zhenping County, Henan Province, China, and water resource data are obtained using remote sensing (RS) and GIS technology. The improved PACA is solved in the GIS environment. Optimal spatial allocation schemes are obtained for surface, ground and transferred water and the model yields optimal spatial benefit schemes of water resources, embracing economic, social and ecological benefits. The results of improved PACA are superior to those of other intelligent optimisation algorithms, including the ant colony algorithm, multi-objective genetic algorithm and back-propagation artificial neural network. Therefore, the integration of RS, GIS and PACA can effectively optimise the large-scale, multi-objective allocation of water resources. The model also enhances the global search capability, convergence speed and result precision, and can potentially solve other optimal spatial problems with multi-objective functions.

Limited development ecological zones (LDEZs) are often located in poverty-stricken, ecologically vulnerable areas where ethnic minorities reside. Studies on optimal spatial land-use allocation in LDEZs can promote economic and intensive land use, improve soil quality, facilitate local socioeconomic development, and maintain environmental stability. In this study, we optimized spatial land-use allocations in an LDEZ using the geographic information system (GIS) and a genetic ant colony algorithm (GACA). The multi-objective function considers economic benefits and ecological green equivalents, and improves soil erosion. We developed the GACA by integrating a genetic algorithm (GA) with an ant colony algorithm (ACA). This avoids a large number of redundant iterations and the low efficiency of the GA, and the slow convergence speed of the ACA. The study area is located in Pengyang County, Ningxia, China, which is a typical LDEZ. The land-use data were interpreted from remote sensing (RS) images and GIS. We determined the optimal spatial land-use allocations in the LDEZ using the GACA in the GIS environment. We compared the original and optimal spatial schemes in terms of economic benefits, ecological green equivalents, and soil erosion. The results of the GACA were superior to the original allocation, the ACA, and the multi-objective genetic algorithm, in terms of the optimum, time, and robust performance indexes. We also present some suggestions for the reasonable development and protection of LDEZs.