The purpose of this paper is to explore the performance of ridge regression and the random forest model improved by genetic algorithm in predicting the Boston house price data set and conduct a comparative analysis. To achieve it, the data is divided into training set and test set according to the ratio of 70-30. The RidgeCV library is used to select the best regularization parameter for the Ridge regression model, and for the random forest model, the genetic algorithm is used to optimize the model's hyperparameters. The result shows that compared with ridge regression, the random forest model improved by genetic algorithm can perform better in the regression problem of Boston house prices.

Remote sensing technologies have revolutionized forestry analysis by providing valuable information about forest ecosystems on a large scale. This review article explores the latest advancements in remote sensing tools that leverage optical, thermal, RADAR, and LiDAR data, along with state-of-the-art methods of data processing and analysis. We investigate how these tools, combined with artificial intelligence (AI) techniques and cloud-computing facilities, enhance the analytical outreach and offer new insights in the fields of remote sensing and forestry disciplines. The article aims to provide a comprehensive overview of these advancements, discuss their potential applications, and highlight the challenges and future directions. Through this examination, we demonstrate the immense potential of integrating remote sensing and AI to revolutionize forest management and conservation practices.

The paper deals with the issues of the influence of forest cover on the average annual runoff of rivers in the Pripyat River basin. In the study area, under the influence of solar radiation, the temperature of the air and the soil surface increases, evaporation from the water surface also increases, and the moisture content of the upper layers of the soil decreases. In general, with an increase in forest cover, the annual layer of the runoff of the studied rivers increases, as well as with an increase in the amount of precipitation (in contrast to the runoff of short-term floods). However, with a forest cover of more than 20%–30% and a relatively small amount of precipitation, the runoff decreases, which is associated with the retention of part of the precipitation by the forest cover. With a large amount of precipitation and low forest cover, the runoff also decreases, which is probably due to the loss of precipitation water for evaporation, etc. The conducted studies show that, just as the forest affects water resources, the flow of moisture to watersheds also affects the state of forest systems. Moreover, this interaction is expressed by evaporation from forests. Under influence of change of a climate growth of evaporation is observed.

The effects of climate change are already being felt, including the failure to harvest several agricultural products. On the other hand, peatland requires good management because it is a high carbon store and is vulnerable as a contributor to high emissions if it catches fire. This study aims to determine the potential for livelihood options through land management with an agroforestry pattern in peatlands. The methods used are field observation and in-depth interviews. The research location is in Kuburaya Regency, West Kalimantan, Indonesia. Several land use scenarios are presented using additional secondary data. The results show that agroforestry provides more livelihood options than monoculture farming or wood. The economic contribution is very important so that people reduce slash-and-burn activities that can increase carbon emissions and threaten the sustainability of peatland.

Root turnover is a key process of terrestrial ecosystem carbon cycle, which is of great significance to the study of soil carbon pool changes and global climate change. However, because there are many measurement and calculation methods of root turnover, the results obtained by different methods are quite different, and the current research on root turnover of forest ecosystem on the global regional scale is not sufficient, so the change law of root turnover of global forest ecosystem is still unclear. By collecting literature data and unifying the calculation method of turnover rate, this study integrates the spatial pattern of fine root turnover of five forest types in the world, and obtains the factors affecting fine root turnover of forest ecosystem in combination with soil physical and chemical properties and climate data. The results showed that there were significant differences in fine root turnover rate among different forest types, and it gradually decreased with the increase of latitude; the turnover rate of fine roots in forest ecosystem is positively correlated with annual average temperature and annual average precipitation; fine root turnover rate of forest ecosystem is positively correlated with soil organic carbon content, but negatively correlated with soil pH value. This study provides a scientific basis for revealing the law and mechanism of fine root turnover in forest ecosystem.