# Using Artificial Intelligence for Space Challenges: A Survey
## 4 State of the Art
### Works on Earth Observation
Furthermore, the K-means algorithm was used to find high-level categories of similar messages. Finally, deep-learning-based techniques were advocated to extract useful information from social media images. In [127], a machine-learning-based disaster prediction model was proposed to identify disaster situations by analyzing information coming from social messaging platforms such as Twitter. Multinomial Naive Bayes and XGB classifiers are used to individuate both tweets that are relevant to a flood and tweets irrelevant to a flood, with an accuracy of above 90%. Climate change is having a considerable impact on the future of lives on Earth by modifying crucial climatic parameters such as temperature, precipitation, and wind [128; 129]. On the other hand, the future implications of greenhouse gas emissions are discussed [130; 131] related to these parameters. AI offers the means to reduce uncontrollable risks and predict extreme weather events and climate conditions [132] and, at the same time, to assess human responsibilities and duties [133; 134; 135]. As suggested in [136], leveraging AI techniques holds the opportunities to combat the climate crisis effectively by finding procedures to monitor, analyze, and predict climate change indicators. One application is proposed in [137], where generative adversarial networks are used to develop an interactive climate impact visualization tool that is able to predict and visualize the future of a specific location based on the effect of current climate indicators, such as floods, storms, and wildfires. The study [138] goes in the same direction to predict daily energy consumption of a buildnig using a data-driven explainable AI model. Instead, the authors of [139] show the effectiveness of support vector machine models to predict solar and wind energy resources. The authors of [138] apply different AI models to the proposed problem: extreme gradient boosting, linear regression, random forest, support vector machine, deep neural networks, and long short-term memory. The impact of climate change on precipitation and temperature over Famagusta, Nicosia, and Kyrenia stations is analyzed in [140]. With the support of a neural network, an adaptive neuro-fuzzy inference system, and multiple linear regression models, these parameters are analyzed and forecasted for 2018-2040. Climate change is intensifying challenges in water resource management [141]. Indeed, understanding our oceans and monitoring the surface water quality are presently priorities that AI can meet, as suggested in [142; 143]. Notably, in [142], a systematic literature analysis on the application of AI models is presented, focusing on the physical location of experiments, input parameters, and output metrics. In [144], a concern about the application of responsible AI in the water domain is investigated. Although recent contributions show that this domain is limited for AI, insights on its optimization are reported. The monitoring of water is challenging and crucial for every organism on Earth and for water precipitation measurements. For example, the authors of [145] adopt artificial neural networks and multiple linear regression for the downscaling of satellite precipitation estimates. In [146], non-linear autoregressive neural networks and long short-term memory deep learning algorithms were developed to model and predict water quality. Then, support vector machines, K-nearest neighbors, and Naive Bayes were used to predict water classification. The authors of [147] exploit an adaptive neuro-fuzzy inference system to predict the water quality index, a feed-forward neural network and a K-nearest neighbors algorithm to classify water quality. In [147], the focus is shifted towards the case of drinking water. The presence of bacteria in water is monitored by automated identification of colors and their intensity from sensor image using a deep convolutional neural networks algorithm developed in [148]. The problem of air pollutants requires attention and puts public health in danger if not adequately monitored [149].