## 1 Introduction
Algorithms integrated by artificial neural networks have recently become an important focus of research, as they represent a robust approach to modeling complex land use behaviors. Unlike the multivariate approach, they do not require assumptions about spatial autocorrelation and multicollinearity of the data [16]. In relation to ecosystem valuation of paramo land uses, different methodologies based on monetary terms and remote sensing have been used. For example. In southeastern Ecuador, a local analysis of the perception of paramo EV was carried out using the avoided cost method and spatial data. The authors determined that the land uses that caused the greatest impact on the area's VE were agriculture and livestock and that the VE from 2010 to 2020 decreased by 6%. The limitation of the avoided cost methodology is that it uses dose-response functions, which require varied data for valuation, which may not be available to the researcher [17]. In another study conducted in San Juan Chimborazo (Ecuador), the EV of Andean soils was studied using techniques based on remote sensing, analysis of physical and chemical parameters, and market and administrative values. The monetary weights used in the study were obtained by applying the theories of opportunity cost and benefit transfer methodologies [18]. The EV results of the study are general since the economic indices used were not processed by any adjustment method, to represent the reality of the area. In the northern and southern departments of Santander, in Colombia, the EV of the paramo was determined using travel cost, benefit transfer, and satellite information methodologies. The authors described those environmental services (ES) from soil provision have a direct impact on cultural ES [19]. The limitation of the travel cost methodology is that it only allows valuing impacts on local public goods based on ex-post situations. Outside South America, in Buddhasanti (Nepal) and northwestern Yunnan (China), several authors have calculated the EV of soils using integrated valuation of ecosystem services and compensation (InVEST) models and remote sensing. Their work described that the loss of EV is mainly linked to population expansion and the agricultural frontier and that the most affected areas are those located in latitudes accessible to human development [20]. The drawback of the compensation method for calculating EV is that it is based on the perception of the maximum willingness to pay by ES beneficiaries and often this perception does not reflect reality. To date, most of the studies analyzing VA related to paramo land use in South America have been conducted in Colombia. Few relevant ecosystem studies exist in Ecuador, most of which cover the northern zone, followed by the southern zone [21]. Furthermore, the few existing studies use methodologies that are not extrapolable, as they are based on local factors. For the study of land use in the paramo, this work used an artificial multilayer perception (MLP) artificial neural network, which has gained recognition as a highly reliable neural network model. MLP has demonstrated its effectiveness in land cover classification and pattern recognition tasks [22; 23]. The neural network model consists of an input layer, hidden layers, and an output. MLP uses spectral and spatial information from satellite images as input information to the network [24]. The model can learn to associate these input features with land use classes through the learning process. Although it is a reliable prediction model, its performance is often impaired by errors in the adjustment of variable weights by the modeler [25]. This work will strive to reduce this type of error as much as possible by adjusting the weights through a comparison of the training values, the network output values, and the expected values. The error will be reduced by comparing the interactions of the weights on the output signals, through the feedback of the network, based on the adjustment of the functions used in the synaptic links of the neurons.