###### Abstract
Second, mapping land value distribution associated indicators (such as home rentals and prices) and modelling their spatiotemporal patterns are critical stages in analyzing and monitoring the local residential land market [6]. Third, variations in land value distribution mapping over time can provide insight into urban growth direction and patterns [7; 8]. Despite the critical points stated above, predicting fine-scale land value distribution in developing countries remains difficult. While the land transaction sample is recognized as a trustworthy data source for modelling land value distribution, it frequently does not include all plots in a city. As a result, more capable methodologies and tools are necessary to ensure complete coverage when estimating or forecasting the land value distribution in unseen areas. Additionally, the models must be capable of effectively learning and fitting the complicated nonlinear interactions between real estate values and potential causes such as the natural environment, economic policy, geography, and community characteristics [9; 10]. Land value varies according to surrounding factors, such as environmental, social, and economic situations [11; 12]. By analyzing land value variation, many studies have found that when social and economic conditions are terrible, the land value is usually low [13]. Some studies have found that land value was related to the quality of the schools in an area. In contrast, some studies have found that land value was related to commercial districts [14; 15]. In addition, other studies have found that public transportation [16] and the environment in the community, such as wetlands [17] and green spaces [18; 19], had some effects on the land value. Therefore, the focus of this study is mainly on using environmental factors to predict land value. Methodologically, many studies have used interpolation methods such as inverse distance weighting and Kriging to study the distribution of land value [20; 21; 22]. Some studies have applied interpolation methods in many areas to confirm data anomaly [23]. In this study, we used the spatial value estimation technique to create the spatial value for the effect of each factor on land value. Machine learning is one of the popular data estimation techniques that enables a system to learn from sample data without a programmer's involvement. Another benefit of this technique is that the machine learning can learn from existing data to determine its correct result. These techniques have been widely used in various parts of the world. in recent years, as evidenced by studies on image classification, climate prediction, road traffic injuries, landslide prediction, and poverty prediction etc. [24; 25; 26; 27]. Moreover, many studies have found that the machine learning algorithm technique has higher accuracy than traditional statistical methods [9] and some studies have also used machine learning to estimate house values [28]. We also found that machine learning has been used to determine the factors that affect land value in New York, United States [29], and predicting property prices in Hong Kong [30], but there have been relatively few studies on this type of research. Therefore, from these findings, we aim to apply machine learning techniques to estimate the land value and to identify the factors affecting land value in the Talingchan district, Bangkok.