Applacation of artificial intelligence in economic analysis of land consolidation projects

Land consolidation projects can be considered as investments with an economic return. From this perspective, it is crucial to prioritise land consolidation initiatives in regions where the potential returns are higher. At this juncture, the economic analysis of land consolidation projects merits particular attention. The profitability of consolidation in a given field can be determined by conducting economic analyses of the aforementioned process. The study employed economic analysis in three distinct fields. The locations in question are the Kizilcaboluk neighbourhood of the Tavas neighbourhood of the Denizli province, the Vakif neighbourhood of the Tavas neighbourhood of the Denizli province, and the Konak neighbourhood of the Gölhisar neighbourhood of the Burdur province. In the study, the initial step was to conduct an economic analysis of consolidation using the classical method. Subsequently, a data set was constructed for artificial intelligence analysis, utilising the findings of the classical method for analysis. Artificial intelligence analyses were performed using the created data set. The plant production variable, labour input variable, fertiliser input variable and water input variable were employed in the analyses, which were conducted using the classical method. Upon completion of the analytical process, the variable representing the change in profitability following consolidation was obtained. Following the completion of the consolidation studies, there has been a notable increase in profitability across the three neighbourhoods. In the Kizilcaboluk neighbourhood, profitability has risen by approximately 85%, while in the Vakif neighbourhood, it has increased by around 70%. In the Konak neighbourhood, the rise in profitability has been more modest, at approximately 6%. The most significant consequence of this outcome is that the irrigation channel will not be constructed in the Konak neighbourhood as part of the consolidation project. Furthermore, the introduction of irrigation canals to the Kizilcaboluk neighbourhood and Konak neighbourhood has led to an increase in productivity. This has also resulted in a diversification of agricultural products, with farmers turning into high-profitability crops such as corn. This situation has a considerable impact on the enhancement of profitability. The process of artificial intelligence analysis comprises a series of stages, including the creation of data sets, the normalisation of data, the separation of training and test data, the application of clustering techniques, cross-validation and the testing of results. During analyses, the test data were estimated by training the model with inputs and outputs. The inputs employed are as follows: agricultural area variable, parcel number variable, crop production variable, labour input variable, fertiliser input variable and water input variables. The profitability change subsequent to consolidation was employed as the output variable. The profitability change that occurred subsequent to the consolidation was successfully estimated using data pertaining to the pre-consolidation situation, as part of the study. Furthermore, sensitivity analyses were conducted to ascertain the relative importance of the criteria employed in the determination of the result's accuracy. The most accurate results were obtained by employing parcel area, parcel number and labour input variables.