Abstract
An assessment of the rainfall station distribution in the mountainous area of the Regional Autonomous Corporation of Cundinamarca (CAR, for its acronym in Spanish), Colombia, was conducted by applying concepts from information entropy and artificial neural networks (ANNs). This study was divided into two phases: first, a classification of the meteorological stations using two-dimensional self-organizing maps; second, the evaluation of the performance of the ANN by applying concepts of information entropy. Three scenarios were raised for the classification of the meteorological stations by adjusting the number of neurons in the output layer. A high number of neurons in the output layer were obtained, causing the model to over-fit while emphasizing differences amid patterns. When comparing the results of the scenarios, the permanence of certain characteristics and features was found in the system, validating the model classification. Subsequently, the results of the first scenario were used to evaluate the entropy of the historical series. Finally, the results show that the area of study presents a lack of information due to the uncertainty associated with the probabilistic arrangement, which can be corrected with the developed model. Consequently, some recommendations for the redesign of the rainfall are provided.
Highlights
An appropriate rainfall network is fundamental when planning watershed management strategies because it must capture and supply reliable spatial and temporal precipitation data needed for the design, construction, and operation of hydraulic structures such as urban stormwater drainage systems [1]
The combined approach of artificial neural networks (ANNs) and SOM is recommended for the design of rainfall networks where there are large scale requirements and random criteria for station location, which makes the application of conventional methods not appropriate
This map of isohyets will support future estimation of design rainfalls, especially in ungauged areas in Colombia, where the lack of readily available and processed information often becomes an obstacle in the development of hydrological studies [40]; the predictive capabilities of data-driven modeling applied to hydrology are demonstrated [41]
Summary
An appropriate rainfall network is fundamental when planning watershed management strategies because it must capture and supply reliable spatial and temporal precipitation data needed for the design, construction, and operation of hydraulic structures such as urban stormwater drainage systems [1]. The entropy of information has been used in the fields of hydraulics and hydrology for different purposes, such as determining a methodology for gauging of rivers [27], regional analysis of precipitation [28], and evaluating precipitation variability in a given area [29]. The combined approach of ANN and SOM is recommended for the design of rainfall networks where there are large scale requirements and random criteria for station location, which makes the application of conventional methods not appropriate. This approach is reflected in monitoring stations being located in redundant sites, neglecting other areas. Corporation of Cundinamarca (CAR, for its acronym in Spanish) in Colombia, was evaluated using applied concepts of the information entropy and ANNs to provide recommendations for the redesign of the rainfall network in the studied mountainous region
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