Spatial prediction of spring locations in data poor region of Central Himalayas

Abstract

This research explores the methods for understanding groundwater springs distribution and occurrence using Geographic Information System (GIS) and Machine Learning technique in data poor areas of the Central Himalayas. The objectives of this study are to analyse the distribution of natural springs, evaluate three random forest models for its predictability and establish a model for the prediction of occurrence of springs. This study evaluates the primary causal factors for occurrence of springs. The data used in this study consists of 20 parameters based on topography, geology, lithology, hydrology and land use as causal factors, whereas 621 spring location and discharge (n = 621) measured during 2014–2016 and 815 non-spring locations (generated by GIS tool) use as supporting evidence to train (80%) and test (20%) the prediction model. Results show that the Bootstrap method is comparatively reliable (92% accuracy) over Boosted tree (64% accuracy) and Decision tree (74% accuracy) methods to classify and predict the occurrence of springs in the watershed. Bootstrap Forest shows the high Prediction rate for True Positive (82% actual spring predicted as a spring) and True Negative (89% actual non-spring predicted as non-spring), and the model seems consistent in both responses. This model was then applied to an independent dataset to predict spring location estimates with 75% accuracy. Therefore, spatial statistical methods prove efficient at predicting spring occurrence in data poor regions.

Analysis and Design of Natural Spring Water Preservation and Monitoring System Using Rapid Application Development

“…The practical implications of this research demonstrate the contribution of developing a GIS-based database and information system, which all stakeholders can utilize in monitoring natural spring waters and designing environmental preservation programs near natural spring waters [26]. By integrating geographic information systems (GIS) into the database and information system, this study offers a powerful tool for spatial analysis, visualization, and decision-making, enhancing the effectiveness and efficiency of conservation efforts [27].…”

Distribution and Characteristics of Springs in Two Neighboring Areas of Different Morphogenic Relief Type—Example of SW Medvednica Mountain (Central Croatia)

“…Previous research has shown that the occurrence of springs is the result of many spatial factors [2,[6][7][8][9][10], which is why it is difficult to predict their exact locations. Despite technological progress and the use of remote sensing in spring research [11][12][13][14], it is still relatively difficult to determine the exact locations of springs without proper fieldwork, especially in the case of relatively small springs, considering their size and discharge. For this reason, studies of mountain springs often require extensive fieldwork, especially for their mapping [2,8].…”

Identifying potential recharge areas of mountain springs through hydrogeological mapping

“…Almost 80% of the people residing in these areas use springs as their primary source of water (Sharma et al, 2016). It is often used for livestock feeding, irrigation for agricultural field etc (Chapagain et al, 2019;Niraula et al, 2020).While springs play vital role in maintaining water-flow, water balance in lakes & ponds, it equally contributes in the downstream water availability (Rosegrant et al, 2009). Besides, these are important resources to maintain land productivity, ecosystem health, and wetland biodiversity.…”