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2 Acta Geophysica

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Snow water equivalent prediction in a mountainous area using hybrid bagging machine learning approaches

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Khosravi K. , Golkarian A. , Omidvar E. , Hatamiafkoueieh J. , Shirali M.

Acta Geophysica

2023

tom Vol. 71, no. 2

1015--1031

Snow Water Equivalent (SWE) is one of the most critical variables in mountainous watersheds and needs to be considered in water resources management plans. As direct measurement of SWE is difficult and empirical equations are highly uncertain, the present study aimed to obtain accurate predictions of SWE using machine learning methods. Five standalone algorithms of tree-based [M5P and random tree (RT)], rule-based [M5Rules (M5R)] and lazy-based learner (IBK and Kstar) and five novel hybrid bagging-based algorithms (BA) with standalone models (i.e., BA-M5P, BA-RT, BA-IBK, BA-Kstar and BA-M5R) were developed. A total of 2550 snow measurements were collected from 62 snow and rain-gauge stations located in 13 mountainous provinces in Iran. Data including ice beneath the snow (IBS), fresh snow depth (FSD), length of snow sample (LSS), snow density (SDN), snow depth (SD) and time of falling (TS) were measured. Based on the Pearson correlation between inputs (IBS, FSD, LSS, SDN, SD and TS) and output (SWE), six different input combinations were constructed. The dataset was separated into two groups (70% and 30% of the data) by a cross-validation technique for model construction (training dataset) and model evaluation (testing dataset), respectively. Different visual and quantitative metrics (e.g., Nash–Sutclife efficiency (NSE)) were used for evaluating model accuracy. It was found that SD had the highest correlation with SWE in Iran (r=0.73). In general, the bootstrap aggregation (i.e., bagging) hybrid machine learning methods (BA-M5P, BA-RT, BA-IBK, BA-Kstar and BA-M5R) increased prediction accuracy when compared to each standalone method. While BA-M5R had the highest prediction accuracy (NSE=0.83) (considering all six input variables), BA-IBK could predict SWE with high accuracy (NSE=0.71) using only two input variables (SD and LSS). Our findings demonstrate that SWE can be accurately predicted through a variety of machine learning methods using easily measurable variables and may be useful for applications in other mountainous regions across the globe.

Groundwater spring potential prediction using a deep-learning algorithm

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Moughani S. K. , Osmani A. , Nohani E. , Khoshtinat S. , Jalilian T. , Askari Z. , Heddam S. , Tiefenbacher J. P. , Hatamiafkoueieh J.

Acta Geophysica

2024

tom Vol. 72, no. 2

1033--1054

Information about water resources is crucial for sustainable development, and this issue is considered to be one of the most important concerns worldwide due to rapid industrialization and population growth. Countries in the semiarid region of the western Asia, like Iran, are dependent on groundwater resources so access to these resources is vital. This study maps surface spring potential on the Nourabad-Koohdasht Plain of Iran using a deep-learning algorithm called convolutional neural network (CNN), and the result was compared to predictions made with five advanced data-mining models: logistic model tree (LMT), LMT hybridized with bagging (BA-LMT), LMT hybridized with dagging (DA-LMT), LMT hybridized with random subspace (RS-LMT), and LMT hybridized with AdaBoost (AB-LMT). Frequency ratio was used to assess the strengths of relationships of each subclass layer to groundwater presence and evidential belief function revealed their effects on model uncertainty. The locations of 2463 springs were determined and showed that the northern part of the plain has the highest groundwater potential based on the density of springs. The data representing each of the spring locations were used for prediction modeling. Receiver operating characteristic (ROC) and area under the ROC curve (AUC) were used to evaluate the strengths of the predictions produced by the models. The results show that CNN (AUC = 0.885) provided the best prediction of spring locations. AB-LMT (AUC = 0.877) was second best, and BA-LMT (AUC = 0.876), DA-LMT (AUC = 0.856), RS-LMT (AUC = 0.846), and the standalone LMT model (AUC = 0.827) followed in rank. It can be concluded that the hybrid LMT models increased the predictive strength of the standalone LMT model when used to predict spring locations. These hybrid modeling methods may be used to improve sustainable groundwater management in the study region and in other regions as well.