Modeling of Groundwater Potential Using Cloud Computing Platform: A Case Study from Nineveh Plain, Northern Iraq

Abstract

Knowledge of the groundwater potential, especially in an arid region, can play a major role in planning the sustainable management of groundwater resources. In this study, nine machine learning (ML) algorithms—namely, Artificial Neural Network (ANN), Decision Jungle (DJ), Averaged Perceptron (AP), Bayes Point Machine (BPM), Decision Forest (DF), Locally-Deep Support Vector Machine (LD-SVM), Boosted Decision Tree (BDT), Logistic Regression (LG), and Support Vector Machine (SVM)—were run on the Microsoft Azure cloud computing platform to model the groundwater potential. We investigated the relationship between 512 operating boreholes with a specified specific capacity and 14 groundwater-influencing occurrence factors. The unconfined aquifer in the Nineveh plain, Mosul Governorate, northern Iraq, was used as a case study. The groundwater-influencing factors used included elevation, slope, curvature, topographic wetness index, stream power index, soil, land use/land cover (LULC), geology, drainage density, aquifer saturated thickness, aquifer hydraulic conductivity, aquifer specific yield, depth to groundwater, distance to faults, and fault density. Analysis of the contribution of these factors in groundwater potential using information gain ratio indicated that aquifer saturated thickness, rainfall, hydraulic conductivity, depth to groundwater, specific yield, and elevation were the most important factors (average merit > 0.1), followed by geology, fault density, drainage density, soil, LULC, and distance to faults (average merit < 0.1). The average merits for the remaining factors were zero, and thus, these factors were removed from the analysis. When the selected ML classifiers were used to estimate groundwater potential in the Azure cloud computing environment, the DJ and BDT models performed the best in terms of all statistical error measures used (accuracy, precision, recall, F-score, and area under the receiver operating characteristics curve), followed by DF and LD-SVM. The probability of groundwater potential from these algorithms was mapped and visualized into five groundwater potential zones: very low, low, moderate, high, and very high, which correspond to the northern (very low to low), southern (moderate), and middle (high to very high) portions of the study area. Using a cloud computing service provides an improved platform for quickly and cheaply running and testing different algorithms for predicting groundwater potential.

Geospatial Assessment of Potential Groundwater Zones Within Igede Ekiti, Ekiti State, Nigeria

“…In order to create a thorough groundwater potential map, the study employs GIS (Geographic Information System) to combine and analyze numerous data layers, including geology, topography, soil, land use, and hydrological factors. Al-Ozeer et al (2021) 811 emphasised that planning the sustainable management of groundwater resources can greatly benefit from knowledge of the groundwater potential, particularly in a dry environment. Nine machine learning (ML) algorithms were used in this study to model the groundwater potential, including the Artificial Neural Network (ANN), Decision Jungle (DJ), Averaged Perceptron (AP), Bayes Point Machine (BPM), Decision Forest (DF), Locally-Deep Support Vector Machine (LD-SVM), Boosted Decision Tree (BDT), Logistic Regression (LG), and Support Vector Machine (SVM).…”

Exploring the Innovative Application of Azure Cloud Computing Platform in Cross-border E-commerce Operation

“…Its flexible and scalable features make it an important tool for Cross-border E-commerce enterprises around the world to support and expand their business operations [6] [7]. The main functions of Azure are mainly focused on big data processing, machine learning, data analysis, and application development [8] [9].…”

“…These tools help enterprises achieve large-scale data integration, storage, and processing. With Azure's powerful computing power and storage capacity, Cross-border E-commerce enterprises can easily process and analyze a large amount of business data to better understand market dynamics and consumer behavior [6] [9].…”

“…Therefore, the Azure cloud computing platform has shown its powerful functions in processing large-scale data and providing machine learning and data analysis tools, especially in Cross-border E-commerce [7] [8]. Its depth and breadth of service capabilities enable enterprises to process and analyze massive data flexibly, thus providing strong support for business operations and decision-making [7] [9]. These advantages of Azure make it an indispensable tool in Crossborder E-commerce operations, providing new possibilities and opportunities for the development of Cross-border Ecommerce [6]…”

Mapping of Groundwater Potential Zone Using GIS and Remote Sensing of Shwan Sub-Basin, Kirkuk, NE Iraq

“…The protection of water resources in Iraq is important, so the government began to monitor the groundwater for its proper management. Hydrogeological maps are an important tool in managing water resources (Al-Ozeer, 2021). Groundwater potential zones depend on geological, land use, and hydrological surveys.…”