Incorporating local researchers' and decision makers' preferences for groundwater resources management in a spatial multi-voiced decision model

A machine learning framework for spatio-temporal vulnerability mapping of groundwaters to nitrate in a data scarce region in Lenjanat Plain, Iran

The temporal aspect of groundwater vulnerability to contaminants such as nitrate is often overlooked, assuming vulnerability has a static nature. This study bridges this gap by employing machine learning with Detecting Breakpoints and Estimating Segments in Trend (DBEST) algorithm to reveal the underlying relationship between nitrate, water table, vegetation cover, and precipitation time series, that are related to agricultural activities and groundwater demand in a semi-arid region. The contamination probability of Lenjanat Plain has been mapped by comparing random forest (RF), support vector machine (SVM), and K-nearest-neighbors (KNN) models, fed with 32 input variables (dem-derived factors, physiography, distance and density maps, time series data). Also, imbalanced learning and feature selection techniques were investigated as supplementary methods, adding up to four scenarios. Results showed that the RF model, integrated with forward sequential feature selection (SFS) and SMOTE-Tomek resampling method, outperformed the other models (F1-score: 0.94, MCC: 0.83). The SFS techniques outperformed other feature selection methods in enhancing the accuracy of the models with the cost of computational expenses, and the cost-sensitive function proved more efficient in tackling imbalanced data issues than the other investigated methods. The DBEST method identified significant breakpoints within each time series dataset, revealing a clear association between agricultural practices along the Zayandehrood River and substantial nitrate contamination within the Lenjanat region. Additionally, the groundwater vulnerability maps created using the candid RF model and an ensemble of the best RF, SVM, and KNN models predicted mid to high levels of vulnerability in the central parts and the downhills in the southwest.

Analysis of sustainable water resource management and driving mechanism in arid region: a case study of Xinjiang, China, from 2005 to 2020

The long-term dynamic comprehensive evaluation of the water resource carrying capacity (WRCC) and the analysis of its potential driving mechanism in arid areas are contemporary research issues and technical means of mitigating and coordinating the conflict between severe resource shortages and human needs. The purpose of this study was to explore the distribution of the WRCC and the spatiotemporal heterogeneity of drivers in arid areas based on an improved two-dimensional spatiotemporal dynamic evaluation model. The results show that (1) the spatial distribution of the WRCC in Xinjiang, China, is high in the north, low in the south, high in the west, and low in the east. (2) From 2005 to 2020, the centers of gravity of the WRCC in northern and southern Xinjiang moved to the southeast and west, respectively, and the spatial distribution exhibited slight diffusion. (3) The factors influencing the WRCC exhibit more obvious spatial and temporal heterogeneity. The domestic waste disposal rate and ecological water use rate were the main factors influencing the WRCC in the early stage, while the GDP per capita gradually played a dominant role in the later stage. (4) In the next 30 years, the WRCC in Xinjiang will increase. The results provide a theoretical reference for the sustainable development of water resources in arid areas.

Evaluating the capability of municipal solid waste separation in China based on AHP-EWM and BP neural network

With the increase in municipal solid waste (MSW), most cities face solid waste management issues. In this study, the analytic hierarchy process (AHP) and artificial neural network (ANN) models were improved to assess the MSW separation capability based on 18 selected indicators of solid waste separation in 15 cities in China. The entropy weight method (EWM) was used in AHP to optimize and determine the indicators and then evaluate their weights, which showed that the general public budget expenditure had the highest weight (0.5239). This implied that the MSW separation capability could be mainly influenced by government financial support. ANN based on scan optimization and machine learning methods were established (R² = 0.9992) to predict the missing indicators. The mapping relationship between MSW separation indicators and capabilities was also significantly improved from R² = 0.5317 to R² = 0.9993, thereby increasing the prediction accuracy of MSW separation capabilities to 95.15%. Thus, this research provides a new avenue for MSW separation and establishes a combined model to predict the separation capability in practical applications.