A Hybrid Clustering-Fusion Methodology for Land Subsidence Estimation

A hybrid clustering-fusion methodology is developed in this study that employs Genetic Algorithm (GA) optimization method, k-means method, and several soft computing (SC) models to better estimate land subsidence. Estimation of land subsidence is important in planning and management of groundwater resources to prevent associated catastrophic damages. Methods such as the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) can be used to estimate the subsidence rate, but PS-InSAR does not offer the required efficiency and accuracy in noisy pixels (obtained from remote sensing). Alternatively, a fusion-based methodology can be used to estimate subsidence rate, which offers a superior accuracy as opposed to the traditionally used methods. In the proposed methodology, five SC methods are employed with hydrogeological forcing of frequency and thickness of fine-grained sediments, groundwater depth, water level decline, transmissivity and storage coefficient, and output of land subsidence rate. Results of individual SC models are then fused to render more accurate land subsidence rate in noisy pixels, for which PS-InSAR cannot be effective. We first extract 14,392 different input-output patterns from PS-InSAR technique for our study area in Tehran province, Iran. Then, k-means method is used to divide the study area to homogenous zones with similar features. The five SC models include Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Regression (SVR), Multi-Layer Perceptron (MLP) neural network and two optimized models, namely, Radial Basis Function (RBF) and Generalized Regression Neural Network (GRNN). To fuse individual SC models, three methods including Genetic Algorithm (GA), K-Nearest Neighbors (KNN) and Ordered Weighted Average (OWA) based on ORNESS method and ORLIKE method, are developed and evaluated. Results show that the fusion-based method is significantly superior to each of the employed individual methods in predicting land subsidence rate

Water Quality Assessment of Garmarood River Using the National Sanitation Foundation Water Quality Index (NSFWQI), River Pollution Index (RPI) and Weighted Arithmetic Water Quality Index (WAWQI)

Rivers are recognized as the most important water supply resources in various sectors, including agriculture water, drinking water, and industrial water. Over recent years, however, urban, industrial, and agricultural sewage have mostly been discharged into rivers. Taking into account that rivers have a limited capacity for toleration of pollutants, river water quality assessment is indispensable. In the present study, three water quality indicators namely the National Sanitation Foundation Water Quality Index (NSFWQI), River Pollution Index (RPI), and Weighted Arithmetic Water Quality Index (WAWQI) were used to assess the quality of Garmarood River water.  Sampling was performed at 3 stations along the river during summer and winter of 2019. A variety of parameters namely DO, temperature, BOD, fecal coliform, turbidity, TSS, pH, NH3-N, and phosphate were measured to calculate NSFWQI and RPI indices. The parameters used to measure WAWQI, included TS, NO3-, chloride, total hardness, SO42-, Mg, turbidity, pH, and Ca. The results obtained from analysis of the above-mentioned parameters showed that the value of NSFWQI, RPI, and WAWQI indices fall within the 50.66-75.6, 2.25-5.5 and 48.33-55.92 ranges. The results obtained from all 3 indices are indicative of relatively high quality of water at station 1 and poor quality of water at stations 2 and 3

Investigation of Quality and Reclamation of Urban Storm Runoff in City of Shiraz

Urban storm runoff is considered as a potentially reclaimable and valuable resource in many arid and semiarid areas, in Iran. Urban storm runoff in Shiraz is collected mainly by Khoshk River and transported to the Maharloo Lake without any treatment or reclamation. In this study, storm runoff quality and the possibility for its reclamation from different parts of the city in certain canals and pipes are investigated. The quality of the first flush in three relatively large and small suburban areas with different land uses is studied. For the purposes of this study, three stations were considered: one near the downstream end of the city on Khoshk River with a relatively large watershed, one in the middle of the city where street runoff is the main constituent of the flush, and a third one near the western outskirts of the city with relatively small mainly residential watershed

Multi-Objective Conflict Resolution Optimization Model for Reservoir’s Selective Depth Water Withdrawal Considering Water Quality

This paper develops a multi-objective conflict resolution simulation-optimization model based on a leader-follower game to resolve conflicts between different water users while optimizing water quality in the river through selective depth water withdrawal from the reservoir. Iran Water Resources Management Company (IWRMC), given the nature of the power distribution in this region, is selected as leader, and agricultural, domestic, and industrial water users are selected as followers. Nash-Harsanyi bargaining theory is used as a nested model in this general framework to model competition between followers. The proposed selective withdrawal approach considers four reservoir outlets, located at 120, 145, 163, and 181 m above sea level. Water withdrawal from multiple outlets addresses reservoir thermal stratification and water quality. Temperature and water quality are simulated based on different possible scenarios of reservoir inflow and release using a calibrated CE-QUAL-W2 model. Five artificial neural network (ANN) surrogate/meta models are then trained and validated based on CE-QUAL-W2 model results for each water quality variable. Subsequently, these validated surrogate models are coupled with the NSGA-II optimization model, which along with the utility functions of different stakeholders, constitute the building blocks of our conflict resolution multi-objective optimization model. Finally, three decision-making methods, namely AHP, PROMETHEE, and TOPSIS, are utilized to choose the superior compromise solution. Our results show that water withdrawal from multiple reservoir outlets ensures optimal water allocation to different stakeholders while satisfying the desired water quality criteria. In this study, the top outlet (181 m) has desirable quality, and the IRWQISC water quality criterion at the top and deepest outlets are highest and lowest, respectively

Uncertainty Analysis of Hydrological Drought Due to Record Length, Time Scale, and Probability Distribution Functions Using Monte Carlo Simulation Method

Standardized Runoff Index (SRI), as one of the well-known hydrological drought indices, may contain uncertainties caused by the employment of the distribution function, time scale, and record length of statistical data. In this study, the uncertainty in the SRI estimation of monthly discharge data of 30- and 49-year lengths from the Minab Dam watershed, south of Iran, was investigated. Four probability distribution functions (Gamma, Weibull, Lognormal, and Normal) were used to fit the cumulative discharge data at 3-, 6-. 9-, 12-, 24-, and 48-month time scales, with their goodness-of-fit and normality evaluated by K-S and normality tests, respectively. Using Monte Carlo sampling, 50,000 statistical data were generated for each event and each time scale, followed by 95% confidence interval. The width of the confidence interval was used as uncertainty, and sources of uncertainty were investigated using miscellaneous factors. It was found that the maximum uncertainty was related to Normal and Lognormal distributions and the minimum uncertainty to Gamma and Weibull distributions. Furthermore, the increase in both time scale and record length led to the decrease in uncertainty