GIS-based multi-criteria decision making for identifying rainwater harvesting sites

Applied Water Science, Journal Year: 2025, Volume and Issue: 15(3)

Published: Feb. 6, 2025

Language: Английский

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Optimal rainwater harvesting locations for arid and semi-arid regions by using MCDM-based GIS techniques

Heliyon, Journal Year: 2025, Volume and Issue: 11(3), P. e42090 - e42090

Published: Jan. 23, 2025

Rainwater collection and effective water resource management are essential for boosting availability, land productivity, groundwater levels in dry places like Iraq, which is susceptible to climate change drought. This work develops a GIS-based rainfall harvesting (RWH) method the western Karbala Governorate, address shortages future replenishment irrigation demands. LARS-WG 8 was used study how affects assess whether rainwater feasible sustainable. The research found that annual governorate would grow by 18%-24 % 21st century, highlighting necessity of sustainability. Themed RWH layers were created using ArcGIS software multi-criteria decision-making technique. Analytic Hierarchy Process determined tier weights based on seven factors. Based literature, local experts, statistics, rainfall, curve number, slope, stream order, soil texture, use, runoff depth considered. consistency ratio 2.6 validated comparison component showed each criterion appropriately weighted. most (47 total) depth. map classified areas as high, medium, or low appropriateness. Results indicated three groups uniformly distributed. results appeared; area lands have 34.4 (745 km2) medium suitability, 34.2 (752 31.8 (697 high largely central sections. Sensitivity analysis applied find sensitive characteristics, establish criteria ideal locations, ensure focuses right elements. this novel help policymakers develop allocation policies, promoting an alternative supply West other water-scarce locations.

Language: Английский

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Estimation of return dates and return levels of extreme rainfall in the city of Douala, Cameroon

Heliyon, Journal Year: 2024, Volume and Issue: 10(14), P. e34832 - e34832

Published: July 1, 2024

The problem of extreme phenomena with a more precise estimation their return periods for early warnings, notably to preserve the safety populations and properties, arises all over world. This work develops another aspect in Return Levels (RLs) Periods (RPs) precipitation particular natural risk general. In particular, it gives Dates (RDs) Confidence Intervals (CIs). RPs, RLs CIs rainfall were also investigated. These estimates made by approaching peak threshold chosen Generalized Pareto Distribution (GPD). RPs determined Delta method. daily data used obtained from synoptic report period 2011 2021 Douala weather station (more details can be found on http://www.ogimet.com/guia.phtml.en). To validate methods used, real cases floods occurred city considered: example, local press compiled flood dates mentioned that April 16, 2013 this city. Following report, corresponding amount was around 150 mm. results have shown RD August 12, 2014. confidence intervals levels method [131.66, 168.456] [June 23, 2014, January 02, 2015], respectively. are agreement since amounts 132.2 mm (belonging interval levels), 11, 2014 dates). predictions RDs CIs, at reasonable time scales, help efficient management thus, improve warnings goods.

Language: Английский

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Comparative Evaluation of Water Level Forecasting Using IoT Sensor Data: Hydrodynamic Model SWMM vs. Machine Learning Models Based on NARX Framework

Water, Journal Year: 2024, Volume and Issue: 16(19), P. 2776 - 2776

Published: Sept. 29, 2024

This study evaluates the accuracy of water level forecasting using two approaches: hydrodynamic model SWMM and machine learning (ML) models based on Nonlinear Autoregressive with Exogenous Inputs (NARX) framework. offers a physically modeling approach, while NARX is data-driven method. Both use real-time precipitation data, their predictions compared against measurements from network IoT sensors in stormwater management system. The results demonstrate that both provide effective forecasts, exhibit higher accuracy, improved Nash–Sutcliffe Efficiency (NSE) coefficients 33–37% lower mean absolute error (MAE) to SWMM. Despite these advantages, may struggle limited data extreme flooding events, where they could face challenges. Enhancements calibration reduce performance gap, but development requires substantial expertise resources. In contrast, are generally more resource-efficient. Future research should focus integrating approaches by leveraging simulations generate synthetic particularly for weather enhance robustness other ML real-world flood prediction scenarios.

Language: Английский

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Review of Green Water Systems for Urban Flood Resilience: Literature and Codes

Water, Journal Year: 2024, Volume and Issue: 16(20), P. 2908 - 2908

Published: Oct. 13, 2024

Achieving Urban Flood Resilience (UFR) is essential for modern societies, requiring the implementation of effective practices in different countries to mitigate hydrological events. Green Water Systems (GWSs) emerge as a promising alternative achieve UFR, but they are still poorly explored and present varied definitions. This article aims define GWSs within framework sustainable propose regulation that promotes UFR. Through systematic review existing definitions an analysis international regulations on urban drainage systems (SuDSs), this study uncovers perceptions applications their role Blue–Green Infrastructure (BGI). Furthermore, research puts forth standardized definition emphasizes SuDSs Peru. approach address knowledge gap contribute advancement infrastructure.

Language: Английский

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