Editorial: Assessment of Climate Change Impact on Water Resources Using Machine Learning Algorithms

Journal of Water and Climate Change, Journal Year: 2024, Volume and Issue: 15(6), P. iii - vi

Published: June 1, 2024

Machine learning (ML) algorithms bring about a game changer tool in developing estimation models various fields of research, including water resources and climate change.These techniques can be used for solving problems when assessing change impacts on resources.For instance, they utilized to downscale outputs Global Climate Models (GCMs) investigate effects hydroclimatic variables.Furthermore, ML employed study variations quantity quality under changing climate.Moreover, exploited explore rivers, groundwater, supply systems.Because the importance topic, this special issue intends provide an opportunity collect recent investigations focusing evaluating resources.The scientific peer-reviewed papers contributed are summarized following:• Statistical computation hydrological assessment Understanding how variables over time considering is crucial.Nguyen et al.(2023) evaluated two models, i.e., convolutional neural network (CNN) long short-term memories (LSTM), estimating at 3S River Basin.For impacts, three CMCC-CMS, HadGEM-AO2, MIROC5, scenarios, Representative Concentration Pathways (RCPs) 4.5 8.5, were considered future periods.An increase mean annual temperature fluctuations precipitation detected.Furthermore, ML-based projections yield rise streamflow Srepok Sesan Rivers, reducing trend Sekong, increasing flood risk Sekong basins.Patel & Mehta (2023) conducted statistical analysis Hanumangarh district.They (i) graphical (Innovative Trend Analysis method) (ii) (Mann-Kendall's test Sen's Slope estimator) methods monthly, seasonal, 122 years.Their results indicated southwest monsoon season based method, which was identified as most robust model their study.

Language: Английский

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