Site Selection Optimisation Using Fuzzy-GIS Integration for Wastewater Treatment Plant DOI Creative Commons
Tasneem I. M. Abdelmagid, Isam Mohammed Abdel-Magid,

Eltayeb H. Onsa Elsadig

et al.

Limnological Review, Journal Year: 2024, Volume and Issue: 24(3), P. 354 - 373

Published: Sept. 6, 2024

Municipal management involves making decisions on various technical issues, and one such crucial aspect is the multicriteria decision-making process. When choosing suitable locations for wastewater treatment plants, it becomes necessary to consider a range of factors as feasibility, economic viability, environmental impact, ecological aspects, requirements. However, evaluating these criteria dealing with uncertainties can be complex. To address this challenge in Tabuk region, combination two powerful analytical methods, fuzzy hierarchy process (FAHP) geographical information system (GIS), were employed. The FAHP methodology allows considering subjective judgements, while GIS provides spatial analysis capabilities. By combining GIS, thorough evaluation potential plant was conducted by determining relative weights each geospatial parameter. These then used generate suitability map, visually representing most favourable areas site selection. resulted higher importance given plant’s distance urban areas, followed roads among seven investigated parameters. integrated FAHP-GIS model results show that western parts region are constructing plants. findings valuable facilitating identifying optimum area. In summary, integrating assessment enables decision-makers technical, economic, environmental, ecological, thereby providing comprehensive framework selection replicated other regions different conditions. This approach enhances municipal promotes more informed effective planning region.

Language: Английский

FLOOD HAZARD ZONES PREDICTION USING MACHINE-LEARNING-BASED GEOSPATIAL APPROACH IN LOWER NIGER RIVER BASIN, NIGERIA DOI Creative Commons

Adedoyin Benson Adeyemi,

Akinola Adesuji Komolafe

Natural Hazards Research, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

1
Rainfall–runoff modeling using an Adaptive Neuro-Fuzzy Inference System considering soil moisture for the Damanganga basin DOI Creative Commons

Vrushti C. Kantharia,

Darshan Mehta, Vijendra Kumar

et al.

Journal of Water and Climate Change, Journal Year: 2024, Volume and Issue: 15(5), P. 2518 - 2531

Published: April 6, 2024

ABSTRACT Rainfall is the major component of hydrologic cycle and it primary source runoff. The main purpose this study was to estimate daily discharge by employing an Adaptive Neuro-Fuzzy Inference System (ANFIS) model using rainfall soil moisture data at three different depths (5 cm, 100 cm bedrock) for Damanganga basin. length period 1983–2022 39 years. employed nine membership functions each variable moisture, rainfall, 30 rules were optimized. results compared considering a range performance indicators as correlation coefficient (R2) Nash–Sutcliffe efficiency (NSE) coefficient. application shows that bedrock gives more precise value with NSE 0.9936 0.9981, respectively, 5 cm. better obtained measurement in deeper layer are consistent hydrological behavior anticipated analyzed catchment, where root-zone driver runoff response rather than surface observations. This can be helpful hydrologists selecting appropriate rainfall–runoff models.

Language: Английский

Citations

6
A data-driven approach to river discharge forecasting in the Himalayan region: Insights from Aglar and Paligaad rivers DOI Creative Commons
Vikram Kumar, Selim Unal, Suraj Kumar Bhagat

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 22, P. 102044 - 102044

Published: March 26, 2024

This study aims to better understand the time series forecasting of Aglar and Paligaad rivers' discharge (which has a significant impact on Himalayan river) using advanced methods such as Holt-Winters (HW) additive method, Simple exponential smoothing (SES), Non-seasonal ARIMA models. used antecedent information forecast next event. Comprehensive statistical examinations were conducted analyzed. The highly stochastic nature these river trends adds complexity efforts requires sophisticated modeling techniques that are capable capturing interpreting variability accurately. models proposed in current provide reliable for 15 months 31 recorded data. analysis shows both HW non-seasonal model results indicate decay end 2016 early 2017. best performance long-term forecasting, indicating sharp increase spring small during fall months. However, short-term non-ARIMA should show more promising results. methodologies substantially improve accuracy all consecutive perennial rivers. While presents discharge, generalizing findings other systems or different geographical regions may be problematic due varying hydrological characteristics environmental conditions, which need further study.

Language: Английский

Citations

6
Understanding Climate Change Impacts on Streamflow by Using Machine Learning: Case Study of Godavari Basin DOI Open Access
Ravi Ande,

Chandrashekar Pandugula,

Darshan Mehta

et al.

Water, Journal Year: 2025, Volume and Issue: 17(8), P. 1171 - 1171

Published: April 14, 2025

The study aims to assess future streamflow forecasts in the Godavari basin of India under climate change scenarios. primary objective Coupled Model Inter-comparison Project Phase 6 (CMIP6) was evaluate across different catchments basin, India, with an emphasis on understanding impacts change. This employed both conceptual and machine learning models how changing precipitation patterns temperature variations influence dynamics. Seven satellite products CMORPH, Princeton Global Forcing (PGF), Tropical Rainfall Measuring Mission (TRMM), Climate Prediction Centre (CPC), Infrared Precipitation Stations (CHIRPS), Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN-CDR) were evaluated a gridded evaluation over River basin. Results Multi-Source Weighted-Ensemble (MSWEP) had Nash–Sutcliffe efficiency (NSE), coefficient determination (R2), root mean square error (RMSE) 0.806, 0.831, 56.734 mm/mon, whereas 0.768, 0.846, 57.413 mm, respectively. MSWEP highest accuracy, lowest false alarm ratio, Peirce’s skill score (0.844, 0.571, 0.462). Correlation pairwise correlation attribution approaches used input parameters, which included two-day lag streamflow, maximum minimum temperatures, several datasets (IMD, EC-Earth3, EC-Earth3-Veg, MIROC6, MRI-ESM2-0, GFDL-ESM4). CMIP6 that been adjusted for bias modeling process. R, NSE, RMSE, R2 assessed model’s effectiveness. RF M5P performed well when using as input. demonstrated adequate performance testing (0.4 < NSE 0.50 0.5 0.6) extremely good training (0.75 1 0.7 R 1). Likewise, 0.6). While best performer datasets, Indian Meteorological Department outperformed all modeling. precipitation, RF’s R2, RMSE values during 0.95, 0.979, 0.937, 30.805 m3/s. test results 0.681, 0.91, 0.828, 41.237 Additionally, Multi-Layer Perceptron (MLP) model consistent assessment phases, reinforcing reliability climate-informed hydrological forecasting. underscores significance incorporating projections into enhance water resource management adaptation strategies similar regions facing climate-induced shifts.

Language: Английский

Citations

0
Improving monthly precipitation prediction accuracy using machine learning models: a multi-view stacking learning technique DOI Creative Commons

Mounia El Hafyani,

Khalid El Himdi, Salah‐Eddine El Adlouni

et al.

Frontiers in Water, Journal Year: 2024, Volume and Issue: 6

Published: May 22, 2024

This research paper explores the implementation of machine learning (ML) techniques in weather and climate forecasting, with a specific focus on predicting monthly precipitation. The study analyzes efficacy six multivariate models: Decision Tree, Random Forest, K-Nearest Neighbors (KNN), AdaBoost, XGBoost, Long Short-Term Memory (LSTM). Multivariate time series models incorporating lagged meteorological variables were employed to capture dynamics rainfall Rabat, Morocco, from 1993 2018. evaluated based various metrics, including root mean square error (RMSE), absolute (MAE), coefficient determination (R2). XGBoost showed highest performance among individual models, an RMSE 40.8 (mm). In contrast, LSTM, KNN relatively lower performances, RMSEs ranging 47.5 (mm) 51 A novel multi-view stacking approach is introduced, offering new perspective ML strategies. integrated algorithm designed leverage strengths each model, aiming substantially improve precision precipitation forecasts. best results achieved by combining KNN, LSTM build meta-base while using as second-level learner. yielded 17.5 millimeters. show potential proposed refine predictive accuracy forecasts, setting benchmark for future this field.

Language: Английский

Citations

3
Agent-Based Risk Analysis Model for Road Transportation of Dangerous Goods DOI Creative Commons
Hassan Kanj,

Ajla Kulaglic,

Wael Hosny Fouad Aly

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 103944 - 103944

Published: Jan. 1, 2025

Language: Английский

Citations

0
Novel Pythagorean Fuzzy Score Function to Optimize Fuzzy Transportation Models DOI Creative Commons

Ritu Ritu,

Tarun Kumar,

Jahnvi

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104048 - 104048

Published: Jan. 1, 2025

Citations

0
Multi-Hazards and Existing Data: A Transboundary Assessment for Climate Planning DOI Creative Commons
Alessandra Longo, Chiara Semenzin,

Linda Zardo

et al.

Land, Journal Year: 2025, Volume and Issue: 14(3), P. 548 - 548

Published: March 5, 2025

Many regions worldwide are exposed to multiple omnipresent hazards occurring in complex interactions. However, multi-hazard assessments not yet fully integrated into current planning tools, particularly when referring transboundary areas. This work aims enable spatial planners include their climate change adaptation measures using available data. We focus on a set of (e.g., extreme heat, drought, landslide) and propose four-step methodology (i) harmonise existing data from different databases scales for assessment mapping (ii) read identified bundles homogeneous territorial The methodology, whose outputs replicable other EU contexts, is applied the illustrative case Northeast Italy. results show significant difference between with ‘dichotomous’ behaviour (shocks) those more nuanced one (stresses). harmonised maps single represent new piece knowledge our territory since, date, there no comparable this level definition understand hazards’ distribution interactions study does present some limitations, including putting together remarkable hazards.

Language: Английский

Citations

0
A framework to assess and report social, environmental, and economic post-disaster damages based on Z-numbers and the Delphi method DOI
Mahdi Anbari Moghadam, Morteza Bagherpour

Natural Hazards, Journal Year: 2024, Volume and Issue: unknown

Published: July 18, 2024

Language: Английский

Citations

2
Multi-hazard susceptibility mapping in the Salt Lake watershed DOI Creative Commons

Sima Pourhashemi,

Mohammad Ali Zangane Asadi,

Mahdi Boroughani

et al.

Environmental Challenges, Journal Year: 2024, Volume and Issue: unknown, P. 101079 - 101079

Published: Dec. 1, 2024

Language: Английский

Citations

2