Water Resources Management Through Flood Spreading Project Suitability Mapping Using Frequency Ratio, k-nearest Neighbours, and Random Forest Algorithms
Naghibi, Seyed Amir ; Vafakhah, Mehdi ; Hashemi, Hossein LU
; Pradhan, Biswajeet
and Alavi, Seyed Jalil
(2020)
In Natural Resources Research
29(3).
p.1915-1933
- Abstract
Lack of water resources is a common issue in many countries, especially in the Middle East. Flood spreading project (FSP) is an artificial recharge technique, which is generally suggested for arid and semi-arid areas with two major aims including (1) flood mitigation and (2) artificial recharge of groundwater. This study implemented three state-of-the-art popular models including frequency ratio (FR), k-nearest neighbours (KNN), and random forest (RF) for determining the suitability of land for FSP. At the first step, suitable areas for FSP were identified according to the national guidelines and the literature. The identified areas were then verified by multiple field surveys. To produce FSP land suitability maps, several FSP... (More)
Lack of water resources is a common issue in many countries, especially in the Middle East. Flood spreading project (FSP) is an artificial recharge technique, which is generally suggested for arid and semi-arid areas with two major aims including (1) flood mitigation and (2) artificial recharge of groundwater. This study implemented three state-of-the-art popular models including frequency ratio (FR), k-nearest neighbours (KNN), and random forest (RF) for determining the suitability of land for FSP. At the first step, suitable areas for FSP were identified according to the national guidelines and the literature. The identified areas were then verified by multiple field surveys. To produce FSP land suitability maps, several FSP conditioning factors such as topographical (i.e. slope, plan curvature, and profile curvature), hydrogeological (i.e. transmissivity, aquifer thickness, and electrical conductivity), hydrological (i.e. rainfall, distance from rivers, river density, and permeability), lithology, and land use were considered as input to the models. For the FR modelling, classified layers of the aforementioned variables were used, while their continuous layers were implemented in the KNN and RF algorithms. At the last step, receiver operating characteristic (ROC) curve was used to assess the ability and accuracy of the applied algorithms. Based on the findings, the area under the curve of ROC for the RF, KNN, and FR models was 97.1, 94.6, and 89.2%, respectively. Furthermore, transmissivity, slope, aquifer thickness, distance from rivers, rainfall, and electrical conductivity were recognized as the most influencing factors in the modelling procedure. The findings of this study indicated that the application of RF, KNN, and FR can be suggested for identification of suitable areas for FSP establishment in other regions.
(Less)
- author
- Naghibi, Seyed Amir
; Vafakhah, Mehdi
; Hashemi, Hossein
LU
; Pradhan, Biswajeet
and Alavi, Seyed Jalil
- organization
- publishing date
- 2020-06
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Artificial recharge, Data mining, Flood spreading project, Hydrogeology, Random forest
- in
- Natural Resources Research
- volume
- 29
- issue
- 3
- pages
- 19 pages
- publisher
- Springer
- external identifiers
-
- scopus:85070257242
- ISSN
- 1520-7439
- DOI
- 10.1007/s11053-019-09530-4
- language
- English
- LU publication?
- yes
- id
- d2b0ee19-a2a8-47d2-b0cc-ee3c0dec989e
- date added to LUP
- 2019-08-27 10:46:11
- date last changed
- 2023-10-07 14:50:43
@article{d2b0ee19-a2a8-47d2-b0cc-ee3c0dec989e,
abstract = {{<p>Lack of water resources is a common issue in many countries, especially in the Middle East. Flood spreading project (FSP) is an artificial recharge technique, which is generally suggested for arid and semi-arid areas with two major aims including (1) flood mitigation and (2) artificial recharge of groundwater. This study implemented three state-of-the-art popular models including frequency ratio (FR), k-nearest neighbours (KNN), and random forest (RF) for determining the suitability of land for FSP. At the first step, suitable areas for FSP were identified according to the national guidelines and the literature. The identified areas were then verified by multiple field surveys. To produce FSP land suitability maps, several FSP conditioning factors such as topographical (i.e. slope, plan curvature, and profile curvature), hydrogeological (i.e. transmissivity, aquifer thickness, and electrical conductivity), hydrological (i.e. rainfall, distance from rivers, river density, and permeability), lithology, and land use were considered as input to the models. For the FR modelling, classified layers of the aforementioned variables were used, while their continuous layers were implemented in the KNN and RF algorithms. At the last step, receiver operating characteristic (ROC) curve was used to assess the ability and accuracy of the applied algorithms. Based on the findings, the area under the curve of ROC for the RF, KNN, and FR models was 97.1, 94.6, and 89.2%, respectively. Furthermore, transmissivity, slope, aquifer thickness, distance from rivers, rainfall, and electrical conductivity were recognized as the most influencing factors in the modelling procedure. The findings of this study indicated that the application of RF, KNN, and FR can be suggested for identification of suitable areas for FSP establishment in other regions.</p>}},
author = {{Naghibi, Seyed Amir and Vafakhah, Mehdi and Hashemi, Hossein and Pradhan, Biswajeet and Alavi, Seyed Jalil}},
issn = {{1520-7439}},
keywords = {{Artificial recharge; Data mining; Flood spreading project; Hydrogeology; Random forest}},
language = {{eng}},
number = {{3}},
pages = {{1915--1933}},
publisher = {{Springer}},
series = {{Natural Resources Research}},
title = {{Water Resources Management Through Flood Spreading Project Suitability Mapping Using Frequency Ratio, k-nearest Neighbours, and Random Forest Algorithms}},
url = {{http://dx.doi.org/10.1007/s11053-019-09530-4}},
doi = {{10.1007/s11053-019-09530-4}},
volume = {{29}},
year = {{2020}},
}