Applications of Machine Learning and Remote Sensing in Soil and Water Conservation DOI Creative Commons
Kwang Jin Kim,

Woo Hyeon Park,

Yongchul Shin

и другие.

Hydrology, Год журнала: 2024, Номер 11(11), С. 183 - 183

Опубликована: Окт. 30, 2024

The application of machine learning (ML) and remote sensing (RS) in soil water conservation has become a powerful tool. As analytical tools continue to advance, the variety ML algorithms RS sources expanded, providing opportunities for more sophisticated analyses. At same time, researchers are required select appropriate technologies based on research objectives, topic, scope study area. In this paper, we present comprehensive review that been implemented advance conservation. key contribution paper is it provides an overview current areas within their effectiveness improving prediction accuracy resource management categorized subfields, including properties, hydrology resources, wildfire management. We also highlight challenges future directions limitations applications This aims serve as reference decision-makers by offering insights into fields

Язык: Английский

Evaluation of Groundwater Potential Zones Using GIS‐Based Machine Learning Ensemble Models in the Gidabo Watershed, Ethiopia DOI Creative Commons
M. Mussa, Tarun Kumar Lohani, Abunu Atlabachew Eshete

и другие.

Global Challenges, Год журнала: 2024, Номер 8(12)

Опубликована: Окт. 16, 2024

Abstract The main objective of this study is to map and evaluate groundwater potential zones (GWPZs) using advanced ensemble machine learning (ML) models, notably Random Forest (RF) Support Vector Machine (SVM). GWPZs are identified by considering essential factors such as geology, drainage density, slope, land use/land cover (LULC), rainfall, soil, lineament density. This combined with datasets used for training validating the RF SVM which consisted 75 sites (boreholes springs), 22 non‐potential (bare lands settlement areas), 20 (water bodies). Each dataset randomly partitioned into two sets: (70%) validation (30%). model's performance evaluated area under receiver operating characteristic curve (AUC‐ROC). AUC model 0.91, compared 0.88 model. Both models classified effectively, but performed slightly better. GWPZ shows that high typically located within water bodies, natural springs, low‐lying regions, forested areas. In contrast, low primarily found in shrubland grassland vital decision‐makers it promotes sustainable use ensures security studied area.

Язык: Английский

Процитировано

1
Integrating Long term Satellite Data and Machine Learning to Spatiotemporal Fire Analysis in Hour al Azim International Wetland DOI

Seyed Fazel Hashemi,

Hossein Mohammad Asgari

Water Air & Soil Pollution, Год журнала: 2024, Номер 235(7)

Опубликована: Июнь 13, 2024

Язык: Английский

Процитировано

1
Forest Fire Susceptibility Zonation using dNBR and Machine Learning models: A case study at the Similipal Biosphere Reserve, Odisha, India DOI
Rajkumar Guria, Manoranjan Mishra,

Samiksha Mohanta

и другие.

Опубликована: Июль 16, 2024

Abstract Forests play a pivotal role in maintaining environmental equilibrium, chiefly due to their biodiversity. This biodiversity is instrumental atmospheric purification and oxygen production. Nowadays forest fires are an exciting phenomenon, identification of fire susceptible (FFS) areas necessary for mitigation management. study delves into trends susceptibility the Similipal Biosphere Reserve (SBR) over period 2012–2023. Utilizing four machine learning models such as Extreme Gradient Boosting Tree (XGBTree), AdaBag, Random Forest (RF), Machine (GBM). inventory was prepared using Delta Normalized Burn Ratio (dNBR) index. Incorporating 19 conditioning factors rigorous testing collinearity, FFS maps were generated, finally, model performance evaluated ROC-AUC, MAE, MSE, RMSE methods. From results, it observed that, overall, about 33.62% area exhibited high very fires. RF exhibiting highest accuracy (AUC = 0.85). Analysis temporal patterns highlighted peak incidents 2021, particularly notable Buffer Zone. Furthermore, significant majority (94.72%) occurred during March April. These findings serve valuable insights policymakers organizations involved management, underscoring importance targeted strategies high-risk areas.

Язык: Английский

Процитировано

1
Climate Impact Prediction: Whale-Optimized Conv-XGBoost with Remote Sensing and Sociological Data DOI

R. Jayakarthik,

Chandrashekhara K.T,

O. Sampath

и другие.

Remote Sensing in Earth Systems Sciences, Год журнала: 2024, Номер unknown

Опубликована: Сен. 18, 2024

Язык: Английский

Процитировано

1
Applications of Machine Learning and Remote Sensing in Soil and Water Conservation DOI Creative Commons
Kwang Jin Kim,

Woo Hyeon Park,

Yongchul Shin

и другие.

Hydrology, Год журнала: 2024, Номер 11(11), С. 183 - 183

Опубликована: Окт. 30, 2024

The application of machine learning (ML) and remote sensing (RS) in soil water conservation has become a powerful tool. As analytical tools continue to advance, the variety ML algorithms RS sources expanded, providing opportunities for more sophisticated analyses. At same time, researchers are required select appropriate technologies based on research objectives, topic, scope study area. In this paper, we present comprehensive review that been implemented advance conservation. key contribution paper is it provides an overview current areas within their effectiveness improving prediction accuracy resource management categorized subfields, including properties, hydrology resources, wildfire management. We also highlight challenges future directions limitations applications This aims serve as reference decision-makers by offering insights into fields

Язык: Английский

Процитировано

1