A simulation of the rainfall-runoff process using artificial neural network and HEC-HMS model in forest lands

Gholami, Vahid; Khaleghi, Mohammad Reza

doi:10.17221/90/2020-jfs

Cited by 16 publications

(6 citation statements)

References 41 publications

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“…Artificial Neural Networks (ANN) are a type of Machine Learning system modeled on the biological neurons and synapses within the human brain (Gholami et al 2015).…”

Section: Artificial Neural Network Algorithmmentioning

confidence: 99%

Artificial Intelligence Based Flood Forecasting for River Hunza at Danyor Station in Pakistan

Yaseen

Awais

Riaz

et al. 2022

Archives of Hydro-Engineering and Environmental Mechanics

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Floods can cause significant problems for humans and can damage the economy. Implementing a reliable flood monitoring warning system in risk areas can help to reduce the negative impacts of these natural disasters. Artificial intelligence algorithms and statistical approaches are employed by researchers to enhance flood forecasting. In this study, a dataset was created using unique features measured by sensors along the Hunza River in Pakistan over the past 31 years. The dataset was used for classification and regression problems. Two types of machine learning algorithms were tested for classification: classical algorithms (Random Forest, RF and Support Vector Classifier, SVC) and deep learning algorithms (Multi-Layer Perceptron, MLP). For the regression problem, the result of MLP and Support Vector Regression (SVR) algorithms were compared based on their mean square, root mean square and mean absolute errors. The results obtained show that the accuracy of the RF classifier is 0.99, while the accuracies of the SVC and MLP methods are 0.98; moreover, in the case of flood prediction, the SVR algorithm outperforms the MLP approach.

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“…Artificial Neural Networks (ANN) are a type of Machine Learning system modeled on the biological neurons and synapses within the human brain (Gholami et al 2015).…”

Section: Artificial Neural Network Algorithmmentioning

confidence: 99%

Artificial Intelligence Based Flood Forecasting for River Hunza at Danyor Station in Pakistan

Yaseen

Awais

Riaz

et al. 2022

Archives of Hydro-Engineering and Environmental Mechanics

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“…Curah hujan menjadi salah satu parameter dalam penentuan daerah resapan air, hal ini disebabkan oleh intensitas dan durasi curah hujan mempengaruhi besaran air yang meresap ke dalam tanah (Gholami & Khaleghi, 2021;Mansida et al, 2021). Semakin besar dan lama intensitas curah hujan, maka semakin besar juga air yang dapat meresap ke dalam tanah.…”

Section: A Parameter Spasial Penentu Potensi Resapan Airunclassified

ANALISIS SPASIAL POTENSI RESAPAN AIR UNTUK MENDUKUNG PENGELOLAAN DAERAH ALIRAN SUNGAI (DAS) UNDA PROVINSI BALI (Spatial Analysis of Water Infiltration Potential to Support The Management of Unda Watershed at Bali Province)

Wiyanti

Susila²,

Suyarto³

et al. 2022

JPPDAS

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The Unda Watershed is categorized as the priority watershed, which part of its area is degraded land. The condition of land cover with the dominance of low-density vegetation has implications for low infiltration and water absorption capacity, so that it has the potential to increase surface runoff. This study aims to identify the potential for water absorption as the basis for watershed management planning. The method used in this study is scoring with four parameters, namely soil type, land use, slope, and rainfall. Spatial data analysis uses Geographic Information System (GIS). The formulation of recommendations for watershed management activities is carried out descriptively, based on the findings of the analysis of water absorption potential. The results showed that the water recharge conditions in the study area were classified as good (22.54%), very critical (18.20%), normal (17.92%), and less critical (7.99%). The recommended watershed management is periodic reforestation, construction of bench/traditional terraces, construction of infiltration wells/biopori holes in the residential areas, and synergistic integration of the roles of watershed institutions from upstream to downstream.

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“…Precipitation can directly affect the growth of soil fungi by changing soil moisture (15, 16, 17). At the same time, runoff caused by precipitation causes the distribution of nutrients and changes in root biomass (18, 19, 20), indirectly affecting soil fungal communities’ occurrence and spread (21, 22, 23, 24, 25). Similarly, temperature changes can also affect the input of organic matter by changing plant communities and productivity (26), thereby indirectly affecting the occurrence of soil fungal communities (27, 28).…”

Section: Introductionmentioning

confidence: 99%

Dominant Role of Stochastic Processes in Soil Fungal Communities in Pioneer Forests at a Regional Scale

Man

Zeng

Zhou

et al. 2023

Preprint

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Soil fungal community assembly is driven by deterministic and stochastic processes. However, the contribution of these mechanisms to structure the composition of fungal communities of forest soils at the regional scale is poorly known. Here, we investigate the relative importance of deterministic and stochastic processes on fungal community composition by rDNA ITS metabarcoding in aPopulus davidianapioneer forests along spatial-temporal gradients. We also assessed the impact of elevation and seasonality. The soil fungal richness ofP. davidianapioneer forests was significantly affected by elevation and less affected by season. Similarly, the variation in the fungal community composition according to the elevation was greater than the effect of seasonality. The fungal community composition showed a significant distance-decay relationship. Variation partitioning analysis showed that plant variables explained the soil fungal community variation. Through null model analysis, we found that stochastic processes were dominant in the soil fungal community assembly. However, the relative importance of ecological processes, including dispersal, selection, and drift, was not consistent across the four soil fungal community assemblies. In addition, the undominated fraction (including weak selection, weak dispersal, diversification and drift) had a high relative contribution to the soil fungal community assembly process in theP. davidianapioneer forest. In summary, our results demonstrated that plant variables and the undominated fraction dominate the deterministic and stochastic processes driving soil fungal community assemblies in aP. davidianapioneer forest at the regional scale, which provides new perspectives for the regional scale studies of soil fungi.IMPORTANCEElevation and seasonality are important factors driving the composition of soil microbiota. Due to the tight interactions of soil fungi with their host trees in forest ecosystems, the spatial variation of soil fungal community is often linked to the variation in the composition of dominant tree species. We compared the responses of soil fungal communities to seasonal and spatial changes at four levels in a temperate poplar forest dominated by a single tree species under elevation changes. Elevation had a higher impact than seasonality on the soil fungal beta diversity. Even when the shift in dominant tree species was limited, vegetation factors still impact soil fungal community variations. The dominant role of homogeneous selection and drift in fungal community assemblies, except for ectomycorrhizal fungi, was further discovered.

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A simulation of the rainfall-runoff process using artificial neural network and HEC-HMS model in forest lands

Cited by 16 publications

References 41 publications

Artificial Intelligence Based Flood Forecasting for River Hunza at Danyor Station in Pakistan

Artificial Intelligence Based Flood Forecasting for River Hunza at Danyor Station in Pakistan

ANALISIS SPASIAL POTENSI RESAPAN AIR UNTUK MENDUKUNG PENGELOLAAN DAERAH ALIRAN SUNGAI (DAS) UNDA PROVINSI BALI (Spatial Analysis of Water Infiltration Potential to Support The Management of Unda Watershed at Bali Province)

Dominant Role of Stochastic Processes in Soil Fungal Communities in Pioneer Forests at a Regional Scale

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