scholarly journals Retraction Note: Climate change in river basin based on machine learning and regional financial risk identification

2021 ◽  
Vol 14 (24) ◽  
Author(s):  
Yue Liu
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
River Basin ◽  
Financial Risk ◽  
Risk Identification

scholarly journals Modelling hydrological responses under climate change using machine learning algorithms – semi-arid river basin of peninsular India

2020 ◽  
Vol 3 (1) ◽  
pp. 481-498
Author(s):  
G. Sireesha Naidu ◽  
M. Pratik ◽  
S. Rehana
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Random Forest ◽  
River Basin ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Peninsular India ◽  
Hydrological Responses ◽  
Semi Arid

Abstract Catchment scale conceptual hydrological models apply calibration parameters entirely based on observed historical data in the climate change impact assessment. The study used the most advanced machine learning algorithms based on Ensemble Regression and Random Forest models to develop dynamically calibrated factors which can form as a basis for the analysis of hydrological responses under climate change. The Random Forest algorithm was identified as a robust method to model the calibration factors with limited data for training and testing with precipitation, evapotranspiration and uncalibrated runoff based on various performance measures. The developed model was further used to study the runoff response under climate change variability of precipitation and temperatures. A statistical downscaling model based on K-means clustering, Classification and Regression Trees and Support Vector Regression was used to develop the precipitation and temperature projections based on MIROC GCM outputs with the RCP 4.5 scenario. The proposed modelling framework has been demonstrated on a semi-arid river basin of peninsular India, Krishna River Basin (KRB). The basin outlet runoff was predicted to decrease (13.26%) for future scenarios under climate change due to an increase in temperature (0.6 °C), compared to a precipitation increase (13.12%), resulting in an overall reduction in water availability over KRB.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

EVALUATION OF IMPACT ON WATER USE CAUSED BY CLIMATE CHANGE IN SOLO RIVER BASIN, INDONESIA

2018 ◽  
Vol 74 (4) ◽  
pp. I_121-I_126
Author(s):  
Hitoshi UMINO ◽  
Maksym GUSYEV ◽  
Akira HASEGAWA ◽  
Yoji CHIDA
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Water Use

Assessment of climate change impacts on water balance and hydrological extremes in Bang Pakong-Prachin Buri river basin, Thailand

2020 ◽  
Vol 186 ◽  
pp. 109544 ◽  
Author(s):  
Thundorn Okwala ◽  
Sangam Shrestha ◽  
Suwas Ghimire ◽  
S. Mohanasundaram ◽  
Avishek Datta
Keyword(s):  
Climate Change ◽  
Water Balance ◽  
River Basin ◽  
Climate Change Impacts ◽  
Hydrological Extremes

scholarly journals Impacts of climate change and LULC change on runoff in the Jinsha River Basin

2020 ◽  
Vol 30 (1) ◽  
pp. 85-102 ◽  
Author(s):  
Qihui Chen ◽  
Hua Chen ◽  
Jun Zhang ◽  
Yukun Hou ◽  
Mingxi Shen ◽  
...  
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Jinsha River ◽  
Lulc Change ◽  
Impacts Of Climate Change

scholarly journals Modelling of the Discharge Response to Climate Change under RCP8.5 Scenario in the Alata River Basin (Mersin, SE Turkey)

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 483
Author(s):  
Ümit Yıldırım ◽  
Cüneyt Güler ◽  
Barış Önol ◽  
Michael Rode ◽  
Seifeddine Jomaa
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Hydrological Model ◽  
Baseline Period ◽  
Change Scenario ◽  
Climate Projections ◽  
Worst Case ◽  
Distributed Process ◽  
In The Beginning ◽  
Se Turkey

This study investigates the impacts of climate change on the hydrological response of a Mediterranean mesoscale catchment using a hydrological model. The effect of climate change on the discharge of the Alata River Basin in Mersin province (Turkey) was assessed under the worst-case climate change scenario (i.e., RCP8.5), using the semi-distributed, process-based hydrological model Hydrological Predictions for the Environment (HYPE). First, the model was evaluated temporally and spatially and has been shown to reproduce the measured discharge consistently. Second, the discharge was predicted under climate projections in three distinct future periods (i.e., 2021–2040, 2046–2065 and 2081–2100, reflecting the beginning, middle and end of the century, respectively). Climate change projections showed that the annual mean temperature in the Alata River Basin rises for the beginning, middle and end of the century, with about 1.35, 2.13 and 4.11 °C, respectively. Besides, the highest discharge timing seems to occur one month earlier (February instead of March) compared to the baseline period (2000–2011) in the beginning and middle of the century. The results show a decrease in precipitation and an increase in temperature in all future projections, resulting in more snowmelt and higher discharge generation in the beginning and middle of the century scenarios. However, at the end of the century, the discharge significantly decreased due to increased evapotranspiration and reduced snow depth in the upstream area. The findings of this study can help develop efficient climate change adaptation options in the Levant’s coastal areas.

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