Application of ensemble machine learning model in downscaling and projecting climate variables over different climate regions in Iran
In: Environmental science and pollution research: ESPR, Band 29, Heft 12, S. 17260-17279
ISSN: 1614-7499
16 Ergebnisse
Sortierung:
In: Environmental science and pollution research: ESPR, Band 29, Heft 12, S. 17260-17279
ISSN: 1614-7499
In: Environmental science and pollution research: ESPR, Band 30, Heft 13, S. 38063-38075
ISSN: 1614-7499
In: Environmental science and pollution research: ESPR, Band 29, Heft 14, S. 20496-20516
ISSN: 1614-7499
In: Scientific African, Band 13, S. e00921
ISSN: 2468-2276
In: Environmental science and pollution research: ESPR, Band 29, Heft 47, S. 71270-71289
ISSN: 1614-7499
In: Environmental science and pollution research: ESPR, Band 28, Heft 29, S. 39139-39158
ISSN: 1614-7499
In: IJDRR-D-23-01371
SSRN
In: Waste management: international journal of integrated waste management, science and technology, Band 95, S. 10-21
ISSN: 1879-2456
In: Environmental science and pollution research: ESPR, Band 29, Heft 60, S. 91212-91231
ISSN: 1614-7499
In: Environmental science and pollution research: ESPR, Band 31, Heft 10, S. 15986-16010
ISSN: 1614-7499
In: Environmental science and pollution research: ESPR, Band 28, Heft 25, S. 32564-32579
ISSN: 1614-7499
In: Environmental science and pollution research: ESPR, Band 26, Heft 1, S. 923-937
ISSN: 1614-7499
In: Environmental science and pollution research: ESPR, Band 28, Heft 45, S. 64818-64829
ISSN: 1614-7499
In: Environmental sciences Europe: ESEU, Band 35, Heft 1
ISSN: 2190-4715
AbstractRainfall is crucial for the development and management of water resources. Six hybrid soft computing models, including multilayer perceptron (MLP)–Henry gas solubility optimization (HGSO), MLP–bat algorithm (MLP–BA), MLP–particle swarm optimization (MLP–PSO), radial basis neural network function (RBFNN)–HGSO, RBFNN–PSO, and RBFGNN–BA, were used in this study to forecast monthly rainfall at two stations in Malaysia (Sara and Banding). Different statistical measures (mean absolute error (MAE) and Nash–Sutcliffe efficiency (NSE) and percentage of BIAS (PBIAS)) and a Taylor diagram were used to assess the models' performance. The results indicated that the MLP–HGSO performed better than the other models in forecasting rainfall at both stations. In addition, transition matrices were computed for each station and year based on the conditional probability of rainfall or absence of rainfall on a given month. The values of MAE for testing processes for the MLP–HGSO, MLP–PSO, MLP–BA, RBFNN–HGSO, RBFNN–BA, and RBFNN–PSO at the first station were 0.712, 0.755, 0.765, 0.717, 0.865, and 0.891, while the corresponding NSE and PBIAS values were 0.90–0.23, 0.83–0.29, 0.85–0.25, 0.87–0.27, 0.81–0.31, and 0.80–0.35, respectively. For the second station, the values of MAE were found 0.711, 0.743, 0.742, 0.719, 0.863 and 0.890 for the MLP–HGSO, MLP–PSO, MLP–BA, RBFNN–HGSO, RBFNN–BA, and RBFNN–PSO during testing processes and the corresponding NSE–PBIAS values were 0.92–0.22, 0.85–0.28, 0.89–0.26, 0.91–0.25, 0.83–0.31, 0.82–0.32, respectively. Based on the outputs of the MLP–HGSO, the highest rainfall was recorded in 2012 with a probability of 0.72, while the lowest rainfall was recorded in 2006 with a probability of 0.52 at the Sara Station. In addition, the results indicated that the MLP–HGSO performed better than the other models within the Banding Station. According to the findings, the hybrid MLP–HGSO was selected as an effective rainfall prediction model.
An approach is proposed in the present study to estimate the soil erosion in data-scarce Kokcha subbasin in Afghanistan. The Revised Universal Soil Loss Equation (RUSLE) model is used to estimate soil erosion. The satellite-based data are used to obtain the RUSLE factors. The results show that the slight (71.34%) and moderate (25.46%) erosion are dominated in the basin. In contrast, the high erosion (0.01%) is insignificant in the study area. The highest amount of erosion is observed in Rangeland (52.2%) followed by rainfed agriculture (15.1%) and barren land (9.8%) while a little or no erosion is found in areas with fruit trees, forest and shrubs, and irrigated agriculture land. The highest soil erosion was observed in summer (June–August) due to snow melting from high mountains. The spatial distribution of soil erosion revealed higher risk in foothills and degraded lands. It is expected that the methodology presented in this study for estimation of spatial and seasonal variability soil erosion in a remote mountainous river basin can be replicated in other similar regions for management of soil, agriculture, and water resources. ; Validerad;2021;Nivå 2;2021-02-25 (alebob); Finansiär: Ministry of Education, Government of Afghanistan; Universiti Teknologi Malaysia
BASE