| 1. |
- Ahmadlou, Mohammad, et al.
(författare)
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Flood susceptibility mapping and assessment using a novel deep learning model combining multilayer perceptron and autoencoder neural networks
- 2021
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Ingår i: Journal of Flood Risk Management. - UK : John Wiley & Sons. - 1753-318X. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- Floods are one of the most destructive natural disasters causing financial dam-ages and casualties every year worldwide. Recently, the combination of data-driven techniques with remote sensing (RS) and geographical information sys-tems (GIS) has been widely used by researchers for flood susceptibility map-ping. This study presents a novel hybrid model combining the multilayerperceptron (MLP) and autoencoder models to produce the susceptibility mapsfor two study areas located in Iran and India. For two cases, nine, and twelvefactors were considered as the predictor variables for flood susceptibility map-ping, respectively. The prediction capability of the proposed hybrid model wascompared with that of the traditional MLP model through the area under thereceiver operating characteristic (AUROC) criterion. The AUROC curve for theMLP and autoencoder-MLP models were, respectively, 75 and 90, 74 and 93%in the training phase and 60 and 91, 81 and 97% in the testing phase, for Iranand India cases, respectively. The results suggested that the hybridautoencoder-MLP model outperformed the MLP model and, therefore, can beused as a powerful model in other studies for flood susceptibility mapping.
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| 2. |
- Aiyelokun, Oluwatobi, et al.
(författare)
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Credibility of design rainfall estimates for drainage infrastructures : extent of disregard in Nigeria and proposed framework for practice
- 2021
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Ingår i: Natural Hazards. - : Springer Science and Business Media LLC. - 0921-030X .- 1573-0840. ; 109:2, s. 1557-1588
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Tidskriftsartikel (refereegranskat)abstract
- Rainfall intensity or depth estimates are vital input for hydrologic and hydraulic models used in designing drainage infrastructures. Unfortunately, these estimates are susceptible to different sources of uncertainties including climate change, which could have high implications on the cost and design of hydraulic structures. This study adopts a systematic literature review to ascertain the disregard of credibility assessment of rainfall estimates in Nigeria. Thereafter, a simple framework for informing the practice of reliability check of rainfall estimates was proposed using freely available open-source tools and applied to the north central region of Nigeria. The study revealed through a synthesis matrix that in the last decade, both empirical and theoretical methods have been applied in predicting design rainfall intensities or depths for different frequencies across Nigeria, but none of the selected studies assessed the credibility of the design estimates. This study has established through the application of the proposed framework that drainage infrastructure designed in the study area using 100–1000-year return periods are more susceptible to error. And that the extent of the credibility of quantitative estimates of extreme rains leading to flooding is not equal for each variability indicator across a large spatial region. Hence, to optimize informed decision-making regarding flood risk reduction by risk assessor, variability and uncertainty of rainfall estimates should be assessed spatially to minimize erroneous deductions.
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| 3. |
- Ali, Sk Ajim, et al.
(författare)
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Sanitary landfill site selection by integrating AHP and FTOPSIS with GIS : a case study of Memari Municipality, India
- 2021
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Ingår i: Environmental Science and Pollution Research. - : Springer. - 0944-1344 .- 1614-7499. ; 28:6, s. 7528-7550
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Tidskriftsartikel (refereegranskat)abstract
- Sanitary landfill is still considered as one of the most significant and least expensive methods of waste disposal. It is essential to consider environmental impacts while selecting a suitable landfill site. Thus, the site selection for sanitary landfill is a complex and time-consuming task needing an assessment of multiple criteria. In the present study, a decision support system (DSS) was prepared for selecting a landfill site in a growing urban region. This study involved two steps of analysis. The first step of analysis involved the application of spatial data to prepare the thematic maps and derive their weight. The second step employed a fuzzy multicriteria decision-making (FMCDM) technique for prioritizing the identified landfill sites. Thus, initially, the analytic hierarchy process (AHP) was used for weighting the selected criteria, while the fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) was applied for addressing the uncertainty associated with decision-making and prioritizing the most suitable site. A case study was conducted in the city of Memari Municipality. The main goal of this study was the initial evaluation and acquisition of landfill candidate sites by utilizing GIS and the following decision criteria: (1) environmental criteria consisting of surface water, groundwater, land elevation, land use land cover, distance from urban residence and buildup, and distance from sensitive places; and (2) socioeconomic criteria including distance from the road, population density, and land value. For preparing the final suitability map, the integration of GIS layers and AHP was used. On output, 7 suitable landfill sites were identified which were further ranked using FTOPSIS based on expert’s views. Finally, candidate site-7 and site-2 were selected as the most suitable for proposing new landfill sites in Memari Municipality. The results from this study showed that the integration of GIS with the MCDM technique can be highly applied for site suitability. The present study will be helpful to local planners and municipal authorities for proposing a planning protocol and suitable sites for sanitary landfill in the near future.
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| 4. |
- Bandyopadhyay, Jatisankar, et al.
(författare)
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Identification and characterization the sources of aerosols over Jharkhand state and surrounding areas, India using AHP model
- 2021
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Ingår i: Geomatics, Natural Hazards and Risk. - : Taylor & Francis. - 1947-5705 .- 1947-5713. ; 12:1, s. 2194-2224
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Tidskriftsartikel (refereegranskat)abstract
- The Aerosol Optical Depth (AOD) has measured using remote sensing and GIS methods, with MODIS data collected in Jharkhand from 2011 to 2017. The state’s eastern and northern borders have greater aerosol loadings (AOD: >0.5) while the southern and western parts have lower aerosol loadings (AOD: <0.3). Primary, secondary, tertiary, and quaternary aerosol sources have been identified and categorized using the Analytic Hierarchical Process (AHP). Only 1.29% of the study area, which still emits the most aerosols, is covered by primary sources. Industrial zones, mining regions, thermal power plants, cement industries, high road density, and stone crushers are found in many locations throughout the country. Secondary sources of aerosols account for 5.23% of the study and are located near the main sources. The quaternary (54.08%) and tertiary (39.4%) aerosol sources mainly covered the Southern, Western, and North-Western portions of the state, which is enveloped by a heavily vegetated region. AOD, sources of aerosols, wind direction, and velocity were examined here. There were non-separable connections in this area and also AOD distribution is connected to aerosol sources, wind direction, and wind velocity. Finally, it employs the AOD values to identify different aerosol kinds and source heterogeneity to elucidate their influence.
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| 5. |
- Basu, Tirthankar, et al.
(författare)
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Development of an integrated peri-urban wetland degradation assessment approach for the Chatra Wetland in eastern India
- 2021
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Ingår i: Scientific Reports. - UK : Springer Nature. - 2045-2322. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- The loss of peri-urban wetlands is a major side effect of urbanization in India in recent days. Timely and proper assessment of wetland area change is essential for the conservation of wetlands. This study follows the integrated way of the peri-urban wetland degradation assessment in the case of medium and small-size urban agglomerations with a special focus on Chatra Wetland. Analysis of land-use and land cover (LULC) maps of the past 28 years shows a decrease of 60% area of the wetland including marshy land. This has reduced the ecosystem services value by about 71.90% over the period 1991–2018. From this end, The Land Change Modeler of IDRISI TerrSet using the combination of MLPNN and Markov Chain has been used to predict the LULC map of this region. The scenario-based modeling following the LULC conversion and nine explanatory variables suggests the complete loss of this wetland by 2045. However, the authors have also tried to present a future LULC pattern of this region based on an environmental perspective. This proposed map suggests possible areas for built-up expansion on the western side of the city without significantly affecting the environment.
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| 6. |
- Islam, Abu Reza Md Towfiqul, et al.
(författare)
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Machine learning algorithm-based risk assessment of riparian wetlands in Padma River Basin of Northwest Bangladesh
- 2021
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Ingår i: Environmental Science and Pollution Research. - : Springer Science and Business Media LLC. - 0944-1344 .- 1614-7499. ; 28, s. 34450-34471
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Tidskriftsartikel (refereegranskat)abstract
- Wetland risk assessment is a global concern especially in developing countries like Bangladesh. The present study explored the spatiotemporal dynamics of wetlands, prediction of wetland risk assessment. The wetland risk assessment was predicted based on ten selected parameters, such as fragmentation probability, distance to road, and settlement. We used M5P, random forest (RF), reduced error pruning tree (REPTree), and support vector machine (SVM) machine learning techniques for wetland risk assessment. The results showed that wetland areas at present are declining less than one-third of those in 1988 due to the construction of the dam at Farakka, which is situated at the upstream of the Padma River. The distance to the river and built-up area are the two most contributing drivers influencing the wetland risk assessment based on information gain ratio (InGR). The prediction results of machine learning models showed 64.48% of area by M5P, 61.75% of area by RF, 62.18% of area by REPTree, and 55.74% of area by SVM have been predicted as the high and very high-risk zones. The results of accuracy assessment showed that the RF outperformed than other models (area under curve: 0.83), followed by the SVM, M5P, and REPTree. Degradation of wetlands explored in this study demonstrated the negative effects on biodiversity. Therefore, to conserve and protect the wetlands, continuous monitoring of wetlands using high resolution satellite images, feeding with the ecological flow, confining built up area and agricultural expansion towards wetlands, and new wetland creation is essential for wetland management. Graphical abstract: [Figure not available: see fulltext.]
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| 7. |
- Mehdizadeh, Saeid, et al.
(författare)
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Development of boosted machine learning models for estimating daily reference evapotranspiration and comparison with empirical approaches
- 2021
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Ingår i: Water. - : MDPI AG. - 2073-4441. ; 13:24
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Tidskriftsartikel (refereegranskat)abstract
- Proper irrigation scheduling and agricultural water management require a precise estimation of crop water requirement. In practice, reference evapotranspiration (ETo) is firstly estimated, and used further to calculate the evapotranspiration of each crop. In this study, two new coupled models were developed for estimating daily ETo. Two optimization algorithms, the shuffled frog-leaping algorithm (SFLA) and invasive weed optimization (IWO), were coupled on an adaptive neuro-fuzzy inference system (ANFIS) to develop and implement the two novel hybrid models (ANFIS-SFLA and ANFIS-IWO). Additionally, four empirical models with varying complexities, including Hargreaves–Samani, Romanenko, Priestley–Taylor, and Valiantzas, were used and compared with the developed hybrid models. The performance of all investigated models was evaluated using the ETo estimates with the FAO-56 recommended method as a benchmark, as well as multiple statistical indicators including root-mean-square error (RMSE), relative RMSE (RRMSE), mean absolute error (MAE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE). All models were tested in Tabriz and Shiraz, Iran as the two studied sites. Evaluation results showed that the developed coupled models yielded better results than the classic ANFIS, with the ANFIS-SFLA outperforming the ANFIS-IWO. Among empirical models, generally the Valiantzas model in its original and calibrated versions presented the best performance. In terms of model complexity (the number of predictors), the model performance was obviously enhanced by an increasing number of predictors. The most accurate estimates of the daily ETo for the study sites were achieved via the hybrid ANFIS-SFLA models using full predictors, with RMSE within 0.15 mm day−1, RRMSE within 4%, MAE within 0.11 mm day−1, and both a high R2 and NSE of 0.99 in the test phase at the two studied sites.
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| 8. |
- Pham, Quoc Bao, et al.
(författare)
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A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping
- 2021
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Ingår i: Geomatics, Natural Hazards and Risk. - : Taylor & Francis. - 1947-5705 .- 1947-5713. ; 12:1, s. 1741-1777
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Tidskriftsartikel (refereegranskat)abstract
- Landslides are dangerous events which threaten both human life and property. The study aims to analyze the landslide susceptibility (LS) in the Kysuca river basin, Slovakia. For this reason, previous landslide events were analyzed with 16 landslide conditioning factors. Landslide inventory was divided into training (70% of landslide locations) and validating dataset (30% of landslide locations). The heuristic approach of Fuzzy Decision Making Trial and Evaluation Laboratory (FDEMATEL)-Analytic Network Process (ANP) was applied first, followed by bivariate Frequency Ratio (FR), multivariate Logistic Regression (LR), Random Forest Classifier (RFC), Naive Bayes Classifier (NBC) and Extreme Gradient Boosting (XGBoost), respectively. The results showed that 52.2%, 36.5%, 40.7%, 50.6%, 43.6% and 40.3% of the total basin area had very high to high LS corresponding to FDEMATEL-ANP, FR, LR, RFC, NBC and XGBoost model, respectively. The analysis revealed that RFC was the most accurate model (overall accuracy of 98.3% and AUC of 97.0%). Besides, the heuristic approach of FDEMATEL-ANP model (overall accuracy of 93.8% and AUC of 92.4%) had better prediction capability than bivariate FR (overall accuracy of 86.9% and AUC of 86.1%), multivariate LR (overall accuracy of 90.5% and AUC of 91.2%), machine learning NBC (overall accuracy of 76.3% and AUC of 90.9%) and even deep learning XGBoost (overall accuracy of 92.3% and AUC of 87.1%) models. The study revealed that the FDEMATEL-ANP outweighed the NBC and XGBoost machine learning models, which suggests that heuristic methods should be tested out before directly applying machine learning models.
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| 9. |
- Pham, Quoc Bao, et al.
(författare)
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A new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation
- 2021
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Ingår i: Environmental Science and Pollution Research. - : Springer Science and Business Media LLC. - 0944-1344 .- 1614-7499. ; 28, s. 32564-32579
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Tidskriftsartikel (refereegranskat)abstract
- Precise monitoring of cyanobacteria concentration in water resources is a daunting task. The development of reliable tools to monitor this contamination is an important research topic in water resources management. Indirect methods such as chlorophyll-a determination, cell counting, and toxin measurement of the cyanobacteria are tedious, cumbersome, and often lead to inaccurate results. The quantity of phycocyanin (PC) pigment is considered more appropriate for cyanobacteria monitoring. Traditional approaches for PC estimation are time-consuming, expensive, and require high expertise. Recently, some studies have proposed the application of artificial intelligence (AI) techniques to predict the amount of PC concentration. Nonetheless, most of these researches are limited to standalone modeling schemas such as artificial neural network (ANN), multilayer perceptron (MLP), and support vector machine (SVM). The independent schema provides imprecise results when faced with highly nonlinear systems and data uncertainties resulting from environmental disturbances. To alleviate the limitations of the existing models, this study proposes the first application of a hybrid AI model that integrates the potentials of relevance vector machine (RVM) and flower pollination algorithm (RVM-FPA) to predict the PC concentration in water resources. The performance of the hybrid model is compared with the standalone RVM model. The prediction performance of the proposed models was evaluated at two stations (stations 508 and 478) using different statistical and graphical performance evaluation methods. The results showed that the hybrid models exhibited higher performance at both stations compared to the standalone RVM model. The proposed hybrid RVM-FPA can therefore serve as a reliable predictive tool for PC concentration in water resources.
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| 10. |
- Shafeeque, Muhammad, et al.
(författare)
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Understanding temporary reduction in atmospheric pollution and its impacts on coastal aquatic system during COVID-19 lockdown : a case study of South Asia
- 2021
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Ingår i: Geomatics, Natural Hazards and Risk. - UK : Taylor & Francis. - 1947-5705 .- 1947-5713. ; 12:1, s. 560-580
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Tidskriftsartikel (refereegranskat)abstract
- The strict lockdown measures not only contributed to curbing the spread of COVID-19 infection, but also improved the environmental conditions worldwide. The main goal of the current study was to investigate the co-benefits of COVID-19 lockdown on the atmosphere and aquatic ecological system under restricted anthropogenic activities in South Asia. The remote sensing data (a) NO2 emissions from the Ozone Monitoring Instrument (OMI), (b) Aerosol Optical Depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS), and (c) chlorophyll (Chl-a) and turbidity data from MODIS-Aqua Level-3 during Jan–Oct (2020) were analyzed to assess the changes in air and water pollution compared to the last five years (2015–2019). The interactions between the air and water pollution were also investigated using overland runoff and precipitation in 2019 and 2020 at a monthly scale to investigate the anomalous events, which could affect the N loading to coastal regions. The results revealed a considerable drop in the air and water pollution (30–40% reduction in NO2 emissions, 45% in AOD, 50% decline in coastal Chl-a concentration, and 29% decline in turbidity) over South Asia. The rate of reduction in NO2 emissions was found the highest for Lahore (32%), New Delhi (31%), Ahmadabad (29%), Karachi (26%), Hyderabad (24%), and Chennai (17%) during the strict lockdown period from Apr–Jun, 2020. A positive correlation between AOD and NO2 emissions (0.23–0.50) implies that a decrease in AOD is attributed to a reduction in NO2. It was observed that during strict lockdown, the turbidity has decreased by 29%, 11%, 16%, and 17% along the coastal regions of Karachi, Mumbai, Calcutta, and Dhaka, respectively, while a 5–6% increase in turbidity was seen over the Madras during the same period. The findings stress the importance of reduced N emissions due to halted fossil fuel consumption and their relationships with the reduced air and water pollution. It is concluded that the atmospheric and hydrospheric environment can be improved by implementing smart restrictions on fossil fuel consumption with a minimum effect on socioeconomics in the region. Smart constraints on fossil fuel usage are recommended to control air and water pollution even after the social and economic activities resume business-as-usual scenario.
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