Mapping the spatial and temporal variability of flood susceptibility using remotely sensed normalized difference vegetation index and the forecasted changes in the future

Multitemporal river flow discharge prediction: A new framework for integrated environmental management and flood control

Riverine flow estimation is critical for effective water resource management and mitigation planning. Traditional machine learning and deep learning models offer various advantages, but their effectiveness in multitemporal river flow discharge estimation has yet to be fully explored. This study introduces an advanced universal group method of data handling (AUGMDH) model to predict river flow discharge across various temporal scales. The accuracy of the proposed model is compared with that of convolutional neural network (CNN) models in terms of estimating daily, mean monthly, and maximum monthly flow discharge. The AUGMDH model consistently outperforms the CNN models across all major performance metrics, such as the coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), normalized root mean square error (NRMSE), RMSE-observed standard deviation ratio (RSR), and percent bias (PBIAS), achieving an R2 of 0.972 and an NSE of 0.972 for the daily flow, an R2 of 0.810 and an NSE of 0.810 for the mean monthly flow, and an R2 of 0.819 and an NSE of 0.818 for the maximum monthly flow. Additionally, compared to the CNN approach, the AUGMDH model yields lower AIC values across all the cases (AIC: 37,744 for daily, 2144 for mean monthly, and 2543 for maximum monthly), indicating a better balance between simplicity and accuracy. In terms of uncertainty analysis, the AUGMDH model exhibits lower uncertainty values (i.e., 2.77 for daily flow, 2.31 for mean monthly flow, and 2.46 for maximum monthly flow estimates) than the CNN models do (i.e., 2.78 for daily flow, 2.48 mean monthly flow, and 2.66 for maximum monthly flow estimates). The findings indicate that the AUGMDH model provides a more robust and reliable solution for riverine flood estimation, outperforming the CNN models across all major performance metrics, including accuracy, reliability, and computational efficiency.

Artificial intelligence-driven assessment of critical inputs for lead adsorption by agro-food wastes in wastewater treatment

Due to environmental concerns and economic value, the adsorption process using agricultural wastes is one of the promising methods to remove lead (Pb) from contaminated water. The relationships between agricultural waste properties, adsorption conditions, and the maximum Pb adsorption capacity of selected adsorbents have not been adequately explored. A thorough understanding of these interactions is crucial for optimizing adsorption processes and enhancing the efficiency of agricultural wastes as sustainable adsorbents. To assess Pb adsorption by agricultural wastes and identify the key influencing factors, three artificial intelligence techniques, namely Extreme Learning Machine (ELM), Adaptive Nuro-Fuzzy Inference Systems (ANFIS), and Group Method of Data Handling (GMDH) have been employed in this study. Seven input variables, namely time, ratio, initial ion concentration, type of adsorbents from agricultural wastes, pH, temperature, and agitation speed, from 771 data points were used as inputs for model development, while the quantity of Pb adsorbed was chosen as target parameter. To identify the best input combinations with one to seven variables, 127 models were defined and analyzed using ELM integrated with the cross-validation technique. The results highlighted that the initial ion concentration is the most critical factor in enhancing heavy metal adsorption, and temperature is the least important factor. The top models, utilizing one to seven input variable(s), were then modeled with ANFIS and GMDH. Subsequently, all three models were compared. The GMDH model with four input variables (initial ion concentration, type of adsorbent, time, and agitation speed) demonstrated the highest performance in terms of accuracy and simplicity.

Impact of climate change on future flood susceptibility projections under shared socioeconomic pathway scenarios in South Asia using artificial intelligence algorithms

This study investigated the impact of climate change on flood susceptibility in six South Asian countries Afghanistan, Bangladesh, Bhutan, Bharat (India), Nepal, and Pakistan-under two distinct Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6 and SSP5-5.8, for 2041-2060 and 2081-2100. To predict flood susceptibility, we employed three artificial intelligence (AI) algorithms: the K-nearest neighbor (KNN), conditional inference random forest (CIRF), and regularized random forest (RRF). Predictions were based on data from 2452 historical flood events, alongside climatic variables measured over monthly, seasonal, and annual timeframes. The innovative aspect of this research is the emphasis on using climatic variables across these progressively condensed timeframes, specifically addressing eight precipitation factors. The performance evaluation, employing the area under the receiver operating characteristic curve (AUC) metric, identified the RRF model as the most accurate, with the highest AUC of 0.94 during the testing phase, followed by the CIRF (AUC = 0.91) and the KNN (AUC = 0.86). An analysis of variable importance highlighted the substantial role of certain climatic factors, namely precipitation in the warmest quarter, annual precipitation, and precipitation during the wettest month, in the modeling of flood susceptibility in South Asia. The resultant flood susceptibility maps demonstrated the influence of climate change scenarios on susceptibility classifications, signalling a dynamic landscape of flood-prone areas over time. The findings revealed variable trends under different climate change scenarios and periods, with marked differences in the percentage of areas classified as having high and very high flood susceptibility. Overall, this study advances our understanding of how climate change affects flood susceptibility in South Asia and offers an essential tool for assessing and managing flood risks in the region.

Unsupervised learning of probabilistic subspaces for multi-spectral and multi-temporal image-based disaster mapping

Accurate and timely identification of regions damaged by a natural disaster is critical for assessing the damages and reducing the human life cost. The increasing availability of satellite imagery and other remote sensing data has triggered research activities on development of algorithms for detection and monitoring of natural events. Here, we introduce an unsupervised subspace learning-based methodology that uses multi-temporal and multi-spectral satellite images to identify regions damaged by natural disasters. It first performs region delineation, matching, and fusion. Next, it applies subspace learning in the joint regional space to produce a change map. It identifies the damaged regions by estimating probabilistic subspace distances and rejecting the non-disaster changes. We evaluated the performance of our method on seven disaster datasets including four wildfire events, two flooding events, and a earthquake/tsunami event. We validated our results by calculating the dice similarity coefficient (DSC), and accuracy of classification between our disaster maps and ground-truth data. Our method produced average DSC values of 0.833 and 0.736, for wildfires and floods, respectively, and overall DSC of 0.855 for the tsunami event. The evaluation results support the applicability of our method to multiple types of natural disasters.

Higher greenspace exposure is associated with a decreased risk of childhood asthma in Shanghai - A megacity in China

Inconsistent evidence exists about whether exposure to greenspace benefits childhood asthma. Previous studies have only focused on residential or school greenspace, and no research has combined greenspace exposures at both homes and schools to determine their link with childhood asthma. A population-based cross-sectional study was conducted among 16,605 children during 2019 in Shanghai, China. Self-reported questionnaires were used to collect information on childhood asthma and demographic, socioeconomic and behavioural factors. Environmental data including ambient temperature, particulate matter with aerodynamic diameter less than 1 µm (PM₁), enhanced vegetation index (EVI), and normalized difference vegetation index (NDVI) were collected from satellite data. Binomial generalized linear models with a logit link were carried out to evaluate the association between greenspace exposure and children's asthma, as well as the effect modifiers. An interquartile range increment of whole greenspace (NDVI₅₀₀, NDVI₂₅₀, EVI₅₀₀, and EVI₂₅₀) exposure was associated with a reduced odds ratio of children's asthma (0.88, 95% CI: 0.78, 0.99; 0.89, 95% CI: 0.79, 1.01; 0.87, 95% CI: 0.77, 0.99; and 0.88, 95% CI: 0.78, 0.99, respectively) after controlling potential confounders. Low temperature, low PM₁, males, vaginal delivery, suburban/rural area, and without family history of allergy appeared to enhance the greenspace-asthma association. Increased greenspace exposure was associated with a lower risk of childhood asthma, and the association was modified by a range of socio-environmental factors. These findings add to the body of evidence on the benefits of biodiversity and supporting the promotion of urban greenspace to protect children's health.

Runoff Estimation in the Upper Reaches of the Heihe River Using an LSTM Model with Remote Sensing Data

Runoff estimations play an important role in water resource planning and management. Many accomplishments have been made in runoff estimations based on data recorded at meteorological stations; however, the advantages of the use of remotely sensed data in estimating runoff in watersheds for which data are lacking remain to be investigated. In this study, the MOD13A2 normalized difference vegetation index (NDVI), TRMM3B43 precipitation (P), MOD11A2 land–surface temperature (LST), MOD16A2 evapotranspiration (ET) and hydrological station data were used as data sources with which to estimate the monthly runoff through the application of a fully connected long short–term memory (LSTM) model in the upstream reach of the Heihe River basin in China from 2001 to 2016. The results showed that inputting multiple remote sensing parameters improved the quality of runoff estimation compared to the use of rain gauge observations; an increase in R2 from 0.91 to 0.94 was observed from the implementation of this process, and Nash–Sutcliffe efficiency (NSE) showed an improvement from 0.89 to 0.93. The incorporation of rain gauge data as well as satellite data provided a slight improvement in estimating runoff with a respective R2 value of 0.95 and NSE value of 0.94. This indicates that the LSTM model based on remote sensing data has great potential for runoff estimation, and data obtained by remote sensing technology provide an alternative approach for estimating runoff in areas for which available data are lacking.

Flood Risk Mapping by Remote Sensing Data and Random Forest Technique

Detecting effective parameters in flood occurrence is one of the most important issues that has drawn more attention in recent years. Remote Sensing (RS) and Geographical Information System (GIS) are two efficient ways to spatially predict Flood Risk Mapping (FRM). In this study, a web-based platform called the Google Earth Engine (GEE) (Google Company, Mountain View, CA, USA) was used to obtain flood risk indices for the Galikesh River basin, Northern Iran. With the aid of Landsat 8 satellite imagery and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), 11 risk indices (Elevation (El), Slope (Sl), Slope Aspect (SA), Land Use (LU), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Topographic Wetness Index (TWI), River Distance (RD), Waterway and River Density (WRD), Soil Texture (ST]), and Maximum One-Day Precipitation (M1DP)) were provided. In the next step, all of these indices were imported into ArcMap 10.8 (Esri, West Redlands, CA, USA) software for index normalization and to better visualize the graphical output. Afterward, an intelligent learning machine (Random Forest (RF)), which is a robust data mining technique, was used to compute the importance degree of each index and to obtain the flood hazard map. According to the results, the indices of WRD, RD, M1DP, and El accounted for about 68.27 percent of the total flood risk. Among these indices, the WRD index containing about 23.8 percent of the total risk has the greatest impact on floods. According to FRM mapping, about 21 and 18 percent of the total areas stood at the higher and highest risk areas, respectively.

Prognostication of Shortwave Radiation Using an Improved No-Tuned Fast Machine Learning

Shortwave radiation density flux (SRDF) modeling can be key in estimating actual evapotranspiration in plants. SRDF is the result of the specific and scattered reflection of shortwave radiation by the underlying surface. SRDF can have profound effects on some plant biophysical processes such as photosynthesis and land surface energy budgets. Since it is the main energy source for most atmospheric phenomena, SRDF is also widely used in numerical weather forecasting. In the current study, an improved version of the extreme learning machine was developed for SRDF forecasting using the historical value of this variable. To do that, the SRDF through 1981–2019 was extracted by developing JavaScript-based coding in the Google Earth Engine. The most important lags were found using the auto-correlation function and defined fifteen input combinations to model SRDF using the improved extreme learning machine (IELM). The performance of the developed model is evaluated based on the correlation coefficient (R), root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash–Sutcliffe efficiency (NSE). The shortwave radiation was developed for two time ahead forecasting (R = 0.986, RMSE = 21.11, MAPE = 8.68%, NSE = 0.97). Additionally, the estimation uncertainty of the developed improved extreme learning machine is quantified and compared with classical ELM and found to be the least with a value of ±3.64 compared to ±6.9 for the classical extreme learning machine. IELM not only overcomes the limitation of the classical extreme learning machine in random adjusting of bias of hidden neurons and input weights but also provides a simple matrix-based method for practical tasks so that there is no need to have any knowledge of the improved extreme learning machine to use it.