Observed and future shifts in climate zone of Borneo based on CMIP6 models.

Climate change has significantly altered the characteristics of climate zones, posing considerable challenges to ecosystems and biodiversity, particularly in Borneo, known for its high species density per unit area. This study aimed to classify the region into homogeneous climate groups based on long-term average behavior. The most effective parameters from the high-resolution daily gridded Princeton climate datasets spanning 65 years (1950-2014) were utilized, including rainfall, relative humidity (RH), temperatures (Tavg, Tmin, Tmax, and diurnal temperature range (DTR)), along with elevation data at 0.25° resolution. The FCM clustering method outperformed K-Mean and two Ward's hierarchical methods (WardD and WardD2) in classifying Borneo's climate zones based on multi-criteria assessment, exhibiting the lowest average distance (2.172-2.180) and the highest compromise programming index (CPI)-based correlation ranking among cluster averages across all climate parameters. Borneo's climate zones were categorized into four: 'Wet and cold' (WC) and 'Wet' (W) representing wetter zones, and 'Wet and hot' (WH) and 'Dry and hot' (DH) representing hotter zones, each with clearly defined boundaries. For future projection, EC-Earth3-Veg ranked first for all climate parameters across 961 grid points, emerging as the top-performing model. The linear scaling (LS) bias-corrected EC-Earth3-Veg model, as shown in the Taylor diagram, closely replicated the observed datasets, facilitating future climate zone reclassification. Improved performance across parameters was evident based on MAE (35.8-94.6%), MSE (57.0-99.5%), NRMSE (42.7-92.1%), PBIAS (100-108%), MD (23.0-85.3%), KGE (21.1-78.1%), and VE (5.1-9.1%), with closer replication of empirical probability distribution function (PDF) curves during the validation period. In the future, Borneo's climate zones will shift notably, with WC elongating southward along the mountainous spine, W forming an enclave over the north-central mountains, WH shifting northward and shrinking inland, and DH expanding northward along the western coast. Under SSP5-8.5, WC is expected to expand by 39% and 11% for the mid- and far-future periods, respectively, while W is set to shrink by 46%. WH is projected to expand by 2% and 8% for the mid- and far-future periods, respectively. Conversely, DH is expected to expand by 43% for the far-future period but shrink by 42% for the mid-future period. This study fills a gap by redefining Borneo's climate zones based on an increased number of effective parameters and projecting future shifts, utilizing advanced clustering methods (FCM) under CMIP6 scenarios. Importantly, it contributes by ranking GCMs using RIMs and CPI across multiple climate parameters, addressing a previous gap in GCM assessment. The study's findings can facilitate cross-border collaboration by providing a shared understanding of climate dynamics and informing joint environmental management and disaster response efforts.

Heavy metals prediction in coastal marine sediments using hybridized machine learning models with metaheuristic optimization algorithm.

This study proposes different standalone models viz: Elman neural network (ENN), Boosted Tree algorithm (BTA), and f relevance vector machine (RVM) for modeling arsenic (As (mg/kg)) and zinc (Zn (mg/kg)) in marine sediments owing to anthropogenic activities. A heuristic algorithm based on the potential of RVM and a flower pollination algorithm (RVM-FPA) was developed to improve the prediction performance. Several evaluation indicators and graphical methods coupled with visualized cumulative probability function (CDF) were used to evaluate the accuracy of the models. Akaike (AIC) and Schwarz (SCI) information criteria based on Dickey-Fuller (ADF) and Philip Perron (PP) tests were introduced to check the reliability and stationarity of the data. The prediction performance in the verification phase indicated that RVM-M2 (PBAIS = -o.0465, MAE = 0.0335) and ENN-M2 (PBAIS = 0.0043, MAE = 0.0322) emerged as the best model for As (mg/kg) and Zn (mg/kg), respectively. In contrast with the standalone approaches, the simulated hybrid RVM-FPA proved merit and the most reliable, with a 5 % and 18 % predictive increase for As (mg/kg) and Zn (mg/kg), respectively. The study's findings validated the potential for estimating complex HMs through intelligent data-driven models and heuristic optimization. The study also generated valuable insights that can inform the decision-makers and stockholders for environmental management strategies.

Short-term streamflow modeling using data-intelligence evolutionary machine learning models.

Accurate streamflow prediction is essential for efficient water resources management. Machine learning (ML) models are the tools to meet this need. This paper presents a comparative research study focusing on hybridizing ML models with bioinspired optimization algorithms (BOA) for short-term multistep streamflow forecasting. Specifically, we focus on applying XGB, MARS, ELM, EN, and SVR models and various BOA, including PSO, GA, and DE, for selecting model parameters. The performances of the resulting hybrid models are compared using performance statistics, graphical analysis, and hypothesis testing. The results show that the hybridization of BOA with ML models demonstrates significant potential as a data-driven approach for short-term multistep streamflow forecasting. The PSO algorithm proved superior to the DE and GA algorithms in determining the optimal hyperparameters of ML models for each step of the considered time horizon. When applied with all BOA, the XGB model outperformed the others (SVR, MARS, ELM, and EN), best predicting the different steps ahead. XGB integrated with PSO emerged as the superior model, according to the considered performance measures and the results of the statistical tests. The proposed XGB hybrid model is a superior alternative to the current daily flow forecast, crucial for water resources planning and management.

Machine learning-based country-level annual air pollutants exploration using Sentinel-5P and Google Earth Engine.

Climatic condition is triggering human health emergencies and earth's surface changes. Anthropogenic activities, such as built-up expansion, transportation development, industrial works, and some extreme phases, are the main reason for climate change and global warming. Air pollutants are increased gradually due to anthropogenic activities and triggering the earth's health. Nitrogen Dioxide (NO2), Carbon Monoxide (CO), and Aerosol Optical Depth (AOD) are truthfully important for air quality measurement because those air pollutants are more harmful to the environment and human's health. Earth observational Sentinel-5P is applied for monitoring the air pollutant and chemical conditions in the atmosphere from 2018 to 2021. The cloud computing-based Google Earth Engine (GEE) platform is applied for monitoring those air pollutants and chemical components in the atmosphere. The NO2 variation indicates high during the time because of the anthropogenic activities. Carbon Monoxide (CO) is also located high between two 1-month different maps. The 2020 and 2021 results indicate AQI change is high where 2018 and 2019 indicates low AQI throughout the year. The Kolkata have seven AQI monitoring station where high nitrogen dioxide recorded 102 (2018), 48 (2019), 26 (2020) and 98 (2021), where Delhi AQI stations recorded 99 (2018), 49 (2019), 37 (2020), and 107 (2021). Delhi, Kolkata, Mumbai, Pune, and Chennai recorded huge fluctuations of air pollutants during the study periods, where ~ 50-60% NO2 was recorded as high in the recent time. The AOD was noticed high in Uttar Pradesh in 2020. These results indicate that air pollutant investigation is much necessary for future planning and management otherwise; our planet earth is mostly affected by the anthropogenic and climatic conditions where maybe life does not exist.

Megacities' environmental assessment for Iraq region using satellite image and geo-spatial tools.

Urban areas are quickly established, and the overwhelming population pressure is triggering heat stress in the metropolitan cities. Climate change impact is the key aspect for maintaining the urban areas and building proper urban planning because spreading of the urban area destroyed the vegetated land and increased heat variation. Remote sensing-based on Landsat images are used for investigating the vegetation circumstances, thermal variation, urban expansion, and surface urban heat island or SUHI in the three megacities of Iraq like Baghdad, Erbil, and Basrah. Four satellite imageries are used aimed at land use and land cover (LULC) study from 1990 to 2020, which indicate the land transformation of those three major cities in Iraq. The average annually temperature is increased during  30 years like Baghdad (0.16 °C), Basrah (0.44 °C), and Erbil (0.32 °C). The built-up area is increased 147.1 km2 (Erbil), 217.86 km2 (Baghdad), and 294.43 km2 (Erbil), which indicated the SUHI affects the entire area of the three cities. The bare land is increased in Baghdad city, which indicated the local climatic condition and affected the livelihood. Basrah City is affected by anthropogenic activities and most areas of Basrah were converted into built-up land in the last 30 years. In Erbil, agricultural land (295.81 km2) is increased. The SUHI study results indicated the climate change effect in those three cities in Iraq. This study's results are more useful for planning, management, and sustainable development of urban areas.

Cyanobacteria blue-green algae prediction enhancement using hybrid machine learning-based gamma test variable selection and empirical wavelet transform.

This study aims to evaluate the usefulness and effectiveness of four machine learning (ML) models for modelling cyanobacteria blue-green algae (CBGA) at two rivers located in the USA. The proposed modelling framework was based on establishing a link between five water quality variables and the concentration of CBGA. For this purpose, artificial neural network (ANN), extreme learning machine (ELM), random forest regression (RFR), and random vector functional link (RVFL) are developed. First, the four models were developed using only water quality variables. Second, based on the results of the first, a new modelling strategy was introduced based on preprocessing signal decomposition. Hence, the empirical mode decomposition (EMD), the variational mode decomposition (VMD), and the empirical wavelet transform (EWT) were used for decomposing the water quality variables into several subcomponents, and the obtained intrinsic mode functions (IMFs) and multiresolution analysis (MRA) components were used as new input variables for the ML models. Results of the present investigation show that (i) using single models, good predictive accuracy was obtained using the RFR model exhibiting an R and NSE values of ≈0.914 and ≈0.833 for the first station, and ≈0.944 and ≈0.884 for the second station, while the others models, i.e., ANN, RVFL, and ELM, have failed to provide a good estimation of the CBGA; (ii) the decomposition methods have contributed to a significant improvement of the individual models performances; (iii) among the thee decomposition methods, the EMD was found to be superior to the VMD and EWT; and (iv) the ANN and RFR were found to be more accurate compared to the ELM and RVFL models, exhibiting high numerical performances with R and NSE values of approximately ≈0.983, ≈0.967, and ≈0.989 and ≈0.976, respectively.